From 940b2b4e99152896a09d9594de20eb773f90a344 Mon Sep 17 00:00:00 2001 From: jung-geun Date: Mon, 4 Sep 2023 14:45:45 +0900 Subject: [PATCH] =?UTF-8?q?23-09-04=20plt.ipynb=20=ED=8C=8C=EC=9D=BC=20?= =?UTF-8?q?=EC=A0=9C=EA=B1=B0=20tensorflow=20=EB=B2=84=EC=A0=84=20?= =?UTF-8?q?=EC=9A=94=EA=B1=B4=20=EB=B3=80=EA=B2=BD=202.11.1=20=EA=B3=A0?= =?UTF-8?q?=EC=A0=95=20-=20>=202.11.1=20=EC=9D=B4=ED=95=98?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .github/workflows/pypi.yml | 21 +- .gitignore | 3 +- mnist.ipynb | 254 - plt.ipynb | 15713 ----------------------------------- pso/__init__.py | 2 +- requirements.txt | 2 +- setup.py | 2 + 7 files changed, 22 insertions(+), 15975 deletions(-) delete mode 100644 mnist.ipynb delete mode 100644 plt.ipynb diff --git a/.github/workflows/pypi.yml b/.github/workflows/pypi.yml index 2bff718..9d2c6ff 100644 --- a/.github/workflows/pypi.yml +++ b/.github/workflows/pypi.yml @@ -1,19 +1,30 @@ name: PyPI package -on: [push] +on: + push: + paths: + - "setup.py" + branches: + - main + +permissions: + contents: read + packages: write jobs: build-linux: runs-on: ubuntu-22.04 strategy: max-parallel: 5 + matrix: + python-version: [3.8, 3.9, 3.10, 3.11] steps: - - uses: actions/checkout@v2 - - name: Set up Python 3.9 - uses: actions/setup-python@v2 + - uses: actions/checkout@v3 + - name: Set up Python + uses: actions/setup-python@v3 with: - python-version: 3.9 + python-version: ${{ matrix.python-version }} - name: Install dependencies run: | python -m pip install --upgrade pip diff --git a/.gitignore b/.gitignore index bcc6ebe..4c3cffd 100644 --- a/.gitignore +++ b/.gitignore @@ -13,6 +13,7 @@ pso2keras.egg-info/ # 테스트용 파일 test.ipynb test.py +log2plt.ipynb # 결과 저장용 디렉토리 result/ @@ -22,4 +23,4 @@ logs/ *.pdf *.pptx 관련 논문/ -발표 자료/ \ No newline at end of file +발표 자료/ diff --git a/mnist.ipynb b/mnist.ipynb deleted file mode 100644 index 69b6012..0000000 --- a/mnist.ipynb +++ /dev/null @@ -1,254 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import sys\n", - "\n", - "os.environ[\"TF_CPP_MIN_LOG_LEVEL\"] = \"2\"\n", - "\n", - "import gc\n", - "\n", - "import tensorflow as tf\n", - "from keras.datasets import mnist\n", - "from keras.layers import Conv2D, Dense, Dropout, Flatten, MaxPooling2D\n", - "from keras.models import Sequential\n", - "from tensorflow import keras\n", - "\n", - "from pso import optimizer\n", - "\n", - "\n", - "def get_data():\n", - " (x_train, y_train), (x_test, y_test) = mnist.load_data()\n", - "\n", - " x_train, x_test = x_train / 255.0, x_test / 255.0\n", - " x_train = x_train.reshape((60000, 28, 28, 1))\n", - " x_test = x_test.reshape((10000, 28, 28, 1))\n", - "\n", - " y_train, y_test = tf.one_hot(y_train, 10), tf.one_hot(y_test, 10)\n", - "\n", - " print(f\"x_train : {x_train[0].shape} | y_train : {y_train[0].shape}\")\n", - " print(f\"x_test : {x_test[0].shape} | y_test : {y_test[0].shape}\")\n", - "\n", - " return x_train, y_train, x_test, y_test\n", - "\n", - "\n", - "def get_data_test():\n", - " (x_train, y_train), (x_test, y_test) = mnist.load_data()\n", - " x_test = x_test / 255.0\n", - " x_test = x_test.reshape((10000, 28, 28, 1))\n", - "\n", - " y_test = tf.one_hot(y_test, 10)\n", - "\n", - " print(f\"x_test : {x_test[0].shape} | y_test : {y_test[0].shape}\")\n", - "\n", - " return x_test, y_test\n", - "\n", - "\n", - "def make_model():\n", - " model = Sequential()\n", - " model.add(\n", - " Conv2D(32, kernel_size=(5, 5), activation=\"relu\", input_shape=(28, 28, 1))\n", - " )\n", - " model.add(MaxPooling2D(pool_size=(3, 3)))\n", - " model.add(Conv2D(64, kernel_size=(3, 3), activation=\"relu\"))\n", - " model.add(MaxPooling2D(pool_size=(2, 2)))\n", - " model.add(Dropout(0.25))\n", - " model.add(Flatten())\n", - " model.add(Dense(128, activation=\"relu\"))\n", - " model.add(Dense(10, activation=\"softmax\"))\n", - "\n", - " return model" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "x_test : (28, 28, 1) | y_test : (10,)\n", - "start running time : 20230716-194018\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "04956808700d412f93bfed35ab8f83f8", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Initializing Particles: 0%| | 0/70 [00:00 55\u001b[0m pso_mnist \u001b[39m=\u001b[39m Optimizer(\n\u001b[1;32m 56\u001b[0m model,\n\u001b[1;32m 57\u001b[0m loss\u001b[39m=\u001b[39;49mloss[\u001b[39m0\u001b[39;49m],\n\u001b[1;32m 58\u001b[0m n_particles\u001b[39m=\u001b[39;49m\u001b[39m70\u001b[39;49m,\n\u001b[1;32m 59\u001b[0m c0\u001b[39m=\u001b[39;49m\u001b[39m0.3\u001b[39;49m,\n\u001b[1;32m 60\u001b[0m c1\u001b[39m=\u001b[39;49m\u001b[39m0.5\u001b[39;49m,\n\u001b[1;32m 61\u001b[0m w_min\u001b[39m=\u001b[39;49m\u001b[39m0.4\u001b[39;49m,\n\u001b[1;32m 62\u001b[0m w_max\u001b[39m=\u001b[39;49m\u001b[39m0.7\u001b[39;49m,\n\u001b[1;32m 63\u001b[0m negative_swarm\u001b[39m=\u001b[39;49m\u001b[39m0.1\u001b[39;49m,\n\u001b[1;32m 64\u001b[0m mutation_swarm\u001b[39m=\u001b[39;49m\u001b[39m0.2\u001b[39;49m,\n\u001b[1;32m 65\u001b[0m particle_min\u001b[39m=\u001b[39;49m\u001b[39m-\u001b[39;49m\u001b[39m5\u001b[39;49m,\n\u001b[1;32m 66\u001b[0m particle_max\u001b[39m=\u001b[39;49m\u001b[39m5\u001b[39;49m,\n\u001b[1;32m 67\u001b[0m )\n\u001b[1;32m 69\u001b[0m best_score \u001b[39m=\u001b[39m pso_mnist\u001b[39m.\u001b[39mfit(\n\u001b[1;32m 70\u001b[0m x_train,\n\u001b[1;32m 71\u001b[0m y_train,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 77\u001b[0m check_point\u001b[39m=\u001b[39m\u001b[39m25\u001b[39m,\n\u001b[1;32m 78\u001b[0m )\n\u001b[1;32m 80\u001b[0m \u001b[39mprint\u001b[39m(\u001b[39m\"\u001b[39m\u001b[39mDone!\u001b[39m\u001b[39m\"\u001b[39m)\n", - "File \u001b[0;32m/drive/samba/private_files/jupyter/PSO/pso/optimizer.py:94\u001b[0m, in \u001b[0;36mOptimizer.__init__\u001b[0;34m(self, model, loss, n_particles, c0, c1, w_min, w_max, negative_swarm, mutation_swarm, np_seed, tf_seed, particle_min, particle_max)\u001b[0m\n\u001b[1;32m 92\u001b[0m \u001b[39mprint\u001b[39m(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mstart running time : \u001b[39m\u001b[39m{\u001b[39;00m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mday\u001b[39m}\u001b[39;00m\u001b[39m\"\u001b[39m)\n\u001b[1;32m 93\u001b[0m \u001b[39mfor\u001b[39;00m i \u001b[39min\u001b[39;00m tqdm(\u001b[39mrange\u001b[39m(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mn_particles), desc\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mInitializing Particles\u001b[39m\u001b[39m\"\u001b[39m):\n\u001b[0;32m---> 94\u001b[0m m \u001b[39m=\u001b[39m keras\u001b[39m.\u001b[39;49mmodels\u001b[39m.\u001b[39;49mmodel_from_json(model\u001b[39m.\u001b[39;49mto_json())\n\u001b[1;32m 95\u001b[0m init_weights \u001b[39m=\u001b[39m m\u001b[39m.\u001b[39mget_weights()\n\u001b[1;32m 97\u001b[0m w_, sh_, len_ \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_encode(init_weights)\n", - "File \u001b[0;32m~/miniconda3/envs/pso/lib/python3.9/site-packages/keras/saving/legacy/model_config.py:109\u001b[0m, in \u001b[0;36mmodel_from_json\u001b[0;34m(json_string, custom_objects)\u001b[0m\n\u001b[1;32m 86\u001b[0m \u001b[39m\u001b[39m\u001b[39m\"\"\"Parses a JSON model configuration string and returns a model instance.\u001b[39;00m\n\u001b[1;32m 87\u001b[0m \n\u001b[1;32m 88\u001b[0m \u001b[39mUsage:\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 103\u001b[0m \u001b[39m A Keras model instance (uncompiled).\u001b[39;00m\n\u001b[1;32m 104\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m 105\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mkeras\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mlayers\u001b[39;00m \u001b[39mimport\u001b[39;00m (\n\u001b[1;32m 106\u001b[0m deserialize_from_json,\n\u001b[1;32m 107\u001b[0m )\n\u001b[0;32m--> 109\u001b[0m \u001b[39mreturn\u001b[39;00m deserialize_from_json(json_string, custom_objects\u001b[39m=\u001b[39;49mcustom_objects)\n", - "File \u001b[0;32m~/miniconda3/envs/pso/lib/python3.9/site-packages/keras/layers/serialization.py:275\u001b[0m, in \u001b[0;36mdeserialize_from_json\u001b[0;34m(json_string, custom_objects)\u001b[0m\n\u001b[1;32m 269\u001b[0m populate_deserializable_objects()\n\u001b[1;32m 270\u001b[0m config \u001b[39m=\u001b[39m json_utils\u001b[39m.\u001b[39mdecode_and_deserialize(\n\u001b[1;32m 271\u001b[0m json_string,\n\u001b[1;32m 272\u001b[0m module_objects\u001b[39m=\u001b[39mLOCAL\u001b[39m.\u001b[39mALL_OBJECTS,\n\u001b[1;32m 273\u001b[0m custom_objects\u001b[39m=\u001b[39mcustom_objects,\n\u001b[1;32m 274\u001b[0m )\n\u001b[0;32m--> 275\u001b[0m \u001b[39mreturn\u001b[39;00m deserialize(config, custom_objects)\n", - "File \u001b[0;32m~/miniconda3/envs/pso/lib/python3.9/site-packages/keras/layers/serialization.py:252\u001b[0m, in \u001b[0;36mdeserialize\u001b[0;34m(config, custom_objects)\u001b[0m\n\u001b[1;32m 215\u001b[0m \u001b[39m\u001b[39m\u001b[39m\"\"\"Instantiates a layer from a config dictionary.\u001b[39;00m\n\u001b[1;32m 216\u001b[0m \n\u001b[1;32m 217\u001b[0m \u001b[39mArgs:\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 249\u001b[0m \u001b[39m```\u001b[39;00m\n\u001b[1;32m 250\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m 251\u001b[0m populate_deserializable_objects()\n\u001b[0;32m--> 252\u001b[0m \u001b[39mreturn\u001b[39;00m serialization\u001b[39m.\u001b[39;49mdeserialize_keras_object(\n\u001b[1;32m 253\u001b[0m config,\n\u001b[1;32m 254\u001b[0m module_objects\u001b[39m=\u001b[39;49mLOCAL\u001b[39m.\u001b[39;49mALL_OBJECTS,\n\u001b[1;32m 255\u001b[0m custom_objects\u001b[39m=\u001b[39;49mcustom_objects,\n\u001b[1;32m 256\u001b[0m printable_module_name\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39mlayer\u001b[39;49m\u001b[39m\"\u001b[39;49m,\n\u001b[1;32m 257\u001b[0m )\n", - "File \u001b[0;32m~/miniconda3/envs/pso/lib/python3.9/site-packages/keras/saving/legacy/serialization.py:517\u001b[0m, in \u001b[0;36mdeserialize_keras_object\u001b[0;34m(identifier, module_objects, custom_objects, printable_module_name)\u001b[0m\n\u001b[1;32m 515\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39m\"\u001b[39m\u001b[39mcustom_objects\u001b[39m\u001b[39m\"\u001b[39m \u001b[39min\u001b[39;00m arg_spec\u001b[39m.\u001b[39margs:\n\u001b[1;32m 516\u001b[0m tlco \u001b[39m=\u001b[39m object_registration\u001b[39m.\u001b[39m_THREAD_LOCAL_CUSTOM_OBJECTS\u001b[39m.\u001b[39m\u001b[39m__dict__\u001b[39m\n\u001b[0;32m--> 517\u001b[0m deserialized_obj \u001b[39m=\u001b[39m \u001b[39mcls\u001b[39;49m\u001b[39m.\u001b[39;49mfrom_config(\n\u001b[1;32m 518\u001b[0m cls_config,\n\u001b[1;32m 519\u001b[0m custom_objects\u001b[39m=\u001b[39;49m{\n\u001b[1;32m 520\u001b[0m \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mobject_registration\u001b[39m.\u001b[39;49m_GLOBAL_CUSTOM_OBJECTS,\n\u001b[1;32m 521\u001b[0m \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mtlco,\n\u001b[1;32m 522\u001b[0m \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mcustom_objects,\n\u001b[1;32m 523\u001b[0m },\n\u001b[1;32m 524\u001b[0m )\n\u001b[1;32m 525\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m 526\u001b[0m \u001b[39mwith\u001b[39;00m object_registration\u001b[39m.\u001b[39mCustomObjectScope(custom_objects):\n", - "File \u001b[0;32m~/miniconda3/envs/pso/lib/python3.9/site-packages/keras/engine/sequential.py:481\u001b[0m, in \u001b[0;36mSequential.from_config\u001b[0;34m(cls, config, custom_objects)\u001b[0m\n\u001b[1;32m 477\u001b[0m \u001b[39mfor\u001b[39;00m layer_config \u001b[39min\u001b[39;00m layer_configs:\n\u001b[1;32m 478\u001b[0m layer \u001b[39m=\u001b[39m layer_module\u001b[39m.\u001b[39mdeserialize(\n\u001b[1;32m 479\u001b[0m layer_config, custom_objects\u001b[39m=\u001b[39mcustom_objects\n\u001b[1;32m 480\u001b[0m )\n\u001b[0;32m--> 481\u001b[0m model\u001b[39m.\u001b[39;49madd(layer)\n\u001b[1;32m 483\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mgetattr\u001b[39m(saving_lib\u001b[39m.\u001b[39m_SAVING_V3_ENABLED, \u001b[39m\"\u001b[39m\u001b[39mvalue\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39mFalse\u001b[39;00m):\n\u001b[1;32m 484\u001b[0m compile_config \u001b[39m=\u001b[39m config\u001b[39m.\u001b[39mget(\u001b[39m\"\u001b[39m\u001b[39mcompile_config\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39mNone\u001b[39;00m)\n", - "File \u001b[0;32m~/miniconda3/envs/pso/lib/python3.9/site-packages/tensorflow/python/trackable/base.py:205\u001b[0m, in \u001b[0;36mno_automatic_dependency_tracking.._method_wrapper\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 203\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_self_setattr_tracking \u001b[39m=\u001b[39m \u001b[39mFalse\u001b[39;00m \u001b[39m# pylint: disable=protected-access\u001b[39;00m\n\u001b[1;32m 204\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 205\u001b[0m result \u001b[39m=\u001b[39m method(\u001b[39mself\u001b[39;49m, \u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m 206\u001b[0m \u001b[39mfinally\u001b[39;00m:\n\u001b[1;32m 207\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_self_setattr_tracking \u001b[39m=\u001b[39m previous_value \u001b[39m# pylint: disable=protected-access\u001b[39;00m\n", - "File \u001b[0;32m~/miniconda3/envs/pso/lib/python3.9/site-packages/keras/utils/traceback_utils.py:65\u001b[0m, in \u001b[0;36mfilter_traceback..error_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 63\u001b[0m filtered_tb \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m\n\u001b[1;32m 64\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m---> 65\u001b[0m \u001b[39mreturn\u001b[39;00m fn(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m 66\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mException\u001b[39;00m \u001b[39mas\u001b[39;00m e:\n\u001b[1;32m 67\u001b[0m filtered_tb \u001b[39m=\u001b[39m _process_traceback_frames(e\u001b[39m.\u001b[39m__traceback__)\n", - "File \u001b[0;32m~/miniconda3/envs/pso/lib/python3.9/site-packages/keras/engine/sequential.py:237\u001b[0m, in \u001b[0;36mSequential.add\u001b[0;34m(self, layer)\u001b[0m\n\u001b[1;32m 232\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_has_explicit_input_shape \u001b[39m=\u001b[39m \u001b[39mTrue\u001b[39;00m\n\u001b[1;32m 234\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39moutputs:\n\u001b[1;32m 235\u001b[0m \u001b[39m# If the model is being built continuously on top of an input layer:\u001b[39;00m\n\u001b[1;32m 236\u001b[0m \u001b[39m# refresh its output.\u001b[39;00m\n\u001b[0;32m--> 237\u001b[0m output_tensor \u001b[39m=\u001b[39m layer(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49moutputs[\u001b[39m0\u001b[39;49m])\n\u001b[1;32m 238\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mlen\u001b[39m(tf\u001b[39m.\u001b[39mnest\u001b[39m.\u001b[39mflatten(output_tensor)) \u001b[39m!=\u001b[39m \u001b[39m1\u001b[39m:\n\u001b[1;32m 239\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(SINGLE_LAYER_OUTPUT_ERROR_MSG)\n", - "File \u001b[0;32m~/miniconda3/envs/pso/lib/python3.9/site-packages/keras/utils/traceback_utils.py:65\u001b[0m, in \u001b[0;36mfilter_traceback..error_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 63\u001b[0m filtered_tb \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m\n\u001b[1;32m 64\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m---> 65\u001b[0m \u001b[39mreturn\u001b[39;00m fn(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m 66\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mException\u001b[39;00m \u001b[39mas\u001b[39;00m e:\n\u001b[1;32m 67\u001b[0m filtered_tb \u001b[39m=\u001b[39m _process_traceback_frames(e\u001b[39m.\u001b[39m__traceback__)\n", - "File \u001b[0;32m~/miniconda3/envs/pso/lib/python3.9/site-packages/keras/engine/base_layer.py:1045\u001b[0m, in \u001b[0;36mLayer.__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1037\u001b[0m \u001b[39m# Functional Model construction mode is invoked when `Layer`s are called\u001b[39;00m\n\u001b[1;32m 1038\u001b[0m \u001b[39m# on symbolic `KerasTensor`s, i.e.:\u001b[39;00m\n\u001b[1;32m 1039\u001b[0m \u001b[39m# >> inputs = tf.keras.Input(10)\u001b[39;00m\n\u001b[1;32m 1040\u001b[0m \u001b[39m# >> outputs = MyLayer()(inputs) # Functional construction mode.\u001b[39;00m\n\u001b[1;32m 1041\u001b[0m \u001b[39m# >> model = tf.keras.Model(inputs, outputs)\u001b[39;00m\n\u001b[1;32m 1042\u001b[0m \u001b[39mif\u001b[39;00m _in_functional_construction_mode(\n\u001b[1;32m 1043\u001b[0m \u001b[39mself\u001b[39m, inputs, args, kwargs, input_list\n\u001b[1;32m 1044\u001b[0m ):\n\u001b[0;32m-> 1045\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_functional_construction_call(\n\u001b[1;32m 1046\u001b[0m inputs, args, kwargs, input_list\n\u001b[1;32m 1047\u001b[0m )\n\u001b[1;32m 1049\u001b[0m \u001b[39m# Maintains info about the `Layer.call` stack.\u001b[39;00m\n\u001b[1;32m 1050\u001b[0m call_context \u001b[39m=\u001b[39m base_layer_utils\u001b[39m.\u001b[39mcall_context()\n", - "File \u001b[0;32m~/miniconda3/envs/pso/lib/python3.9/site-packages/keras/engine/base_layer.py:2535\u001b[0m, in \u001b[0;36mLayer._functional_construction_call\u001b[0;34m(self, inputs, args, kwargs, input_list)\u001b[0m\n\u001b[1;32m 2528\u001b[0m training_arg_passed_by_framework \u001b[39m=\u001b[39m \u001b[39mTrue\u001b[39;00m\n\u001b[1;32m 2530\u001b[0m \u001b[39mwith\u001b[39;00m call_context\u001b[39m.\u001b[39menter(\n\u001b[1;32m 2531\u001b[0m layer\u001b[39m=\u001b[39m\u001b[39mself\u001b[39m, inputs\u001b[39m=\u001b[39minputs, build_graph\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m, training\u001b[39m=\u001b[39mtraining_value\n\u001b[1;32m 2532\u001b[0m ):\n\u001b[1;32m 2533\u001b[0m \u001b[39m# Check input assumptions set after layer building, e.g. input\u001b[39;00m\n\u001b[1;32m 2534\u001b[0m \u001b[39m# shape.\u001b[39;00m\n\u001b[0;32m-> 2535\u001b[0m outputs \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_keras_tensor_symbolic_call(\n\u001b[1;32m 2536\u001b[0m inputs, input_masks, args, kwargs\n\u001b[1;32m 2537\u001b[0m )\n\u001b[1;32m 2539\u001b[0m \u001b[39mif\u001b[39;00m outputs \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m 2540\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[1;32m 2541\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mA layer\u001b[39m\u001b[39m'\u001b[39m\u001b[39ms `call` method should return a \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m 2542\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mTensor or a list of Tensors, not None \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m 2543\u001b[0m \u001b[39m\"\u001b[39m\u001b[39m(layer: \u001b[39m\u001b[39m\"\u001b[39m \u001b[39m+\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mname \u001b[39m+\u001b[39m \u001b[39m\"\u001b[39m\u001b[39m).\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m 2544\u001b[0m )\n", - "File \u001b[0;32m~/miniconda3/envs/pso/lib/python3.9/site-packages/keras/engine/base_layer.py:2382\u001b[0m, in \u001b[0;36mLayer._keras_tensor_symbolic_call\u001b[0;34m(self, inputs, input_masks, args, kwargs)\u001b[0m\n\u001b[1;32m 2378\u001b[0m \u001b[39mreturn\u001b[39;00m tf\u001b[39m.\u001b[39mnest\u001b[39m.\u001b[39mmap_structure(\n\u001b[1;32m 2379\u001b[0m keras_tensor\u001b[39m.\u001b[39mKerasTensor, output_signature\n\u001b[1;32m 2380\u001b[0m )\n\u001b[1;32m 2381\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m-> 2382\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_infer_output_signature(\n\u001b[1;32m 2383\u001b[0m inputs, args, kwargs, input_masks\n\u001b[1;32m 2384\u001b[0m )\n", - "File \u001b[0;32m~/miniconda3/envs/pso/lib/python3.9/site-packages/keras/engine/base_layer.py:2418\u001b[0m, in \u001b[0;36mLayer._infer_output_signature\u001b[0;34m(self, inputs, args, kwargs, input_masks)\u001b[0m\n\u001b[1;32m 2414\u001b[0m scratch_graph \u001b[39m=\u001b[39m tf\u001b[39m.\u001b[39m__internal__\u001b[39m.\u001b[39mFuncGraph(\n\u001b[1;32m 2415\u001b[0m \u001b[39mstr\u001b[39m(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mname) \u001b[39m+\u001b[39m \u001b[39m\"\u001b[39m\u001b[39m_scratch_graph\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m 2416\u001b[0m )\n\u001b[1;32m 2417\u001b[0m \u001b[39mwith\u001b[39;00m scratch_graph\u001b[39m.\u001b[39mas_default():\n\u001b[0;32m-> 2418\u001b[0m inputs \u001b[39m=\u001b[39m tf\u001b[39m.\u001b[39;49mnest\u001b[39m.\u001b[39;49mmap_structure(\n\u001b[1;32m 2419\u001b[0m keras_tensor\u001b[39m.\u001b[39;49mkeras_tensor_to_placeholder, inputs\n\u001b[1;32m 2420\u001b[0m )\n\u001b[1;32m 2421\u001b[0m args \u001b[39m=\u001b[39m tf\u001b[39m.\u001b[39mnest\u001b[39m.\u001b[39mmap_structure(\n\u001b[1;32m 2422\u001b[0m keras_tensor\u001b[39m.\u001b[39mkeras_tensor_to_placeholder, args\n\u001b[1;32m 2423\u001b[0m )\n\u001b[1;32m 2424\u001b[0m kwargs \u001b[39m=\u001b[39m tf\u001b[39m.\u001b[39mnest\u001b[39m.\u001b[39mmap_structure(\n\u001b[1;32m 2425\u001b[0m keras_tensor\u001b[39m.\u001b[39mkeras_tensor_to_placeholder, kwargs\n\u001b[1;32m 2426\u001b[0m )\n", - "File \u001b[0;32m~/miniconda3/envs/pso/lib/python3.9/site-packages/tensorflow/python/util/nest.py:917\u001b[0m, in \u001b[0;36mmap_structure\u001b[0;34m(func, *structure, **kwargs)\u001b[0m\n\u001b[1;32m 913\u001b[0m flat_structure \u001b[39m=\u001b[39m (flatten(s, expand_composites) \u001b[39mfor\u001b[39;00m s \u001b[39min\u001b[39;00m structure)\n\u001b[1;32m 914\u001b[0m entries \u001b[39m=\u001b[39m \u001b[39mzip\u001b[39m(\u001b[39m*\u001b[39mflat_structure)\n\u001b[1;32m 916\u001b[0m \u001b[39mreturn\u001b[39;00m pack_sequence_as(\n\u001b[0;32m--> 917\u001b[0m structure[\u001b[39m0\u001b[39m], [func(\u001b[39m*\u001b[39mx) \u001b[39mfor\u001b[39;00m x \u001b[39min\u001b[39;00m entries],\n\u001b[1;32m 918\u001b[0m expand_composites\u001b[39m=\u001b[39mexpand_composites)\n", - "File \u001b[0;32m~/miniconda3/envs/pso/lib/python3.9/site-packages/tensorflow/python/util/nest.py:917\u001b[0m, in \u001b[0;36m\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 913\u001b[0m flat_structure \u001b[39m=\u001b[39m (flatten(s, expand_composites) \u001b[39mfor\u001b[39;00m s \u001b[39min\u001b[39;00m structure)\n\u001b[1;32m 914\u001b[0m entries \u001b[39m=\u001b[39m \u001b[39mzip\u001b[39m(\u001b[39m*\u001b[39mflat_structure)\n\u001b[1;32m 916\u001b[0m \u001b[39mreturn\u001b[39;00m pack_sequence_as(\n\u001b[0;32m--> 917\u001b[0m structure[\u001b[39m0\u001b[39m], [func(\u001b[39m*\u001b[39;49mx) \u001b[39mfor\u001b[39;00m x \u001b[39min\u001b[39;00m entries],\n\u001b[1;32m 918\u001b[0m expand_composites\u001b[39m=\u001b[39mexpand_composites)\n", - "File \u001b[0;32m~/miniconda3/envs/pso/lib/python3.9/site-packages/keras/engine/keras_tensor.py:648\u001b[0m, in \u001b[0;36mkeras_tensor_to_placeholder\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m 646\u001b[0m \u001b[39m\u001b[39m\u001b[39m\"\"\"Construct a graph placeholder to represent a KerasTensor when tracing.\"\"\"\u001b[39;00m\n\u001b[1;32m 647\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(x, KerasTensor):\n\u001b[0;32m--> 648\u001b[0m \u001b[39mreturn\u001b[39;00m x\u001b[39m.\u001b[39;49m_to_placeholder()\n\u001b[1;32m 649\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m 650\u001b[0m \u001b[39mreturn\u001b[39;00m x\n", - "File \u001b[0;32m~/miniconda3/envs/pso/lib/python3.9/site-packages/keras/engine/keras_tensor.py:236\u001b[0m, in \u001b[0;36mKerasTensor._to_placeholder\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 233\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mcomponent_to_placeholder\u001b[39m(component):\n\u001b[1;32m 234\u001b[0m \u001b[39mreturn\u001b[39;00m tf\u001b[39m.\u001b[39mcompat\u001b[39m.\u001b[39mv1\u001b[39m.\u001b[39mplaceholder(component\u001b[39m.\u001b[39mdtype, component\u001b[39m.\u001b[39mshape)\n\u001b[0;32m--> 236\u001b[0m \u001b[39mreturn\u001b[39;00m tf\u001b[39m.\u001b[39;49mnest\u001b[39m.\u001b[39;49mmap_structure(\n\u001b[1;32m 237\u001b[0m component_to_placeholder, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mtype_spec, expand_composites\u001b[39m=\u001b[39;49m\u001b[39mTrue\u001b[39;49;00m\n\u001b[1;32m 238\u001b[0m )\n", - "File \u001b[0;32m~/miniconda3/envs/pso/lib/python3.9/site-packages/tensorflow/python/util/nest.py:917\u001b[0m, in \u001b[0;36mmap_structure\u001b[0;34m(func, *structure, **kwargs)\u001b[0m\n\u001b[1;32m 913\u001b[0m flat_structure \u001b[39m=\u001b[39m (flatten(s, expand_composites) \u001b[39mfor\u001b[39;00m s \u001b[39min\u001b[39;00m structure)\n\u001b[1;32m 914\u001b[0m entries \u001b[39m=\u001b[39m \u001b[39mzip\u001b[39m(\u001b[39m*\u001b[39mflat_structure)\n\u001b[1;32m 916\u001b[0m \u001b[39mreturn\u001b[39;00m pack_sequence_as(\n\u001b[0;32m--> 917\u001b[0m structure[\u001b[39m0\u001b[39m], [func(\u001b[39m*\u001b[39mx) \u001b[39mfor\u001b[39;00m x \u001b[39min\u001b[39;00m entries],\n\u001b[1;32m 918\u001b[0m expand_composites\u001b[39m=\u001b[39mexpand_composites)\n", - "File \u001b[0;32m~/miniconda3/envs/pso/lib/python3.9/site-packages/tensorflow/python/util/nest.py:917\u001b[0m, in \u001b[0;36m\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 913\u001b[0m flat_structure \u001b[39m=\u001b[39m (flatten(s, expand_composites) \u001b[39mfor\u001b[39;00m s \u001b[39min\u001b[39;00m structure)\n\u001b[1;32m 914\u001b[0m entries \u001b[39m=\u001b[39m \u001b[39mzip\u001b[39m(\u001b[39m*\u001b[39mflat_structure)\n\u001b[1;32m 916\u001b[0m \u001b[39mreturn\u001b[39;00m pack_sequence_as(\n\u001b[0;32m--> 917\u001b[0m structure[\u001b[39m0\u001b[39m], [func(\u001b[39m*\u001b[39;49mx) \u001b[39mfor\u001b[39;00m x \u001b[39min\u001b[39;00m entries],\n\u001b[1;32m 918\u001b[0m expand_composites\u001b[39m=\u001b[39mexpand_composites)\n", - "File \u001b[0;32m~/miniconda3/envs/pso/lib/python3.9/site-packages/keras/engine/keras_tensor.py:234\u001b[0m, in \u001b[0;36mKerasTensor._to_placeholder..component_to_placeholder\u001b[0;34m(component)\u001b[0m\n\u001b[1;32m 233\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mcomponent_to_placeholder\u001b[39m(component):\n\u001b[0;32m--> 234\u001b[0m \u001b[39mreturn\u001b[39;00m tf\u001b[39m.\u001b[39;49mcompat\u001b[39m.\u001b[39;49mv1\u001b[39m.\u001b[39;49mplaceholder(component\u001b[39m.\u001b[39;49mdtype, component\u001b[39m.\u001b[39;49mshape)\n", - "File \u001b[0;32m~/miniconda3/envs/pso/lib/python3.9/site-packages/tensorflow/python/ops/array_ops.py:3343\u001b[0m, in \u001b[0;36mplaceholder\u001b[0;34m(dtype, shape, name)\u001b[0m\n\u001b[1;32m 3339\u001b[0m \u001b[39mif\u001b[39;00m context\u001b[39m.\u001b[39mexecuting_eagerly():\n\u001b[1;32m 3340\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mRuntimeError\u001b[39;00m(\u001b[39m\"\u001b[39m\u001b[39mtf.placeholder() is not compatible with \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m 3341\u001b[0m \u001b[39m\"\u001b[39m\u001b[39meager execution.\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[0;32m-> 3343\u001b[0m \u001b[39mreturn\u001b[39;00m gen_array_ops\u001b[39m.\u001b[39;49mplaceholder(dtype\u001b[39m=\u001b[39;49mdtype, shape\u001b[39m=\u001b[39;49mshape, name\u001b[39m=\u001b[39;49mname)\n", - "File \u001b[0;32m~/miniconda3/envs/pso/lib/python3.9/site-packages/tensorflow/python/ops/gen_array_ops.py:6898\u001b[0m, in \u001b[0;36mplaceholder\u001b[0;34m(dtype, shape, name)\u001b[0m\n\u001b[1;32m 6896\u001b[0m shape \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m\n\u001b[1;32m 6897\u001b[0m shape \u001b[39m=\u001b[39m _execute\u001b[39m.\u001b[39mmake_shape(shape, \u001b[39m\"\u001b[39m\u001b[39mshape\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[0;32m-> 6898\u001b[0m _, _, _op, _outputs \u001b[39m=\u001b[39m _op_def_library\u001b[39m.\u001b[39;49m_apply_op_helper(\n\u001b[1;32m 6899\u001b[0m \u001b[39m\"\u001b[39;49m\u001b[39mPlaceholder\u001b[39;49m\u001b[39m\"\u001b[39;49m, dtype\u001b[39m=\u001b[39;49mdtype, shape\u001b[39m=\u001b[39;49mshape, name\u001b[39m=\u001b[39;49mname)\n\u001b[1;32m 6900\u001b[0m _result \u001b[39m=\u001b[39m _outputs[:]\n\u001b[1;32m 6901\u001b[0m \u001b[39mif\u001b[39;00m _execute\u001b[39m.\u001b[39mmust_record_gradient():\n", - "File \u001b[0;32m~/miniconda3/envs/pso/lib/python3.9/site-packages/tensorflow/python/framework/op_def_library.py:795\u001b[0m, in \u001b[0;36m_apply_op_helper\u001b[0;34m(op_type_name, name, **keywords)\u001b[0m\n\u001b[1;32m 790\u001b[0m must_colocate_inputs \u001b[39m=\u001b[39m [val \u001b[39mfor\u001b[39;00m arg, val \u001b[39min\u001b[39;00m \u001b[39mzip\u001b[39m(op_def\u001b[39m.\u001b[39minput_arg, inputs)\n\u001b[1;32m 791\u001b[0m \u001b[39mif\u001b[39;00m arg\u001b[39m.\u001b[39mis_ref]\n\u001b[1;32m 792\u001b[0m \u001b[39mwith\u001b[39;00m _MaybeColocateWith(must_colocate_inputs):\n\u001b[1;32m 793\u001b[0m \u001b[39m# Add Op to graph\u001b[39;00m\n\u001b[1;32m 794\u001b[0m \u001b[39m# pylint: disable=protected-access\u001b[39;00m\n\u001b[0;32m--> 795\u001b[0m op \u001b[39m=\u001b[39m g\u001b[39m.\u001b[39;49m_create_op_internal(op_type_name, inputs, dtypes\u001b[39m=\u001b[39;49m\u001b[39mNone\u001b[39;49;00m,\n\u001b[1;32m 796\u001b[0m name\u001b[39m=\u001b[39;49mscope, input_types\u001b[39m=\u001b[39;49minput_types,\n\u001b[1;32m 797\u001b[0m attrs\u001b[39m=\u001b[39;49mattr_protos, op_def\u001b[39m=\u001b[39;49mop_def)\n\u001b[1;32m 799\u001b[0m \u001b[39m# `outputs` is returned as a separate return value so that the output\u001b[39;00m\n\u001b[1;32m 800\u001b[0m \u001b[39m# tensors can the `op` per se can be decoupled so that the\u001b[39;00m\n\u001b[1;32m 801\u001b[0m \u001b[39m# `op_callbacks` can function properly. See framework/op_callbacks.py\u001b[39;00m\n\u001b[1;32m 802\u001b[0m \u001b[39m# for more details.\u001b[39;00m\n\u001b[1;32m 803\u001b[0m outputs \u001b[39m=\u001b[39m op\u001b[39m.\u001b[39moutputs\n", - "File \u001b[0;32m~/miniconda3/envs/pso/lib/python3.9/site-packages/tensorflow/python/framework/func_graph.py:749\u001b[0m, in \u001b[0;36mFuncGraph._create_op_internal\u001b[0;34m(self, op_type, inputs, dtypes, input_types, name, attrs, op_def, compute_device)\u001b[0m\n\u001b[1;32m 747\u001b[0m inp \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mcapture(inp)\n\u001b[1;32m 748\u001b[0m captured_inputs\u001b[39m.\u001b[39mappend(inp)\n\u001b[0;32m--> 749\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39msuper\u001b[39;49m(FuncGraph, \u001b[39mself\u001b[39;49m)\u001b[39m.\u001b[39;49m_create_op_internal( \u001b[39m# pylint: disable=protected-access\u001b[39;49;00m\n\u001b[1;32m 750\u001b[0m op_type, captured_inputs, dtypes, input_types, name, attrs, op_def,\n\u001b[1;32m 751\u001b[0m compute_device)\n", - "File \u001b[0;32m~/miniconda3/envs/pso/lib/python3.9/site-packages/tensorflow/python/framework/ops.py:3798\u001b[0m, in \u001b[0;36mGraph._create_op_internal\u001b[0;34m(self, op_type, inputs, dtypes, input_types, name, attrs, op_def, compute_device)\u001b[0m\n\u001b[1;32m 3795\u001b[0m \u001b[39m# _create_op_helper mutates the new Operation. `_mutation_lock` ensures a\u001b[39;00m\n\u001b[1;32m 3796\u001b[0m \u001b[39m# Session.run call cannot occur between creating and mutating the op.\u001b[39;00m\n\u001b[1;32m 3797\u001b[0m \u001b[39mwith\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_mutation_lock():\n\u001b[0;32m-> 3798\u001b[0m ret \u001b[39m=\u001b[39m Operation(\n\u001b[1;32m 3799\u001b[0m node_def,\n\u001b[1;32m 3800\u001b[0m \u001b[39mself\u001b[39;49m,\n\u001b[1;32m 3801\u001b[0m inputs\u001b[39m=\u001b[39;49minputs,\n\u001b[1;32m 3802\u001b[0m output_types\u001b[39m=\u001b[39;49mdtypes,\n\u001b[1;32m 3803\u001b[0m control_inputs\u001b[39m=\u001b[39;49mcontrol_inputs,\n\u001b[1;32m 3804\u001b[0m input_types\u001b[39m=\u001b[39;49minput_types,\n\u001b[1;32m 3805\u001b[0m original_op\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_default_original_op,\n\u001b[1;32m 3806\u001b[0m op_def\u001b[39m=\u001b[39;49mop_def)\n\u001b[1;32m 3807\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_create_op_helper(ret, compute_device\u001b[39m=\u001b[39mcompute_device)\n\u001b[1;32m 3808\u001b[0m \u001b[39mreturn\u001b[39;00m ret\n", - "File \u001b[0;32m~/miniconda3/envs/pso/lib/python3.9/site-packages/tensorflow/python/framework/ops.py:2085\u001b[0m, in \u001b[0;36mOperation.__init__\u001b[0;34m(***failed resolving arguments***)\u001b[0m\n\u001b[1;32m 2082\u001b[0m input_types \u001b[39m=\u001b[39m [i\u001b[39m.\u001b[39mdtype\u001b[39m.\u001b[39mbase_dtype \u001b[39mfor\u001b[39;00m i \u001b[39min\u001b[39;00m inputs]\n\u001b[1;32m 2083\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m 2084\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mall\u001b[39m(\n\u001b[0;32m-> 2085\u001b[0m x\u001b[39m.\u001b[39mis_compatible_with(i\u001b[39m.\u001b[39mdtype) \u001b[39mfor\u001b[39;00m i, x \u001b[39min\u001b[39;00m \u001b[39mzip\u001b[39;49m(inputs, input_types)):\n\u001b[1;32m 2086\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mTypeError\u001b[39;00m(\u001b[39m\"\u001b[39m\u001b[39mIn op \u001b[39m\u001b[39m'\u001b[39m\u001b[39m%s\u001b[39;00m\u001b[39m'\u001b[39m\u001b[39m, input types (\u001b[39m\u001b[39m%s\u001b[39;00m\u001b[39m) are not compatible \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m 2087\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mwith expected types (\u001b[39m\u001b[39m%s\u001b[39;00m\u001b[39m)\u001b[39m\u001b[39m\"\u001b[39m \u001b[39m%\u001b[39m\n\u001b[1;32m 2088\u001b[0m (node_def\u001b[39m.\u001b[39mname, [i\u001b[39m.\u001b[39mdtype \u001b[39mfor\u001b[39;00m i \u001b[39min\u001b[39;00m inputs], input_types))\n\u001b[1;32m 2090\u001b[0m \u001b[39m# Build the list of control inputs.\u001b[39;00m\n", - "\u001b[0;31mKeyboardInterrupt\u001b[0m: " - ] - } - ], - "source": [ - "%load_ext memory_profiler\n", - "import linecache\n", - "import os\n", - "import tracemalloc\n", - "\n", - "\n", - "def display_top(snapshot, key_type=\"lineno\", limit=10):\n", - " snapshot = snapshot.filter_traces(\n", - " (\n", - " tracemalloc.Filter(False, \"\"),\n", - " tracemalloc.Filter(False, \"\"),\n", - " )\n", - " )\n", - " top_stats = snapshot.statistics(key_type)\n", - "\n", - " print(\"Top %s lines\" % limit)\n", - " for index, stat in enumerate(top_stats[:limit], 1):\n", - " frame = stat.traceback[0]\n", - " print(\n", - " \"#%s: %s:%s: %.1f KiB\"\n", - " % (index, frame.filename, frame.lineno, stat.size / 1024)\n", - " )\n", - " line = linecache.getline(frame.filename, frame.lineno).strip()\n", - " if line:\n", - " print(\" %s\" % line)\n", - "\n", - " other = top_stats[limit:]\n", - " if other:\n", - " size = sum(stat.size for stat in other)\n", - " print(\"%s other: %.1f KiB\" % (len(other), size / 1024))\n", - " total = sum(stat.size for stat in top_stats)\n", - " print(\"Total allocated size: %.1f KiB\" % (total / 1024))\n", - "\n", - "\n", - "tracemalloc.start()\n", - "\n", - "model = make_model()\n", - "x_train, y_train = get_data_test()\n", - "\n", - "loss = [\n", - " \"mean_squared_error\",\n", - " \"categorical_crossentropy\",\n", - " \"sparse_categorical_crossentropy\",\n", - " \"binary_crossentropy\",\n", - " \"kullback_leibler_divergence\",\n", - " \"poisson\",\n", - " \"cosine_similarity\",\n", - " \"log_cosh\",\n", - " \"huber_loss\",\n", - " \"mean_absolute_error\",\n", - " \"mean_absolute_percentage_error\",\n", - "]\n", - "\n", - "\n", - "pso_mnist = optimizer(\n", - " model,\n", - " loss=loss[0],\n", - " n_particles=70,\n", - " c0=0.3,\n", - " c1=0.5,\n", - " w_min=0.4,\n", - " w_max=0.7,\n", - " negative_swarm=0.1,\n", - " mutation_swarm=0.2,\n", - " particle_min=-5,\n", - " particle_max=5,\n", - ")\n", - "\n", - "best_score = pso_mnist.fit(\n", - " x_train,\n", - " y_train,\n", - " epochs=200,\n", - " save_info=True,\n", - " log=2,\n", - " save_path=\"./result/mnist\",\n", - " renewal=\"acc\",\n", - " check_point=25,\n", - ")\n", - "\n", - "print(\"Done!\")\n", - "\n", - "snapshot = tracemalloc.take_snapshot()\n", - "display_top(snapshot)\n", - "\n", - "%memit\n", - "gc.collect()\n", - "sys.exit(0)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "pso", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.16" - }, - "orig_nbformat": 4 - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/plt.ipynb b/plt.ipynb deleted file mode 100644 index 01bafaa..0000000 --- a/plt.ipynb +++ /dev/null @@ -1,15713 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(115, 8000)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:15: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " loss[i] = data[i]\n", - "/tmp/ipykernel_4018928/1584692904.py:17: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " acc[i] = data[i]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(115, 4000) (115, 4000)\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import pandas as pd\n", - "\n", - "%matplotlib inline\n", - "\n", - "data = pd.read_csv(\"logs/mnist/20230901-005949/4000_300_0.2_0.4_0.3_acc.csv\", header=None)\n", - "print(data.shape)\n", - "\n", - "loss = pd.DataFrame()\n", - "acc = pd.DataFrame()\n", - "\n", - "for i in range(len(data.iloc[0])):\n", - " if i % 2 == 0:\n", - " loss[i] = data[i]\n", - " else:\n", - " acc[i] = data[i]\n", - "\n", - "print(loss.shape, acc.shape)\n", - "\n", - "fig, axes = plt.subplots(nrows=2, sharey=False, sharex=True, figsize=(6, 6))\n", - "\n", - "loss.replace('nan' ,np.inf ,inplace=True)\n", - "loss = loss.fillna(np.inf)\n", - "\n", - "loss.plot(kind=\"line\", ax=axes[0], legend=False, alpha=0.5, ylabel=\"loss\", grid=True, title=f\"best loss {loss.min(numeric_only=True).min()}\")\n", - "\n", - "acc.plot(kind=\"line\", ax=axes[1], legend=False, alpha=0.5, ylabel=\"acc\", xlabel=\"epoch\", grid=True, title=f\"best acc {acc.max(numeric_only=True).max()}\")\n", - "\n", - "plt.show()\n", - "plt.clf()\n", - "plt.clf()\n", - "plt.close()\n", - "plt.close()\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "pso", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.16" - }, - "widgets": { - "application/vnd.jupyter.widget-state+json": { - "state": {}, - "version_major": 2, - "version_minor": 0 - } - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/pso/__init__.py b/pso/__init__.py index 46ba2ca..d973856 100644 --- a/pso/__init__.py +++ b/pso/__init__.py @@ -1,7 +1,7 @@ from .optimizer import Optimizer as optimizer from .particle import Particle as particle -__version__ = "0.1.6" +__version__ = "0.1.7" __all__ = [ "optimizer", diff --git a/requirements.txt b/requirements.txt index 3bacae2..663648a 100644 --- a/requirements.txt +++ b/requirements.txt @@ -2,5 +2,5 @@ ipython keras==2.11.0 numpy==1.25.0 pandas==1.5.3 -tensorflow==2.11.1 +tensorflow<=2.11.1 tqdm==4.65.0 diff --git a/setup.py b/setup.py index b2b40b9..53f4d87 100644 --- a/setup.py +++ b/setup.py @@ -16,6 +16,8 @@ setup( "numpy", "pandas", "ipython", + "tensorflow<=2.11.1", + "tensorboard", ], packages=find_packages(exclude=[]), keywords=["pso", "tensorflow", "keras"],