12-asina-test.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c772a64f-823c-4973-bf41-3c9a239c601a",
   "metadata": {},
   "source": [
    "# ASINA test set"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6cc25fc3-e84a-4d87-ace3-e947218d58aa",
   "metadata": {},
   "source": [
    "### Importing libraries and define functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5e31b3ce-df35-4b10-a99a-2f753f523fb9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import scipy\n",
    "import math\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1d53b807-2713-40cb-8199-e45bf7e3ca0d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns                       #visualisation\n",
    "import matplotlib.pyplot as plt             #visualisation\n",
    "%matplotlib inline     \n",
    "sns.set(color_codes=True)\n",
    "%config InlineBackend.figure_format = 'retina'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8375cb30",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Managing Warnings \n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2d2c745b-9e8f-415c-a43c-f08fb5937b46",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sklearn\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn import preprocessing\n",
    "from sklearn.impute import SimpleImputer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "03a6e29f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pycaret.regression import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0c7a62fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "import functions as f"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4fb11b21",
   "metadata": {},
   "source": [
    "### Define dataset columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7668f520",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_columns = ['NP_type', 'Coating','core_size_nm','hydro_size_nm','Surf_charge_mV','Surface_area_m2_g',\n",
    "               'shape','Dose_microg_mL','Duration_h','Cell_name','Cell_species','cell_Organ','Cell_morphology',\n",
    "               'Cell_age','cell_type','sex','Assay','Test_indicator','Viability']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf6d7f7e",
   "metadata": {},
   "source": [
    "### Loading the ASINA test data into the data frame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "da49b7e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "tdf = pd.read_csv(\"input/test_set_asina.txt\", sep=\"\\t\", encoding=\"unicode_escape\", usecols=df_columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "db18e698",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Does numeric columns have float64 pandas type? \n",
      "\n",
      "Duration_h: True\n",
      "core_size_nm: True\n",
      "hydro_size_nm: True\n",
      "Surf_charge_mV: True\n",
      "Surface_area_m2_g: True\n",
      "Dose_microg_mL: True\n",
      "Duration_h: True\n",
      "Viability: True\n",
      "\n",
      "Final column types: \n",
      "\n",
      "NP_type                object\n",
      "Coating                object\n",
      "core_size_nm          float64\n",
      "hydro_size_nm         float64\n",
      "Surf_charge_mV        float64\n",
      "Surface_area_m2_g     float64\n",
      "shape                  object\n",
      "Dose_microg_mL        float64\n",
      "Duration_h            float64\n",
      "Cell_name              object\n",
      "Cell_species           object\n",
      "cell_Organ             object\n",
      "Cell_morphology        object\n",
      "Cell_age               object\n",
      "cell_type              object\n",
      "sex                    object\n",
      "Assay                  object\n",
      "Test_indicator         object\n",
      "nanomaterial_group     object\n",
      "Viability             float64\n",
      "dtype: object\n",
      "\n",
      "NP types that fall into the 'other' group:\n",
      "['Ag']\n"
     ]
    }
   ],
   "source": [
    "data_asina_test_df = f.canonicalize_dataset(tdf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "863cf301",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(969, 20)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_asina_test_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ebeeb573",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NP_type</th>\n",
       "      <th>Coating</th>\n",
       "      <th>core_size_nm</th>\n",
       "      <th>hydro_size_nm</th>\n",
       "      <th>Surf_charge_mV</th>\n",
       "      <th>Surface_area_m2_g</th>\n",
       "      <th>shape</th>\n",
       "      <th>Dose_microg_mL</th>\n",
       "      <th>Duration_h</th>\n",
       "      <th>Cell_name</th>\n",
       "      <th>Cell_species</th>\n",
       "      <th>cell_Organ</th>\n",
       "      <th>Cell_morphology</th>\n",
       "      <th>Cell_age</th>\n",
       "      <th>cell_type</th>\n",
       "      <th>sex</th>\n",
       "      <th>Assay</th>\n",
       "      <th>Test_indicator</th>\n",
       "      <th>nanomaterial_group</th>\n",
       "      <th>Viability</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ag</td>\n",
       "      <td>None</td>\n",
       "      <td>150.0</td>\n",
       "      <td>144.04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sphere</td>\n",
       "      <td>1.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>A549</td>\n",
       "      <td>Human</td>\n",
       "      <td>Lung</td>\n",
       "      <td>Epithelial</td>\n",
       "      <td>Adult</td>\n",
       "      <td>cancer</td>\n",
       "      <td>male</td>\n",
       "      <td>MTT</td>\n",
       "      <td>TetrazoliumSalt</td>\n",
       "      <td>other</td>\n",
       "      <td>80.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ag</td>\n",
       "      <td>None</td>\n",
       "      <td>150.0</td>\n",
       "      <td>145.09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sphere</td>\n",
       "      <td>5.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>A549</td>\n",
       "      <td>Human</td>\n",
       "      <td>Lung</td>\n",
       "      <td>Epithelial</td>\n",
       "      <td>Adult</td>\n",
       "      <td>cancer</td>\n",
       "      <td>male</td>\n",
       "      <td>MTT</td>\n",
       "      <td>TetrazoliumSalt</td>\n",
       "      <td>other</td>\n",
       "      <td>65.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ag</td>\n",
       "      <td>None</td>\n",
       "      <td>150.0</td>\n",
       "      <td>146.40</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sphere</td>\n",
       "      <td>10.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>A549</td>\n",
       "      <td>Human</td>\n",
       "      <td>Lung</td>\n",
       "      <td>Epithelial</td>\n",
       "      <td>Adult</td>\n",
       "      <td>cancer</td>\n",
       "      <td>male</td>\n",
       "      <td>MTT</td>\n",
       "      <td>TetrazoliumSalt</td>\n",
       "      <td>other</td>\n",
       "      <td>64.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ag</td>\n",
       "      <td>None</td>\n",
       "      <td>150.0</td>\n",
       "      <td>149.02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sphere</td>\n",
       "      <td>20.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>A549</td>\n",
       "      <td>Human</td>\n",
       "      <td>Lung</td>\n",
       "      <td>Epithelial</td>\n",
       "      <td>Adult</td>\n",
       "      <td>cancer</td>\n",
       "      <td>male</td>\n",
       "      <td>MTT</td>\n",
       "      <td>TetrazoliumSalt</td>\n",
       "      <td>other</td>\n",
       "      <td>57.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ag</td>\n",
       "      <td>None</td>\n",
       "      <td>150.0</td>\n",
       "      <td>154.27</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sphere</td>\n",
       "      <td>40.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>A549</td>\n",
       "      <td>Human</td>\n",
       "      <td>Lung</td>\n",
       "      <td>Epithelial</td>\n",
       "      <td>Adult</td>\n",
       "      <td>cancer</td>\n",
       "      <td>male</td>\n",
       "      <td>MTT</td>\n",
       "      <td>TetrazoliumSalt</td>\n",
       "      <td>other</td>\n",
       "      <td>35.84</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  NP_type Coating  core_size_nm  hydro_size_nm  Surf_charge_mV  \\\n",
       "0      Ag    None         150.0         144.04             NaN   \n",
       "1      Ag    None         150.0         145.09             NaN   \n",
       "2      Ag    None         150.0         146.40             NaN   \n",
       "3      Ag    None         150.0         149.02             NaN   \n",
       "4      Ag    None         150.0         154.27             NaN   \n",
       "\n",
       "   Surface_area_m2_g   shape  Dose_microg_mL  Duration_h Cell_name  \\\n",
       "0                NaN  Sphere             1.0        24.0      A549   \n",
       "1                NaN  Sphere             5.0        24.0      A549   \n",
       "2                NaN  Sphere            10.0        24.0      A549   \n",
       "3                NaN  Sphere            20.0        24.0      A549   \n",
       "4                NaN  Sphere            40.0        24.0      A549   \n",
       "\n",
       "  Cell_species cell_Organ Cell_morphology Cell_age cell_type   sex Assay  \\\n",
       "0        Human       Lung      Epithelial    Adult    cancer  male   MTT   \n",
       "1        Human       Lung      Epithelial    Adult    cancer  male   MTT   \n",
       "2        Human       Lung      Epithelial    Adult    cancer  male   MTT   \n",
       "3        Human       Lung      Epithelial    Adult    cancer  male   MTT   \n",
       "4        Human       Lung      Epithelial    Adult    cancer  male   MTT   \n",
       "\n",
       "    Test_indicator nanomaterial_group  Viability  \n",
       "0  TetrazoliumSalt              other      80.15  \n",
       "1  TetrazoliumSalt              other      65.05  \n",
       "2  TetrazoliumSalt              other      64.27  \n",
       "3  TetrazoliumSalt              other      57.01  \n",
       "4  TetrazoliumSalt              other      35.84  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_asina_test_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3894927b",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_train_df = pd.read_csv(\"output/datasets/universal/data_regression_train_df.csv\", encoding=\"utf-8\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bccbe653",
   "metadata": {},
   "source": [
    "### Plot numerical variables distribution of train and ASINA test sets "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a971dc5a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x3000 with 7 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 1016,
       "width": 1635
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f.plot_distribution_train_test(data_train_df, data_asina_test_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "140cae24",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NP_type\n",
      "53 - 2 - 2\n",
      "Coating\n",
      "34 - 4 - 2\n",
      "core_size_nm\n",
      "516 - 4 - 4\n",
      "hydro_size_nm\n",
      "1914 - 579 - 2\n",
      "Surf_charge_mV\n",
      "403 - 809 - 2\n",
      "Surface_area_m2_g\n",
      "3306 - 969 - 0\n",
      "shape\n",
      "13 - 2 - 2\n",
      "Dose_microg_mL\n",
      "240 - 16 - 16\n",
      "Duration_h\n",
      "277 - 1 - 1\n",
      "Cell_name\n",
      "82 - 2 - 2\n",
      "Cell_species\n",
      "8 - 1 - 1\n",
      "cell_Organ\n",
      "30 - 2 - 2\n",
      "Cell_morphology\n",
      "16 - 1 - 1\n",
      "Cell_age\n",
      "5 - 1 - 1\n",
      "cell_type\n",
      "3 - 1 - 1\n",
      "sex\n",
      "3 - 1 - 1\n",
      "Assay\n",
      "19 - 3 - 3\n",
      "Test_indicator\n",
      "13 - 2 - 1\n",
      "nanomaterial_group\n",
      "5 - 2 - 2\n",
      "Viability\n",
      "2746 - 913 - 279\n"
     ]
    }
   ],
   "source": [
    "for i in data_train_df.columns:\n",
    "    print(i)\n",
    "    unique_train = len(set(data_train_df[i].tolist()))\n",
    "    unique_test = len(set(data_asina_test_df[i].tolist()))\n",
    "    unique_intersect = len(set(data_train_df[i].tolist()).intersection(set(data_asina_test_df[i].tolist())))\n",
    "    print(str(unique_train) + \" - \" + str(unique_test) + \" - \" + str(unique_intersect))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69d0f4c0",
   "metadata": {},
   "source": [
    "### Prepare classification dataframes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "aa75269e",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_classification_asina_test_df = f.convert_to_classification_dataset(data_asina_test_df.copy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "6043d936",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(969, 20)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_classification_asina_test_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a2375a4",
   "metadata": {},
   "source": [
    "# PyCaret"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d808957",
   "metadata": {},
   "source": [
    "### Load the universal regression model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "cf3430fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_config = load_config('output/models/universal/regression_config.cfg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "44fcbb86",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Transformation Pipeline and Model Successfully Loaded\n"
     ]
    }
   ],
   "source": [
    "final_model = load_model('output/models/universal/regression_model_et_final.mdl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "25be94da",
   "metadata": {},
   "outputs": [
    {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>core_size_nm</th>\n",
       "      <th>Surf_charge_mV</th>\n",
       "      <th>Dose_microg_mL</th>\n",
       "      <th>Duration_h</th>\n",
       "      <th>NP_type_Ag</th>\n",
       "      <th>NP_type_Al2O3</th>\n",
       "      <th>NP_type_Au</th>\n",
       "      <th>NP_type_Bi2O3</th>\n",
       "      <th>NP_type_C60</th>\n",
       "      <th>NP_type_C70</th>\n",
       "      <th>NP_type_Carbon</th>\n",
       "      <th>NP_type_CeO2</th>\n",
       "      <th>NP_type_Chitosan</th>\n",
       "      <th>NP_type_Co3O4</th>\n",
       "      <th>NP_type_CoO</th>\n",
       "      <th>NP_type_Cr2O3</th>\n",
       "      <th>NP_type_CuO</th>\n",
       "      <th>NP_type_Dendrimer</th>\n",
       "      <th>NP_type_Diamond</th>\n",
       "      <th>NP_type_Dy2O3</th>\n",
       "      <th>NP_type_Er2O3</th>\n",
       "      <th>NP_type_Eu2O3</th>\n",
       "      <th>NP_type_EudragitRL</th>\n",
       "      <th>NP_type_Fe2O3</th>\n",
       "      <th>NP_type_Fe3O4</th>\n",
       "      <th>NP_type_Gd2O3</th>\n",
       "      <th>NP_type_Graphite</th>\n",
       "      <th>NP_type_HfO2</th>\n",
       "      <th>NP_type_Hydroxyapatite</th>\n",
       "      <th>NP_type_In2O3</th>\n",
       "      <th>NP_type_IronOxide</th>\n",
       "      <th>NP_type_La2O3</th>\n",
       "      <th>NP_type_MWCNT</th>\n",
       "      <th>NP_type_MgO</th>\n",
       "      <th>NP_type_Mn2O3</th>\n",
       "      <th>NP_type_MnO2</th>\n",
       "      <th>NP_type_Nd2O3</th>\n",
       "      <th>NP_type_Ni2O3</th>\n",
       "      <th>NP_type_NiO</th>\n",
       "      <th>NP_type_PLGA</th>\n",
       "      <th>NP_type_PTFE-PMMA</th>\n",
       "      <th>NP_type_Polystyrene</th>\n",
       "      <th>NP_type_Pt</th>\n",
       "      <th>NP_type_SLN</th>\n",
       "      <th>NP_type_SWCNT</th>\n",
       "      <th>NP_type_Sb2O3</th>\n",
       "      <th>NP_type_SiO2</th>\n",
       "      <th>NP_type_Sm2O3</th>\n",
       "      <th>NP_type_SnO2</th>\n",
       "      <th>NP_type_TiO2</th>\n",
       "      <th>NP_type_WO3</th>\n",
       "      <th>NP_type_Y2O3</th>\n",
       "      <th>NP_type_Yb2O3</th>\n",
       "      <th>NP_type_ZnO</th>\n",
       "      <th>NP_type_ZrO2</th>\n",
       "      <th>Coating_Ac</th>\n",
       "      <th>Coating_C12</th>\n",
       "      <th>Coating_CH3</th>\n",
       "      <th>Coating_COOH</th>\n",
       "      <th>Coating_CTAB</th>\n",
       "      <th>Coating_Chitosan</th>\n",
       "      <th>Coating_Citrate_based</th>\n",
       "      <th>Coating_CyA</th>\n",
       "      <th>Coating_Cystein_based</th>\n",
       "      <th>Coating_DDAB</th>\n",
       "      <th>Coating_DHLA</th>\n",
       "      <th>Coating_DPPC</th>\n",
       "      <th>Coating_Dextran</th>\n",
       "      <th>Coating_Digestive enzymes</th>\n",
       "      <th>Coating_Folic_acid_based</th>\n",
       "      <th>Coating_GSH</th>\n",
       "      <th>Coating_Liposome</th>\n",
       "      <th>Coating_NH2</th>\n",
       "      <th>Coating_None</th>\n",
       "      <th>Coating_OH</th>\n",
       "      <th>Coating_OS</th>\n",
       "      <th>Coating_OleicAcid</th>\n",
       "      <th>Coating_PAMAM</th>\n",
       "      <th>Coating_PEG_based</th>\n",
       "      <th>Coating_PEI</th>\n",
       "      <th>Coating_PVA</th>\n",
       "      <th>Coating_PVP</th>\n",
       "      <th>Coating_Phosphonate</th>\n",
       "      <th>Coating_Poloxamer</th>\n",
       "      <th>Coating_SC</th>\n",
       "      <th>Coating_silica_based</th>\n",
       "      <th>Coating_simethicone then esters on top</th>\n",
       "      <th>Coating_sodium_based</th>\n",
       "      <th>shape_Cubic</th>\n",
       "      <th>shape_Hexagon</th>\n",
       "      <th>shape_Irregular</th>\n",
       "      <th>shape_Monoclinic</th>\n",
       "      <th>shape_Needle</th>\n",
       "      <th>shape_Orthorhombic</th>\n",
       "      <th>shape_Rhombohedral</th>\n",
       "      <th>shape_Rod</th>\n",
       "      <th>shape_Sphere</th>\n",
       "      <th>shape_Star</th>\n",
       "      <th>shape_Tetragonal</th>\n",
       "      <th>shape_Wire</th>\n",
       "      <th>shape_not_available</th>\n",
       "      <th>Cell_name_16HBE14</th>\n",
       "      <th>Cell_name_3T3_L1</th>\n",
       "      <th>Cell_name_A2780</th>\n",
       "      <th>Cell_name_A431</th>\n",
       "      <th>Cell_name_A549</th>\n",
       "      <th>Cell_name_ASM</th>\n",
       "      <th>Cell_name_AT2</th>\n",
       "      <th>Cell_name_BALB_c3T3</th>\n",
       "      <th>Cell_name_BEAS_2B</th>\n",
       "      <th>Cell_name_BEC</th>\n",
       "      <th>Cell_name_BJ</th>\n",
       "      <th>Cell_name_C17_2</th>\n",
       "      <th>Cell_name_C3A</th>\n",
       "      <th>Cell_name_C6</th>\n",
       "      <th>Cell_name_CCL-110</th>\n",
       "      <th>Cell_name_Caco_2</th>\n",
       "      <th>Cell_name_Calu-3</th>\n",
       "      <th>Cell_name_Chang_Liver</th>\n",
       "      <th>Cell_name_Colo­205</th>\n",
       "      <th>Cell_name_HCMEC</th>\n",
       "      <th>Cell_name_HCT-116</th>\n",
       "      <th>Cell_name_HDF</th>\n",
       "      <th>Cell_name_HEBC3-KT</th>\n",
       "      <th>Cell_name_HEK</th>\n",
       "      <th>Cell_name_HEK293</th>\n",
       "      <th>Cell_name_HFL1</th>\n",
       "      <th>Cell_name_HK-2</th>\n",
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       "      <th>Cell_name_HT29</th>\n",
       "      <th>Cell_name_HaCat</th>\n",
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       "      <th>Cell_name_HepG2</th>\n",
       "      <th>Cell_name_IMR90</th>\n",
       "      <th>Cell_name_J774A.1</th>\n",
       "      <th>Cell_name_Jurkat</th>\n",
       "      <th>Cell_name_K562</th>\n",
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       "      <th>Cell_name_L-02</th>\n",
       "      <th>Cell_name_L-132</th>\n",
       "      <th>Cell_name_L929</th>\n",
       "      <th>Cell_name_MBMC</th>\n",
       "      <th>Cell_name_MCF-10A</th>\n",
       "      <th>Cell_name_MCF7</th>\n",
       "      <th>Cell_name_Macrophages</th>\n",
       "      <th>Cell_name_Monocytes</th>\n",
       "      <th>Cell_name_NHDF</th>\n",
       "      <th>Cell_name_NIH3T3</th>\n",
       "      <th>Cell_name_NR8383</th>\n",
       "      <th>Cell_name_PBMC</th>\n",
       "      <th>Cell_name_RAW 264.7</th>\n",
       "      <th>Cell_name_SH­SY5Y</th>\n",
       "      <th>Cell_name_SKOV­3</th>\n",
       "      <th>Cell_name_SV-80</th>\n",
       "      <th>Cell_name_SW480</th>\n",
       "      <th>Cell_name_THP_1</th>\n",
       "      <th>Cell_name_TT1</th>\n",
       "      <th>Cell_name_U87</th>\n",
       "      <th>Cell_name_V79</th>\n",
       "      <th>Cell_name_VERO</th>\n",
       "      <th>Cell_name_WA09</th>\n",
       "      <th>Cell_name_WI_38</th>\n",
       "      <th>Cell_name_hNPC</th>\n",
       "      <th>Cell_name_hPDLF</th>\n",
       "      <th>Cell_name_mDF</th>\n",
       "      <th>Cell_name_primary</th>\n",
       "      <th>Cell_species_Cow</th>\n",
       "      <th>Cell_species_Dog</th>\n",
       "      <th>Cell_species_Human</th>\n",
       "      <th>Cell_species_Mouse</th>\n",
       "      <th>Cell_species_Rabbit</th>\n",
       "      <th>Cell_species_Rat</th>\n",
       "      <th>cell_Organ_Adrenal-gland</th>\n",
       "      <th>cell_Organ_Amniotic-membrane</th>\n",
       "      <th>cell_Organ_Blood</th>\n",
       "      <th>cell_Organ_BoneMarrow</th>\n",
       "      <th>cell_Organ_Brain</th>\n",
       "      <th>cell_Organ_Breast</th>\n",
       "      <th>cell_Organ_Colon</th>\n",
       "      <th>cell_Organ_Connective-tissue</th>\n",
       "      <th>cell_Organ_Cortex</th>\n",
       "      <th>cell_Organ_Embryo</th>\n",
       "      <th>cell_Organ_FatPad</th>\n",
       "      <th>cell_Organ_Heart</th>\n",
       "      <th>cell_Organ_Kidney</th>\n",
       "      <th>cell_Organ_Liver</th>\n",
       "      <th>cell_Organ_Lung</th>\n",
       "      <th>cell_Organ_Mesothelium</th>\n",
       "      <th>cell_Organ_Ovary</th>\n",
       "      <th>cell_Organ_Peritoneal Cavity</th>\n",
       "      <th>cell_Organ_Skin</th>\n",
       "      <th>cell_Organ_Stomach</th>\n",
       "      <th>cell_Organ_Umbilical</th>\n",
       "      <th>Cell_morphology_Endothelial</th>\n",
       "      <th>Cell_morphology_Epithelial</th>\n",
       "      <th>Cell_morphology_Fibroblast</th>\n",
       "      <th>Cell_morphology_Keratinocyte</th>\n",
       "      <th>Cell_morphology_Lymphoblast</th>\n",
       "      <th>Cell_morphology_Macrophage</th>\n",
       "      <th>Cell_morphology_Mesenchymal</th>\n",
       "      <th>Cell_morphology_Mixed</th>\n",
       "      <th>Cell_morphology_Monocyte</th>\n",
       "      <th>Cell_morphology_MulticellularClusters</th>\n",
       "      <th>Cell_morphology_Neural</th>\n",
       "      <th>Cell_morphology_Preosteoblast</th>\n",
       "      <th>Cell_morphology_Spindle</th>\n",
       "      <th>Cell_morphology_monocyte</th>\n",
       "      <th>Cell_age_Adult</th>\n",
       "      <th>Cell_age_Young</th>\n",
       "      <th>Cell_age_fetus</th>\n",
       "      <th>Cell_age_neonate</th>\n",
       "      <th>Cell_age_not_available</th>\n",
       "      <th>cell_type_Normal</th>\n",
       "      <th>cell_type_cancer</th>\n",
       "      <th>sex_female</th>\n",
       "      <th>sex_male</th>\n",
       "      <th>sex_not_available</th>\n",
       "      <th>Assay_ATPLite</th>\n",
       "      <th>Assay_Alamar_Blue</th>\n",
       "      <th>Assay_Annexiv_V_</th>\n",
       "      <th>Assay_CCK_8</th>\n",
       "      <th>Assay_CellTiterBlue</th>\n",
       "      <th>Assay_CellTiter_Glo</th>\n",
       "      <th>Assay_CytoTox-Glo</th>\n",
       "      <th>Assay_EZ4U</th>\n",
       "      <th>Assay_LDH</th>\n",
       "      <th>Assay_Live/Dead</th>\n",
       "      <th>Assay_MTS</th>\n",
       "      <th>Assay_MTT</th>\n",
       "      <th>Assay_Promegas CTB Assay</th>\n",
       "      <th>Assay_Vialight</th>\n",
       "      <th>Assay_WST-1</th>\n",
       "      <th>Assay_XTT</th>\n",
       "      <th>Test_indicator_AlamarBlue</th>\n",
       "      <th>Test_indicator_Annexin V</th>\n",
       "      <th>Test_indicator_CalceinAM</th>\n",
       "      <th>Test_indicator_Impedance</th>\n",
       "      <th>Test_indicator_Sytox Red</th>\n",
       "      <th>Test_indicator_TetrazoliumSalt</th>\n",
       "      <th>Test_indicator_Trypan_Blue</th>\n",
       "      <th>Test_indicator_luciferase</th>\n",
       "      <th>Test_indicator_not_available</th>\n",
       "      <th>Test_indicator_propidium_iodide</th>\n",
       "      <th>Test_indicator_toluylene red</th>\n",
       "      <th>nanomaterial_group_carbon</th>\n",
       "      <th>nanomaterial_group_meta_oxide</th>\n",
       "      <th>nanomaterial_group_nanotubes</th>\n",
       "      <th>nanomaterial_group_other</th>\n",
       "      <th>nanomaterial_group_quantum_dots</th>\n",
       "      <th>Viability</th>\n",
       "      <th>Label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>104.589996</td>\n",
       "      <td>100.942000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-3.446481e-01</td>\n",
       "      <td>1.539525</td>\n",
       "      <td>-0.265763</td>\n",
       "      <td>-0.423384</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>93.769997</td>\n",
       "      <td>95.888400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.088713e-02</td>\n",
       "      <td>-0.661574</td>\n",
       "      <td>-0.265950</td>\n",
       "      <td>-0.423384</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>91.919998</td>\n",
       "      <td>85.453999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.322197e-01</td>\n",
       "      <td>-0.051457</td>\n",
       "      <td>-0.116313</td>\n",
       "      <td>-0.423384</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>49.349998</td>\n",
       "      <td>50.965199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.207865e-08</td>\n",
       "      <td>0.043121</td>\n",
       "      <td>0.482236</td>\n",
       "      <td>-0.423384</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>928</th>\n",
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       "      <th>929</th>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>109.234002</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>932 rows × 251 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     core_size_nm  Surf_charge_mV  Dose_microg_mL  Duration_h  NP_type_Ag  \\\n",
       "0   -6.201670e-01       -0.689088       -0.219189   -1.323329         0.0   \n",
       "1   -3.446481e-01        1.539525       -0.265763   -0.423384         0.0   \n",
       "2    4.088713e-02       -0.661574       -0.265950   -0.423384         0.0   \n",
       "3    2.322197e-01       -0.051457       -0.116313   -0.423384         0.0   \n",
       "4   -1.207865e-08        0.043121        0.482236   -0.423384         0.0   \n",
       "..            ...             ...             ...         ...         ...   \n",
       "927 -4.374444e-01       -0.330722       -0.228541   -0.423384         0.0   \n",
       "928 -4.891999e-01       -0.199000       -0.247246    0.776542         0.0   \n",
       "929 -2.461118e-01       -0.488238       -0.078904    0.776542         0.0   \n",
       "930 -6.201670e-01       -0.689088        0.014619   -1.473320         0.0   \n",
       "931 -6.526936e-01       -0.087225       -0.078904   -0.423384         0.0   \n",
       "\n",
       "     NP_type_Al2O3  NP_type_Au  NP_type_Bi2O3  NP_type_C60  NP_type_C70  \\\n",
       "0              0.0         0.0            0.0          0.0          0.0   \n",
       "1              1.0         0.0            0.0          0.0          0.0   \n",
       "2              0.0         0.0            0.0          0.0          0.0   \n",
       "3              0.0         0.0            0.0          0.0          0.0   \n",
       "4              0.0         0.0            0.0          0.0          0.0   \n",
       "..             ...         ...            ...          ...          ...   \n",
       "927            0.0         0.0            0.0          0.0          0.0   \n",
       "928            0.0         0.0            0.0          0.0          0.0   \n",
       "929            0.0         0.0            0.0          0.0          0.0   \n",
       "930            0.0         0.0            0.0          0.0          0.0   \n",
       "931            0.0         0.0            0.0          0.0          0.0   \n",
       "\n",
       "     NP_type_Carbon  NP_type_CeO2  NP_type_Chitosan  NP_type_Co3O4  \\\n",
       "0               0.0           0.0               0.0            0.0   \n",
       "1               0.0           0.0               0.0            0.0   \n",
       "2               0.0           0.0               0.0            0.0   \n",
       "3               0.0           0.0               0.0            0.0   \n",
       "4               0.0           0.0               0.0            0.0   \n",
       "..              ...           ...               ...            ...   \n",
       "927             0.0           0.0               0.0            0.0   \n",
       "928             0.0           0.0               0.0            0.0   \n",
       "929             0.0           0.0               0.0            0.0   \n",
       "930             0.0           0.0               0.0            0.0   \n",
       "931             0.0           0.0               0.0            0.0   \n",
       "\n",
       "     NP_type_CoO  NP_type_Cr2O3  NP_type_CuO  NP_type_Dendrimer  \\\n",
       "0            0.0            0.0          0.0                0.0   \n",
       "1            0.0            0.0          0.0                0.0   \n",
       "2            0.0            0.0          0.0                0.0   \n",
       "3            0.0            0.0          0.0                0.0   \n",
       "4            0.0            0.0          0.0                0.0   \n",
       "..           ...            ...          ...                ...   \n",
       "927          0.0            0.0          1.0                0.0   \n",
       "928          0.0            0.0          0.0                0.0   \n",
       "929          0.0            0.0          0.0                0.0   \n",
       "930          0.0            0.0          0.0                0.0   \n",
       "931          0.0            0.0          0.0                0.0   \n",
       "\n",
       "     NP_type_Diamond  NP_type_Dy2O3  NP_type_Er2O3  NP_type_Eu2O3  \\\n",
       "0                0.0            0.0            0.0            0.0   \n",
       "1                0.0            0.0            0.0            0.0   \n",
       "2                0.0            0.0            0.0            0.0   \n",
       "3                0.0            0.0            0.0            0.0   \n",
       "4                0.0            0.0            0.0            0.0   \n",
       "..               ...            ...            ...            ...   \n",
       "927              0.0            0.0            0.0            0.0   \n",
       "928              0.0            0.0            0.0            0.0   \n",
       "929              0.0            0.0            0.0            0.0   \n",
       "930              0.0            0.0            0.0            0.0   \n",
       "931              0.0            0.0            0.0            0.0   \n",
       "\n",
       "     NP_type_EudragitRL  NP_type_Fe2O3  NP_type_Fe3O4  NP_type_Gd2O3  \\\n",
       "0                   0.0            0.0            0.0            0.0   \n",
       "1                   0.0            0.0            0.0            0.0   \n",
       "2                   0.0            0.0            0.0            0.0   \n",
       "3                   0.0            0.0            0.0            0.0   \n",
       "4                   0.0            0.0            0.0            0.0   \n",
       "..                  ...            ...            ...            ...   \n",
       "927                 0.0            0.0            0.0            0.0   \n",
       "928                 0.0            0.0            0.0            0.0   \n",
       "929                 0.0            0.0            0.0            0.0   \n",
       "930                 0.0            0.0            0.0            0.0   \n",
       "931                 0.0            0.0            0.0            0.0   \n",
       "\n",
       "     NP_type_Graphite  NP_type_HfO2  NP_type_Hydroxyapatite  NP_type_In2O3  \\\n",
       "0                 0.0           0.0                     0.0            0.0   \n",
       "1                 0.0           0.0                     0.0            0.0   \n",
       "2                 0.0           0.0                     0.0            0.0   \n",
       "3                 0.0           0.0                     0.0            0.0   \n",
       "4                 0.0           0.0                     0.0            0.0   \n",
       "..                ...           ...                     ...            ...   \n",
       "927               0.0           0.0                     0.0            0.0   \n",
       "928               0.0           0.0                     0.0            0.0   \n",
       "929               0.0           0.0                     0.0            0.0   \n",
       "930               0.0           0.0                     0.0            0.0   \n",
       "931               0.0           0.0                     0.0            0.0   \n",
       "\n",
       "     NP_type_IronOxide  NP_type_La2O3  NP_type_MWCNT  NP_type_MgO  \\\n",
       "0                  1.0            0.0            0.0          0.0   \n",
       "1                  0.0            0.0            0.0          0.0   \n",
       "2                  1.0            0.0            0.0          0.0   \n",
       "3                  0.0            0.0            0.0          0.0   \n",
       "4                  0.0            0.0            0.0          1.0   \n",
       "..                 ...            ...            ...          ...   \n",
       "927                0.0            0.0            0.0          0.0   \n",
       "928                0.0            0.0            0.0          0.0   \n",
       "929                0.0            0.0            0.0          0.0   \n",
       "930                1.0            0.0            0.0          0.0   \n",
       "931                1.0            0.0            0.0          0.0   \n",
       "\n",
       "     NP_type_Mn2O3  NP_type_MnO2  NP_type_Nd2O3  NP_type_Ni2O3  NP_type_NiO  \\\n",
       "0              0.0           0.0            0.0            0.0          0.0   \n",
       "1              0.0           0.0            0.0            0.0          0.0   \n",
       "2              0.0           0.0            0.0            0.0          0.0   \n",
       "3              0.0           0.0            0.0            0.0          0.0   \n",
       "4              0.0           0.0            0.0            0.0          0.0   \n",
       "..             ...           ...            ...            ...          ...   \n",
       "927            0.0           0.0            0.0            0.0          0.0   \n",
       "928            0.0           0.0            0.0            0.0          0.0   \n",
       "929            0.0           0.0            0.0            0.0          0.0   \n",
       "930            0.0           0.0            0.0            0.0          0.0   \n",
       "931            0.0           0.0            0.0            0.0          0.0   \n",
       "\n",
       "     NP_type_PLGA  NP_type_PTFE-PMMA  NP_type_Polystyrene  NP_type_Pt  \\\n",
       "0             0.0                0.0                  0.0         0.0   \n",
       "1             0.0                0.0                  0.0         0.0   \n",
       "2             0.0                0.0                  0.0         0.0   \n",
       "3             0.0                0.0                  0.0         0.0   \n",
       "4             0.0                0.0                  0.0         0.0   \n",
       "..            ...                ...                  ...         ...   \n",
       "927           0.0                0.0                  0.0         0.0   \n",
       "928           0.0                0.0                  0.0         0.0   \n",
       "929           0.0                0.0                  1.0         0.0   \n",
       "930           0.0                0.0                  0.0         0.0   \n",
       "931           0.0                0.0                  0.0         0.0   \n",
       "\n",
       "     NP_type_SLN  NP_type_SWCNT  NP_type_Sb2O3  NP_type_SiO2  NP_type_Sm2O3  \\\n",
       "0            0.0            0.0            0.0           0.0            0.0   \n",
       "1            0.0            0.0            0.0           0.0            0.0   \n",
       "2            0.0            0.0            0.0           0.0            0.0   \n",
       "3            0.0            0.0            0.0           0.0            0.0   \n",
       "4            0.0            0.0            0.0           0.0            0.0   \n",
       "..           ...            ...            ...           ...            ...   \n",
       "927          0.0            0.0            0.0           0.0            0.0   \n",
       "928          0.0            0.0            0.0           0.0            0.0   \n",
       "929          0.0            0.0            0.0           0.0            0.0   \n",
       "930          0.0            0.0            0.0           0.0            0.0   \n",
       "931          0.0            0.0            0.0           0.0            0.0   \n",
       "\n",
       "     NP_type_SnO2  NP_type_TiO2  NP_type_WO3  NP_type_Y2O3  NP_type_Yb2O3  \\\n",
       "0             0.0           0.0          0.0           0.0            0.0   \n",
       "1             0.0           0.0          0.0           0.0            0.0   \n",
       "2             0.0           0.0          0.0           0.0            0.0   \n",
       "3             0.0           0.0          0.0           0.0            0.0   \n",
       "4             0.0           0.0          0.0           0.0            0.0   \n",
       "..            ...           ...          ...           ...            ...   \n",
       "927           0.0           0.0          0.0           0.0            0.0   \n",
       "928           0.0           1.0          0.0           0.0            0.0   \n",
       "929           0.0           0.0          0.0           0.0            0.0   \n",
       "930           0.0           0.0          0.0           0.0            0.0   \n",
       "931           0.0           0.0          0.0           0.0            0.0   \n",
       "\n",
       "     NP_type_ZnO  NP_type_ZrO2  Coating_Ac  Coating_C12  Coating_CH3  \\\n",
       "0            0.0           0.0         0.0          0.0          0.0   \n",
       "1            0.0           0.0         0.0          0.0          0.0   \n",
       "2            0.0           0.0         0.0          0.0          0.0   \n",
       "3            1.0           0.0         0.0          0.0          0.0   \n",
       "4            0.0           0.0         0.0          0.0          0.0   \n",
       "..           ...           ...         ...          ...          ...   \n",
       "927          0.0           0.0         0.0          0.0          0.0   \n",
       "928          0.0           0.0         0.0          0.0          0.0   \n",
       "929          0.0           0.0         0.0          0.0          0.0   \n",
       "930          0.0           0.0         0.0          0.0          0.0   \n",
       "931          0.0           0.0         0.0          0.0          0.0   \n",
       "\n",
       "     Coating_COOH  Coating_CTAB  Coating_Chitosan  Coating_Citrate_based  \\\n",
       "0             0.0           0.0               0.0                    0.0   \n",
       "1             0.0           0.0               0.0                    0.0   \n",
       "2             0.0           0.0               0.0                    0.0   \n",
       "3             0.0           0.0               0.0                    0.0   \n",
       "4             0.0           0.0               0.0                    0.0   \n",
       "..            ...           ...               ...                    ...   \n",
       "927           0.0           0.0               0.0                    0.0   \n",
       "928           0.0           0.0               0.0                    0.0   \n",
       "929           0.0           0.0               0.0                    0.0   \n",
       "930           0.0           0.0               0.0                    0.0   \n",
       "931           0.0           0.0               0.0                    0.0   \n",
       "\n",
       "     Coating_CyA  Coating_Cystein_based  Coating_DDAB  Coating_DHLA  \\\n",
       "0            0.0                    0.0           0.0           0.0   \n",
       "1            0.0                    0.0           0.0           0.0   \n",
       "2            0.0                    0.0           0.0           0.0   \n",
       "3            0.0                    0.0           0.0           0.0   \n",
       "4            0.0                    0.0           0.0           0.0   \n",
       "..           ...                    ...           ...           ...   \n",
       "927          0.0                    0.0           0.0           0.0   \n",
       "928          0.0                    0.0           0.0           0.0   \n",
       "929          0.0                    0.0           0.0           0.0   \n",
       "930          0.0                    0.0           0.0           0.0   \n",
       "931          0.0                    0.0           0.0           0.0   \n",
       "\n",
       "     Coating_DPPC  Coating_Dextran  Coating_Digestive enzymes  \\\n",
       "0             0.0              0.0                        0.0   \n",
       "1             0.0              0.0                        0.0   \n",
       "2             0.0              0.0                        0.0   \n",
       "3             0.0              0.0                        0.0   \n",
       "4             0.0              0.0                        0.0   \n",
       "..            ...              ...                        ...   \n",
       "927           0.0              0.0                        0.0   \n",
       "928           0.0              0.0                        0.0   \n",
       "929           0.0              0.0                        0.0   \n",
       "930           0.0              0.0                        0.0   \n",
       "931           0.0              1.0                        0.0   \n",
       "\n",
       "     Coating_Folic_acid_based  Coating_GSH  Coating_Liposome  Coating_NH2  \\\n",
       "0                         0.0          0.0               0.0          0.0   \n",
       "1                         0.0          0.0               0.0          0.0   \n",
       "2                         1.0          0.0               0.0          0.0   \n",
       "3                         0.0          0.0               0.0          0.0   \n",
       "4                         0.0          0.0               0.0          0.0   \n",
       "..                        ...          ...               ...          ...   \n",
       "927                       0.0          0.0               0.0          0.0   \n",
       "928                       0.0          0.0               0.0          0.0   \n",
       "929                       0.0          0.0               0.0          0.0   \n",
       "930                       0.0          0.0               0.0          0.0   \n",
       "931                       0.0          0.0               0.0          0.0   \n",
       "\n",
       "     Coating_None  Coating_OH  Coating_OS  Coating_OleicAcid  Coating_PAMAM  \\\n",
       "0             0.0         0.0         0.0                1.0            0.0   \n",
       "1             1.0         0.0         0.0                0.0            0.0   \n",
       "2             0.0         0.0         0.0                0.0            0.0   \n",
       "3             1.0         0.0         0.0                0.0            0.0   \n",
       "4             1.0         0.0         0.0                0.0            0.0   \n",
       "..            ...         ...         ...                ...            ...   \n",
       "927           1.0         0.0         0.0                0.0            0.0   \n",
       "928           1.0         0.0         0.0                0.0            0.0   \n",
       "929           1.0         0.0         0.0                0.0            0.0   \n",
       "930           0.0         0.0         0.0                1.0            0.0   \n",
       "931           0.0         0.0         0.0                0.0            0.0   \n",
       "\n",
       "     Coating_PEG_based  Coating_PEI  Coating_PVA  Coating_PVP  \\\n",
       "0                  0.0          0.0          0.0          0.0   \n",
       "1                  0.0          0.0          0.0          0.0   \n",
       "2                  0.0          0.0          0.0          0.0   \n",
       "3                  0.0          0.0          0.0          0.0   \n",
       "4                  0.0          0.0          0.0          0.0   \n",
       "..                 ...          ...          ...          ...   \n",
       "927                0.0          0.0          0.0          0.0   \n",
       "928                0.0          0.0          0.0          0.0   \n",
       "929                0.0          0.0          0.0          0.0   \n",
       "930                0.0          0.0          0.0          0.0   \n",
       "931                0.0          0.0          0.0          0.0   \n",
       "\n",
       "     Coating_Phosphonate  Coating_Poloxamer  Coating_SC  Coating_silica_based  \\\n",
       "0                    0.0                0.0         0.0                   0.0   \n",
       "1                    0.0                0.0         0.0                   0.0   \n",
       "2                    0.0                0.0         0.0                   0.0   \n",
       "3                    0.0                0.0         0.0                   0.0   \n",
       "4                    0.0                0.0         0.0                   0.0   \n",
       "..                   ...                ...         ...                   ...   \n",
       "927                  0.0                0.0         0.0                   0.0   \n",
       "928                  0.0                0.0         0.0                   0.0   \n",
       "929                  0.0                0.0         0.0                   0.0   \n",
       "930                  0.0                0.0         0.0                   0.0   \n",
       "931                  0.0                0.0         0.0                   0.0   \n",
       "\n",
       "     Coating_simethicone then esters on top  Coating_sodium_based  \\\n",
       "0                                       0.0                   0.0   \n",
       "1                                       0.0                   0.0   \n",
       "2                                       0.0                   0.0   \n",
       "3                                       0.0                   0.0   \n",
       "4                                       0.0                   0.0   \n",
       "..                                      ...                   ...   \n",
       "927                                     0.0                   0.0   \n",
       "928                                     0.0                   0.0   \n",
       "929                                     0.0                   0.0   \n",
       "930                                     0.0                   0.0   \n",
       "931                                     0.0                   0.0   \n",
       "\n",
       "     shape_Cubic  shape_Hexagon  shape_Irregular  shape_Monoclinic  \\\n",
       "0            0.0            0.0              0.0               0.0   \n",
       "1            0.0            0.0              0.0               0.0   \n",
       "2            0.0            0.0              0.0               0.0   \n",
       "3            0.0            0.0              0.0               0.0   \n",
       "4            0.0            1.0              0.0               0.0   \n",
       "..           ...            ...              ...               ...   \n",
       "927          0.0            0.0              0.0               0.0   \n",
       "928          0.0            0.0              0.0               0.0   \n",
       "929          0.0            0.0              0.0               0.0   \n",
       "930          0.0            0.0              0.0               0.0   \n",
       "931          0.0            0.0              0.0               0.0   \n",
       "\n",
       "     shape_Needle  shape_Orthorhombic  shape_Rhombohedral  shape_Rod  \\\n",
       "0             0.0                 0.0                 0.0        0.0   \n",
       "1             0.0                 0.0                 0.0        0.0   \n",
       "2             0.0                 0.0                 0.0        0.0   \n",
       "3             0.0                 0.0                 0.0        0.0   \n",
       "4             0.0                 0.0                 0.0        0.0   \n",
       "..            ...                 ...                 ...        ...   \n",
       "927           0.0                 0.0                 0.0        0.0   \n",
       "928           0.0                 0.0                 0.0        0.0   \n",
       "929           0.0                 0.0                 0.0        0.0   \n",
       "930           0.0                 0.0                 0.0        0.0   \n",
       "931           0.0                 0.0                 0.0        0.0   \n",
       "\n",
       "     shape_Sphere  shape_Star  shape_Tetragonal  shape_Wire  \\\n",
       "0             1.0         0.0               0.0         0.0   \n",
       "1             1.0         0.0               0.0         0.0   \n",
       "2             0.0         0.0               0.0         0.0   \n",
       "3             1.0         0.0               0.0         0.0   \n",
       "4             0.0         0.0               0.0         0.0   \n",
       "..            ...         ...               ...         ...   \n",
       "927           1.0         0.0               0.0         0.0   \n",
       "928           1.0         0.0               0.0         0.0   \n",
       "929           1.0         0.0               0.0         0.0   \n",
       "930           1.0         0.0               0.0         0.0   \n",
       "931           1.0         0.0               0.0         0.0   \n",
       "\n",
       "     shape_not_available  Cell_name_16HBE14  Cell_name_3T3_L1  \\\n",
       "0                    0.0                0.0               0.0   \n",
       "1                    0.0                0.0               0.0   \n",
       "2                    1.0                0.0               0.0   \n",
       "3                    0.0                0.0               0.0   \n",
       "4                    0.0                0.0               0.0   \n",
       "..                   ...                ...               ...   \n",
       "927                  0.0                0.0               0.0   \n",
       "928                  0.0                0.0               0.0   \n",
       "929                  0.0                0.0               0.0   \n",
       "930                  0.0                0.0               0.0   \n",
       "931                  0.0                0.0               0.0   \n",
       "\n",
       "     Cell_name_A2780  Cell_name_A431  Cell_name_A549  Cell_name_ASM  \\\n",
       "0                0.0             0.0             0.0            0.0   \n",
       "1                0.0             0.0             0.0            0.0   \n",
       "2                0.0             0.0             0.0            0.0   \n",
       "3                0.0             0.0             0.0            0.0   \n",
       "4                0.0             0.0             0.0            0.0   \n",
       "..               ...             ...             ...            ...   \n",
       "927              0.0             0.0             1.0            0.0   \n",
       "928              0.0             0.0             0.0            0.0   \n",
       "929              0.0             0.0             0.0            0.0   \n",
       "930              0.0             0.0             0.0            0.0   \n",
       "931              0.0             0.0             0.0            0.0   \n",
       "\n",
       "     Cell_name_AT2  Cell_name_BALB_c3T3  Cell_name_BEAS_2B  Cell_name_BEC  \\\n",
       "0              0.0                  0.0                0.0            0.0   \n",
       "1              0.0                  0.0                0.0            0.0   \n",
       "2              0.0                  0.0                0.0            0.0   \n",
       "3              0.0                  0.0                0.0            0.0   \n",
       "4              0.0                  0.0                0.0            0.0   \n",
       "..             ...                  ...                ...            ...   \n",
       "927            0.0                  0.0                0.0            0.0   \n",
       "928            0.0                  0.0                0.0            0.0   \n",
       "929            0.0                  0.0                0.0            0.0   \n",
       "930            0.0                  0.0                0.0            0.0   \n",
       "931            0.0                  0.0                0.0            0.0   \n",
       "\n",
       "     Cell_name_BJ  Cell_name_C17_2  Cell_name_C3A  Cell_name_C6  \\\n",
       "0             0.0              0.0            0.0           0.0   \n",
       "1             0.0              0.0            0.0           0.0   \n",
       "2             0.0              0.0            0.0           0.0   \n",
       "3             0.0              0.0            0.0           0.0   \n",
       "4             0.0              0.0            0.0           0.0   \n",
       "..            ...              ...            ...           ...   \n",
       "927           0.0              0.0            0.0           0.0   \n",
       "928           0.0              0.0            0.0           0.0   \n",
       "929           0.0              0.0            0.0           0.0   \n",
       "930           0.0              0.0            0.0           0.0   \n",
       "931           0.0              0.0            0.0           0.0   \n",
       "\n",
       "     Cell_name_CCL-110  Cell_name_Caco_2  Cell_name_Calu-3  \\\n",
       "0                  0.0               0.0               0.0   \n",
       "1                  0.0               0.0               0.0   \n",
       "2                  0.0               0.0               0.0   \n",
       "3                  0.0               0.0               0.0   \n",
       "4                  0.0               0.0               0.0   \n",
       "..                 ...               ...               ...   \n",
       "927                0.0               0.0               0.0   \n",
       "928                0.0               1.0               0.0   \n",
       "929                0.0               1.0               0.0   \n",
       "930                0.0               0.0               0.0   \n",
       "931                0.0               0.0               0.0   \n",
       "\n",
       "     Cell_name_Chang_Liver  Cell_name_Colo­205  Cell_name_HCMEC  \\\n",
       "0                      0.0                 0.0              0.0   \n",
       "1                      0.0                 0.0              1.0   \n",
       "2                      0.0                 0.0              0.0   \n",
       "3                      0.0                 0.0              0.0   \n",
       "4                      0.0                 0.0              0.0   \n",
       "..                     ...                 ...              ...   \n",
       "927                    0.0                 0.0              0.0   \n",
       "928                    0.0                 0.0              0.0   \n",
       "929                    0.0                 0.0              0.0   \n",
       "930                    0.0                 0.0              0.0   \n",
       "931                    0.0                 0.0              0.0   \n",
       "\n",
       "     Cell_name_HCT-116  Cell_name_HDF  Cell_name_HEBC3-KT  Cell_name_HEK  \\\n",
       "0                  0.0            0.0                 0.0            0.0   \n",
       "1                  0.0            0.0                 0.0            0.0   \n",
       "2                  0.0            0.0                 0.0            0.0   \n",
       "3                  0.0            0.0                 0.0            0.0   \n",
       "4                  0.0            0.0                 0.0            0.0   \n",
       "..                 ...            ...                 ...            ...   \n",
       "927                0.0            0.0                 0.0            0.0   \n",
       "928                0.0            0.0                 0.0            0.0   \n",
       "929                0.0            0.0                 0.0            0.0   \n",
       "930                0.0            0.0                 0.0            0.0   \n",
       "931                0.0            0.0                 0.0            0.0   \n",
       "\n",
       "     Cell_name_HEK293  Cell_name_HFL1  Cell_name_HK-2  Cell_name_HT-1080  \\\n",
       "0                 0.0             0.0             0.0                0.0   \n",
       "1                 0.0             0.0             0.0                0.0   \n",
       "2                 0.0             0.0             0.0                0.0   \n",
       "3                 0.0             0.0             0.0                0.0   \n",
       "4                 0.0             0.0             0.0                0.0   \n",
       "..                ...             ...             ...                ...   \n",
       "927               0.0             0.0             0.0                0.0   \n",
       "928               0.0             0.0             0.0                0.0   \n",
       "929               0.0             0.0             0.0                0.0   \n",
       "930               0.0             0.0             0.0                0.0   \n",
       "931               0.0             0.0             0.0                0.0   \n",
       "\n",
       "     Cell_name_HT29  Cell_name_HaCat  Cell_name_HeLa  Cell_name_HepG2  \\\n",
       "0               0.0              0.0             0.0              0.0   \n",
       "1               0.0              0.0             0.0              0.0   \n",
       "2               0.0              0.0             0.0              0.0   \n",
       "3               0.0              0.0             0.0              0.0   \n",
       "4               0.0              0.0             0.0              0.0   \n",
       "..              ...              ...             ...              ...   \n",
       "927             0.0              0.0             0.0              0.0   \n",
       "928             0.0              0.0             0.0              0.0   \n",
       "929             0.0              0.0             0.0              0.0   \n",
       "930             0.0              0.0             0.0              0.0   \n",
       "931             0.0              0.0             0.0              0.0   \n",
       "\n",
       "     Cell_name_IMR90  Cell_name_J774A.1  Cell_name_Jurkat  Cell_name_K562  \\\n",
       "0                0.0                0.0               0.0             0.0   \n",
       "1                0.0                0.0               0.0             0.0   \n",
       "2                0.0                0.0               0.0             0.0   \n",
       "3                0.0                0.0               0.0             0.0   \n",
       "4                0.0                0.0               0.0             0.0   \n",
       "..               ...                ...               ...             ...   \n",
       "927              0.0                0.0               0.0             0.0   \n",
       "928              0.0                0.0               0.0             0.0   \n",
       "929              0.0                0.0               0.0             0.0   \n",
       "930              0.0                0.0               0.0             0.0   \n",
       "931              0.0                0.0               0.0             0.0   \n",
       "\n",
       "     Cell_name_KEC  Cell_name_L-02  Cell_name_L-132  Cell_name_L929  \\\n",
       "0              0.0             0.0              0.0             0.0   \n",
       "1              0.0             0.0              0.0             0.0   \n",
       "2              0.0             0.0              0.0             0.0   \n",
       "3              0.0             0.0              0.0             0.0   \n",
       "4              0.0             0.0              0.0             0.0   \n",
       "..             ...             ...              ...             ...   \n",
       "927            0.0             0.0              0.0             0.0   \n",
       "928            0.0             0.0              0.0             0.0   \n",
       "929            0.0             0.0              0.0             0.0   \n",
       "930            0.0             0.0              0.0             0.0   \n",
       "931            0.0             0.0              0.0             0.0   \n",
       "\n",
       "     Cell_name_MBMC  Cell_name_MCF-10A  Cell_name_MCF7  Cell_name_Macrophages  \\\n",
       "0               0.0                0.0             0.0                    0.0   \n",
       "1               0.0                0.0             0.0                    0.0   \n",
       "2               0.0                0.0             0.0                    0.0   \n",
       "3               0.0                0.0             0.0                    0.0   \n",
       "4               0.0                0.0             0.0                    0.0   \n",
       "..              ...                ...             ...                    ...   \n",
       "927             0.0                0.0             0.0                    0.0   \n",
       "928             0.0                0.0             0.0                    0.0   \n",
       "929             0.0                0.0             0.0                    0.0   \n",
       "930             0.0                0.0             0.0                    0.0   \n",
       "931             0.0                0.0             0.0                    0.0   \n",
       "\n",
       "     Cell_name_Monocytes  Cell_name_NHDF  Cell_name_NIH3T3  Cell_name_NR8383  \\\n",
       "0                    0.0             0.0               0.0               0.0   \n",
       "1                    0.0             0.0               0.0               0.0   \n",
       "2                    0.0             0.0               0.0               0.0   \n",
       "3                    0.0             0.0               0.0               0.0   \n",
       "4                    0.0             0.0               0.0               0.0   \n",
       "..                   ...             ...               ...               ...   \n",
       "927                  0.0             0.0               0.0               0.0   \n",
       "928                  0.0             0.0               0.0               0.0   \n",
       "929                  0.0             0.0               0.0               0.0   \n",
       "930                  0.0             0.0               0.0               0.0   \n",
       "931                  1.0             0.0               0.0               0.0   \n",
       "\n",
       "     Cell_name_PBMC  Cell_name_RAW 264.7  Cell_name_SH­SY5Y  Cell_name_SKOV­3  \\\n",
       "0               0.0                  0.0                1.0               0.0   \n",
       "1               0.0                  0.0                0.0               0.0   \n",
       "2               0.0                  0.0                0.0               1.0   \n",
       "3               0.0                  0.0                1.0               0.0   \n",
       "4               0.0                  0.0                0.0               0.0   \n",
       "..              ...                  ...                ...               ...   \n",
       "927             0.0                  0.0                0.0               0.0   \n",
       "928             0.0                  0.0                0.0               0.0   \n",
       "929             0.0                  0.0                0.0               0.0   \n",
       "930             0.0                  0.0                1.0               0.0   \n",
       "931             0.0                  0.0                0.0               0.0   \n",
       "\n",
       "     Cell_name_SV-80  Cell_name_SW480  Cell_name_THP_1  Cell_name_TT1  \\\n",
       "0                0.0              0.0              0.0            0.0   \n",
       "1                0.0              0.0              0.0            0.0   \n",
       "2                0.0              0.0              0.0            0.0   \n",
       "3                0.0              0.0              0.0            0.0   \n",
       "4                0.0              0.0              0.0            0.0   \n",
       "..               ...              ...              ...            ...   \n",
       "927              0.0              0.0              0.0            0.0   \n",
       "928              0.0              0.0              0.0            0.0   \n",
       "929              0.0              0.0              0.0            0.0   \n",
       "930              0.0              0.0              0.0            0.0   \n",
       "931              0.0              0.0              0.0            0.0   \n",
       "\n",
       "     Cell_name_U87  Cell_name_V79  Cell_name_VERO  Cell_name_WA09  \\\n",
       "0              0.0            0.0             0.0             0.0   \n",
       "1              0.0            0.0             0.0             0.0   \n",
       "2              0.0            0.0             0.0             0.0   \n",
       "3              0.0            0.0             0.0             0.0   \n",
       "4              0.0            0.0             1.0             0.0   \n",
       "..             ...            ...             ...             ...   \n",
       "927            0.0            0.0             0.0             0.0   \n",
       "928            0.0            0.0             0.0             0.0   \n",
       "929            0.0            0.0             0.0             0.0   \n",
       "930            0.0            0.0             0.0             0.0   \n",
       "931            0.0            0.0             0.0             0.0   \n",
       "\n",
       "     Cell_name_WI_38  Cell_name_hNPC  Cell_name_hPDLF  Cell_name_mDF  \\\n",
       "0                0.0             0.0              0.0            0.0   \n",
       "1                0.0             0.0              0.0            0.0   \n",
       "2                0.0             0.0              0.0            0.0   \n",
       "3                0.0             0.0              0.0            0.0   \n",
       "4                0.0             0.0              0.0            0.0   \n",
       "..               ...             ...              ...            ...   \n",
       "927              0.0             0.0              0.0            0.0   \n",
       "928              0.0             0.0              0.0            0.0   \n",
       "929              0.0             0.0              0.0            0.0   \n",
       "930              0.0             0.0              0.0            0.0   \n",
       "931              0.0             0.0              0.0            0.0   \n",
       "\n",
       "     Cell_name_primary  Cell_species_Cow  Cell_species_Dog  \\\n",
       "0                  0.0               0.0               0.0   \n",
       "1                  0.0               0.0               0.0   \n",
       "2                  0.0               0.0               0.0   \n",
       "3                  0.0               0.0               0.0   \n",
       "4                  0.0               0.0               0.0   \n",
       "..                 ...               ...               ...   \n",
       "927                0.0               0.0               0.0   \n",
       "928                0.0               0.0               0.0   \n",
       "929                0.0               0.0               0.0   \n",
       "930                0.0               0.0               0.0   \n",
       "931                0.0               0.0               0.0   \n",
       "\n",
       "     Cell_species_Human  Cell_species_Mouse  Cell_species_Rabbit  \\\n",
       "0                   1.0                 0.0                  0.0   \n",
       "1                   1.0                 0.0                  0.0   \n",
       "2                   1.0                 0.0                  0.0   \n",
       "3                   1.0                 0.0                  0.0   \n",
       "4                   0.0                 0.0                  0.0   \n",
       "..                  ...                 ...                  ...   \n",
       "927                 1.0                 0.0                  0.0   \n",
       "928                 1.0                 0.0                  0.0   \n",
       "929                 1.0                 0.0                  0.0   \n",
       "930                 1.0                 0.0                  0.0   \n",
       "931                 1.0                 0.0                  0.0   \n",
       "\n",
       "     Cell_species_Rat  cell_Organ_Adrenal-gland  cell_Organ_Amniotic-membrane  \\\n",
       "0                 0.0                       0.0                           0.0   \n",
       "1                 0.0                       0.0                           0.0   \n",
       "2                 0.0                       0.0                           0.0   \n",
       "3                 0.0                       0.0                           0.0   \n",
       "4                 0.0                       0.0                           0.0   \n",
       "..                ...                       ...                           ...   \n",
       "927               0.0                       0.0                           0.0   \n",
       "928               0.0                       0.0                           0.0   \n",
       "929               0.0                       0.0                           0.0   \n",
       "930               0.0                       0.0                           0.0   \n",
       "931               0.0                       0.0                           0.0   \n",
       "\n",
       "     cell_Organ_Blood  cell_Organ_BoneMarrow  cell_Organ_Brain  \\\n",
       "0                 0.0                    1.0               0.0   \n",
       "1                 0.0                    0.0               0.0   \n",
       "2                 0.0                    0.0               0.0   \n",
       "3                 0.0                    1.0               0.0   \n",
       "4                 0.0                    0.0               0.0   \n",
       "..                ...                    ...               ...   \n",
       "927               0.0                    0.0               0.0   \n",
       "928               0.0                    0.0               0.0   \n",
       "929               0.0                    0.0               0.0   \n",
       "930               0.0                    1.0               0.0   \n",
       "931               1.0                    0.0               0.0   \n",
       "\n",
       "     cell_Organ_Breast  cell_Organ_Colon  cell_Organ_Connective-tissue  \\\n",
       "0                  0.0               0.0                           0.0   \n",
       "1                  0.0               0.0                           0.0   \n",
       "2                  0.0               0.0                           0.0   \n",
       "3                  0.0               0.0                           0.0   \n",
       "4                  0.0               0.0                           0.0   \n",
       "..                 ...               ...                           ...   \n",
       "927                0.0               0.0                           0.0   \n",
       "928                0.0               1.0                           0.0   \n",
       "929                0.0               1.0                           0.0   \n",
       "930                0.0               0.0                           0.0   \n",
       "931                0.0               0.0                           0.0   \n",
       "\n",
       "     cell_Organ_Cortex  cell_Organ_Embryo  cell_Organ_FatPad  \\\n",
       "0                  0.0                0.0                0.0   \n",
       "1                  0.0                0.0                0.0   \n",
       "2                  0.0                0.0                0.0   \n",
       "3                  0.0                0.0                0.0   \n",
       "4                  0.0                0.0                0.0   \n",
       "..                 ...                ...                ...   \n",
       "927                0.0                0.0                0.0   \n",
       "928                0.0                0.0                0.0   \n",
       "929                0.0                0.0                0.0   \n",
       "930                0.0                0.0                0.0   \n",
       "931                0.0                0.0                0.0   \n",
       "\n",
       "     cell_Organ_Heart  cell_Organ_Kidney  cell_Organ_Liver  cell_Organ_Lung  \\\n",
       "0                 0.0                0.0               0.0              0.0   \n",
       "1                 1.0                0.0               0.0              0.0   \n",
       "2                 0.0                0.0               0.0              0.0   \n",
       "3                 0.0                0.0               0.0              0.0   \n",
       "4                 0.0                1.0               0.0              0.0   \n",
       "..                ...                ...               ...              ...   \n",
       "927               0.0                0.0               0.0              1.0   \n",
       "928               0.0                0.0               0.0              0.0   \n",
       "929               0.0                0.0               0.0              0.0   \n",
       "930               0.0                0.0               0.0              0.0   \n",
       "931               0.0                0.0               0.0              0.0   \n",
       "\n",
       "     cell_Organ_Mesothelium  cell_Organ_Ovary  cell_Organ_Peritoneal Cavity  \\\n",
       "0                       0.0               0.0                           0.0   \n",
       "1                       0.0               0.0                           0.0   \n",
       "2                       0.0               1.0                           0.0   \n",
       "3                       0.0               0.0                           0.0   \n",
       "4                       0.0               0.0                           0.0   \n",
       "..                      ...               ...                           ...   \n",
       "927                     0.0               0.0                           0.0   \n",
       "928                     0.0               0.0                           0.0   \n",
       "929                     0.0               0.0                           0.0   \n",
       "930                     0.0               0.0                           0.0   \n",
       "931                     0.0               0.0                           0.0   \n",
       "\n",
       "     cell_Organ_Skin  cell_Organ_Stomach  cell_Organ_Umbilical  \\\n",
       "0                0.0                 0.0                   0.0   \n",
       "1                0.0                 0.0                   0.0   \n",
       "2                0.0                 0.0                   0.0   \n",
       "3                0.0                 0.0                   0.0   \n",
       "4                0.0                 0.0                   0.0   \n",
       "..               ...                 ...                   ...   \n",
       "927              0.0                 0.0                   0.0   \n",
       "928              0.0                 0.0                   0.0   \n",
       "929              0.0                 0.0                   0.0   \n",
       "930              0.0                 0.0                   0.0   \n",
       "931              0.0                 0.0                   0.0   \n",
       "\n",
       "     Cell_morphology_Endothelial  Cell_morphology_Epithelial  \\\n",
       "0                            0.0                         1.0   \n",
       "1                            1.0                         0.0   \n",
       "2                            0.0                         1.0   \n",
       "3                            0.0                         1.0   \n",
       "4                            0.0                         1.0   \n",
       "..                           ...                         ...   \n",
       "927                          0.0                         1.0   \n",
       "928                          0.0                         1.0   \n",
       "929                          0.0                         1.0   \n",
       "930                          0.0                         1.0   \n",
       "931                          0.0                         0.0   \n",
       "\n",
       "     Cell_morphology_Fibroblast  Cell_morphology_Keratinocyte  \\\n",
       "0                           0.0                           0.0   \n",
       "1                           0.0                           0.0   \n",
       "2                           0.0                           0.0   \n",
       "3                           0.0                           0.0   \n",
       "4                           0.0                           0.0   \n",
       "..                          ...                           ...   \n",
       "927                         0.0                           0.0   \n",
       "928                         0.0                           0.0   \n",
       "929                         0.0                           0.0   \n",
       "930                         0.0                           0.0   \n",
       "931                         0.0                           0.0   \n",
       "\n",
       "     Cell_morphology_Lymphoblast  Cell_morphology_Macrophage  \\\n",
       "0                            0.0                         0.0   \n",
       "1                            0.0                         0.0   \n",
       "2                            0.0                         0.0   \n",
       "3                            0.0                         0.0   \n",
       "4                            0.0                         0.0   \n",
       "..                           ...                         ...   \n",
       "927                          0.0                         0.0   \n",
       "928                          0.0                         0.0   \n",
       "929                          0.0                         0.0   \n",
       "930                          0.0                         0.0   \n",
       "931                          0.0                         0.0   \n",
       "\n",
       "     Cell_morphology_Mesenchymal  Cell_morphology_Mixed  \\\n",
       "0                            0.0                    0.0   \n",
       "1                            0.0                    0.0   \n",
       "2                            0.0                    0.0   \n",
       "3                            0.0                    0.0   \n",
       "4                            0.0                    0.0   \n",
       "..                           ...                    ...   \n",
       "927                          0.0                    0.0   \n",
       "928                          0.0                    0.0   \n",
       "929                          0.0                    0.0   \n",
       "930                          0.0                    0.0   \n",
       "931                          0.0                    0.0   \n",
       "\n",
       "     Cell_morphology_Monocyte  Cell_morphology_MulticellularClusters  \\\n",
       "0                         0.0                                    0.0   \n",
       "1                         0.0                                    0.0   \n",
       "2                         0.0                                    0.0   \n",
       "3                         0.0                                    0.0   \n",
       "4                         0.0                                    0.0   \n",
       "..                        ...                                    ...   \n",
       "927                       0.0                                    0.0   \n",
       "928                       0.0                                    0.0   \n",
       "929                       0.0                                    0.0   \n",
       "930                       0.0                                    0.0   \n",
       "931                       1.0                                    0.0   \n",
       "\n",
       "     Cell_morphology_Neural  Cell_morphology_Preosteoblast  \\\n",
       "0                       0.0                            0.0   \n",
       "1                       0.0                            0.0   \n",
       "2                       0.0                            0.0   \n",
       "3                       0.0                            0.0   \n",
       "4                       0.0                            0.0   \n",
       "..                      ...                            ...   \n",
       "927                     0.0                            0.0   \n",
       "928                     0.0                            0.0   \n",
       "929                     0.0                            0.0   \n",
       "930                     0.0                            0.0   \n",
       "931                     0.0                            0.0   \n",
       "\n",
       "     Cell_morphology_Spindle  Cell_morphology_monocyte  Cell_age_Adult  \\\n",
       "0                        0.0                       0.0             0.0   \n",
       "1                        0.0                       0.0             1.0   \n",
       "2                        0.0                       0.0             0.0   \n",
       "3                        0.0                       0.0             0.0   \n",
       "4                        0.0                       0.0             1.0   \n",
       "..                       ...                       ...             ...   \n",
       "927                      0.0                       0.0             1.0   \n",
       "928                      0.0                       0.0             1.0   \n",
       "929                      0.0                       0.0             1.0   \n",
       "930                      0.0                       0.0             0.0   \n",
       "931                      0.0                       0.0             1.0   \n",
       "\n",
       "     Cell_age_Young  Cell_age_fetus  Cell_age_neonate  Cell_age_not_available  \\\n",
       "0               1.0             0.0               0.0                     0.0   \n",
       "1               0.0             0.0               0.0                     0.0   \n",
       "2               1.0             0.0               0.0                     0.0   \n",
       "3               1.0             0.0               0.0                     0.0   \n",
       "4               0.0             0.0               0.0                     0.0   \n",
       "..              ...             ...               ...                     ...   \n",
       "927             0.0             0.0               0.0                     0.0   \n",
       "928             0.0             0.0               0.0                     0.0   \n",
       "929             0.0             0.0               0.0                     0.0   \n",
       "930             1.0             0.0               0.0                     0.0   \n",
       "931             0.0             0.0               0.0                     0.0   \n",
       "\n",
       "     cell_type_Normal  cell_type_cancer  sex_female  sex_male  \\\n",
       "0                 0.0               1.0         1.0       0.0   \n",
       "1                 1.0               0.0         0.0       0.0   \n",
       "2                 0.0               1.0         1.0       0.0   \n",
       "3                 0.0               1.0         1.0       0.0   \n",
       "4                 1.0               0.0         1.0       0.0   \n",
       "..                ...               ...         ...       ...   \n",
       "927               0.0               1.0         0.0       1.0   \n",
       "928               0.0               1.0         0.0       1.0   \n",
       "929               0.0               1.0         0.0       1.0   \n",
       "930               0.0               1.0         1.0       0.0   \n",
       "931               1.0               0.0         0.0       0.0   \n",
       "\n",
       "     sex_not_available  Assay_ATPLite  Assay_Alamar_Blue  Assay_Annexiv_V_  \\\n",
       "0                  0.0            0.0                0.0               0.0   \n",
       "1                  1.0            0.0                0.0               0.0   \n",
       "2                  0.0            0.0                0.0               0.0   \n",
       "3                  0.0            0.0                0.0               0.0   \n",
       "4                  0.0            0.0                0.0               0.0   \n",
       "..                 ...            ...                ...               ...   \n",
       "927                0.0            0.0                0.0               0.0   \n",
       "928                0.0            0.0                0.0               0.0   \n",
       "929                0.0            0.0                0.0               0.0   \n",
       "930                0.0            0.0                0.0               0.0   \n",
       "931                1.0            0.0                0.0               0.0   \n",
       "\n",
       "     Assay_CCK_8  Assay_CellTiterBlue  Assay_CellTiter_Glo  Assay_CytoTox-Glo  \\\n",
       "0            0.0                  0.0                  0.0                0.0   \n",
       "1            0.0                  0.0                  0.0                0.0   \n",
       "2            0.0                  0.0                  0.0                0.0   \n",
       "3            0.0                  0.0                  0.0                0.0   \n",
       "4            0.0                  0.0                  0.0                0.0   \n",
       "..           ...                  ...                  ...                ...   \n",
       "927          1.0                  0.0                  0.0                0.0   \n",
       "928          0.0                  0.0                  0.0                0.0   \n",
       "929          0.0                  0.0                  0.0                0.0   \n",
       "930          0.0                  0.0                  0.0                0.0   \n",
       "931          1.0                  0.0                  0.0                0.0   \n",
       "\n",
       "     Assay_EZ4U  Assay_LDH  Assay_Live/Dead  Assay_MTS  Assay_MTT  \\\n",
       "0           0.0        0.0              0.0        0.0        0.0   \n",
       "1           0.0        0.0              0.0        0.0        1.0   \n",
       "2           0.0        0.0              0.0        0.0        1.0   \n",
       "3           0.0        0.0              0.0        0.0        0.0   \n",
       "4           0.0        0.0              0.0        0.0        1.0   \n",
       "..          ...        ...              ...        ...        ...   \n",
       "927         0.0        0.0              0.0        0.0        0.0   \n",
       "928         0.0        0.0              0.0        0.0        1.0   \n",
       "929         0.0        0.0              0.0        0.0        0.0   \n",
       "930         0.0        0.0              0.0        0.0        0.0   \n",
       "931         0.0        0.0              0.0        0.0        0.0   \n",
       "\n",
       "     Assay_Promegas CTB Assay  Assay_Vialight  Assay_WST-1  Assay_XTT  \\\n",
       "0                         0.0             0.0          0.0        0.0   \n",
       "1                         0.0             0.0          0.0        0.0   \n",
       "2                         0.0             0.0          0.0        0.0   \n",
       "3                         0.0             0.0          0.0        0.0   \n",
       "4                         0.0             0.0          0.0        0.0   \n",
       "..                        ...             ...          ...        ...   \n",
       "927                       0.0             0.0          0.0        0.0   \n",
       "928                       0.0             0.0          0.0        0.0   \n",
       "929                       0.0             0.0          0.0        0.0   \n",
       "930                       0.0             0.0          0.0        0.0   \n",
       "931                       0.0             0.0          0.0        0.0   \n",
       "\n",
       "     Test_indicator_AlamarBlue  Test_indicator_Annexin V  \\\n",
       "0                          0.0                       0.0   \n",
       "1                          0.0                       0.0   \n",
       "2                          0.0                       0.0   \n",
       "3                          0.0                       0.0   \n",
       "4                          0.0                       0.0   \n",
       "..                         ...                       ...   \n",
       "927                        0.0                       0.0   \n",
       "928                        0.0                       0.0   \n",
       "929                        0.0                       0.0   \n",
       "930                        0.0                       0.0   \n",
       "931                        0.0                       0.0   \n",
       "\n",
       "     Test_indicator_CalceinAM  Test_indicator_Impedance  \\\n",
       "0                         0.0                       0.0   \n",
       "1                         0.0                       0.0   \n",
       "2                         0.0                       0.0   \n",
       "3                         0.0                       0.0   \n",
       "4                         0.0                       0.0   \n",
       "..                        ...                       ...   \n",
       "927                       0.0                       0.0   \n",
       "928                       0.0                       0.0   \n",
       "929                       0.0                       1.0   \n",
       "930                       0.0                       0.0   \n",
       "931                       0.0                       0.0   \n",
       "\n",
       "     Test_indicator_Sytox Red  Test_indicator_TetrazoliumSalt  \\\n",
       "0                         0.0                             0.0   \n",
       "1                         0.0                             1.0   \n",
       "2                         0.0                             1.0   \n",
       "3                         0.0                             0.0   \n",
       "4                         0.0                             1.0   \n",
       "..                        ...                             ...   \n",
       "927                       0.0                             1.0   \n",
       "928                       0.0                             1.0   \n",
       "929                       0.0                             0.0   \n",
       "930                       0.0                             0.0   \n",
       "931                       0.0                             1.0   \n",
       "\n",
       "     Test_indicator_Trypan_Blue  Test_indicator_luciferase  \\\n",
       "0                           0.0                        0.0   \n",
       "1                           0.0                        0.0   \n",
       "2                           0.0                        0.0   \n",
       "3                           0.0                        0.0   \n",
       "4                           0.0                        0.0   \n",
       "..                          ...                        ...   \n",
       "927                         0.0                        0.0   \n",
       "928                         0.0                        0.0   \n",
       "929                         0.0                        0.0   \n",
       "930                         0.0                        0.0   \n",
       "931                         0.0                        0.0   \n",
       "\n",
       "     Test_indicator_not_available  Test_indicator_propidium_iodide  \\\n",
       "0                             0.0                              0.0   \n",
       "1                             0.0                              0.0   \n",
       "2                             0.0                              0.0   \n",
       "3                             0.0                              0.0   \n",
       "4                             0.0                              0.0   \n",
       "..                            ...                              ...   \n",
       "927                           0.0                              0.0   \n",
       "928                           0.0                              0.0   \n",
       "929                           0.0                              0.0   \n",
       "930                           0.0                              0.0   \n",
       "931                           0.0                              0.0   \n",
       "\n",
       "     Test_indicator_toluylene red  nanomaterial_group_carbon  \\\n",
       "0                             1.0                        0.0   \n",
       "1                             0.0                        0.0   \n",
       "2                             0.0                        0.0   \n",
       "3                             1.0                        0.0   \n",
       "4                             0.0                        0.0   \n",
       "..                            ...                        ...   \n",
       "927                           0.0                        0.0   \n",
       "928                           0.0                        0.0   \n",
       "929                           0.0                        0.0   \n",
       "930                           1.0                        0.0   \n",
       "931                           0.0                        0.0   \n",
       "\n",
       "     nanomaterial_group_meta_oxide  nanomaterial_group_nanotubes  \\\n",
       "0                              1.0                           0.0   \n",
       "1                              1.0                           0.0   \n",
       "2                              1.0                           0.0   \n",
       "3                              1.0                           0.0   \n",
       "4                              1.0                           0.0   \n",
       "..                             ...                           ...   \n",
       "927                            1.0                           0.0   \n",
       "928                            1.0                           0.0   \n",
       "929                            0.0                           0.0   \n",
       "930                            1.0                           0.0   \n",
       "931                            1.0                           0.0   \n",
       "\n",
       "     nanomaterial_group_other  nanomaterial_group_quantum_dots   Viability  \\\n",
       "0                         0.0                              0.0  104.589996   \n",
       "1                         0.0                              0.0   93.769997   \n",
       "2                         0.0                              0.0   91.919998   \n",
       "3                         0.0                              0.0   49.349998   \n",
       "4                         0.0                              0.0   70.959999   \n",
       "..                        ...                              ...         ...   \n",
       "927                       0.0                              0.0   84.239998   \n",
       "928                       0.0                              0.0   64.709999   \n",
       "929                       1.0                              0.0   92.559998   \n",
       "930                       0.0                              0.0   81.739998   \n",
       "931                       0.0                              0.0   99.000000   \n",
       "\n",
       "          Label  \n",
       "0    100.942000  \n",
       "1     95.888400  \n",
       "2     85.453999  \n",
       "3     50.965199  \n",
       "4     61.083701  \n",
       "..          ...  \n",
       "927   49.079599  \n",
       "928   70.387299  \n",
       "929  108.646101  \n",
       "930  111.144798  \n",
       "931  109.234002  \n",
       "\n",
       "[932 rows x 251 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict_model(final_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "5195d56e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table id=\"T_d43bf\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_d43bf_level0_col0\" class=\"col_heading level0 col0\" >Model</th>\n",
       "      <th id=\"T_d43bf_level0_col1\" class=\"col_heading level0 col1\" >MAE</th>\n",
       "      <th id=\"T_d43bf_level0_col2\" class=\"col_heading level0 col2\" >MSE</th>\n",
       "      <th id=\"T_d43bf_level0_col3\" class=\"col_heading level0 col3\" >RMSE</th>\n",
       "      <th id=\"T_d43bf_level0_col4\" class=\"col_heading level0 col4\" >R2</th>\n",
       "      <th id=\"T_d43bf_level0_col5\" class=\"col_heading level0 col5\" >RMSLE</th>\n",
       "      <th id=\"T_d43bf_level0_col6\" class=\"col_heading level0 col6\" >MAPE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_d43bf_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_d43bf_row0_col0\" class=\"data row0 col0\" >Extra Trees Regressor</td>\n",
       "      <td id=\"T_d43bf_row0_col1\" class=\"data row0 col1\" >27.3208</td>\n",
       "      <td id=\"T_d43bf_row0_col2\" class=\"data row0 col2\" >2561.7611</td>\n",
       "      <td id=\"T_d43bf_row0_col3\" class=\"data row0 col3\" >50.6138</td>\n",
       "      <td id=\"T_d43bf_row0_col4\" class=\"data row0 col4\" >0.0678</td>\n",
       "      <td id=\"T_d43bf_row0_col5\" class=\"data row0 col5\" >1.0915</td>\n",
       "      <td id=\"T_d43bf_row0_col6\" class=\"data row0 col6\" >6.7938</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f1ac251c2b0>"
      ]
     },
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     "output_type": "display_data"
    },
    {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NP_type</th>\n",
       "      <th>Coating</th>\n",
       "      <th>core_size_nm</th>\n",
       "      <th>hydro_size_nm</th>\n",
       "      <th>Surf_charge_mV</th>\n",
       "      <th>Surface_area_m2_g</th>\n",
       "      <th>shape</th>\n",
       "      <th>Dose_microg_mL</th>\n",
       "      <th>Duration_h</th>\n",
       "      <th>Cell_name</th>\n",
       "      <th>Cell_species</th>\n",
       "      <th>cell_Organ</th>\n",
       "      <th>Cell_morphology</th>\n",
       "      <th>Cell_age</th>\n",
       "      <th>cell_type</th>\n",
       "      <th>sex</th>\n",
       "      <th>Assay</th>\n",
       "      <th>Test_indicator</th>\n",
       "      <th>nanomaterial_group</th>\n",
       "      <th>Viability</th>\n",
       "      <th>Label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ag</td>\n",
       "      <td>None</td>\n",
       "      <td>150.0</td>\n",
       "      <td>144.04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sphere</td>\n",
       "      <td>1.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>A549</td>\n",
       "      <td>Human</td>\n",
       "      <td>Lung</td>\n",
       "      <td>Epithelial</td>\n",
       "      <td>Adult</td>\n",
       "      <td>cancer</td>\n",
       "      <td>male</td>\n",
       "      <td>MTT</td>\n",
       "      <td>TetrazoliumSalt</td>\n",
       "      <td>other</td>\n",
       "      <td>80.15</td>\n",
       "      <td>85.999500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ag</td>\n",
       "      <td>None</td>\n",
       "      <td>150.0</td>\n",
       "      <td>145.09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sphere</td>\n",
       "      <td>5.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>A549</td>\n",
       "      <td>Human</td>\n",
       "      <td>Lung</td>\n",
       "      <td>Epithelial</td>\n",
       "      <td>Adult</td>\n",
       "      <td>cancer</td>\n",
       "      <td>male</td>\n",
       "      <td>MTT</td>\n",
       "      <td>TetrazoliumSalt</td>\n",
       "      <td>other</td>\n",
       "      <td>65.05</td>\n",
       "      <td>83.584699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ag</td>\n",
       "      <td>None</td>\n",
       "      <td>150.0</td>\n",
       "      <td>146.40</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sphere</td>\n",
       "      <td>10.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>A549</td>\n",
       "      <td>Human</td>\n",
       "      <td>Lung</td>\n",
       "      <td>Epithelial</td>\n",
       "      <td>Adult</td>\n",
       "      <td>cancer</td>\n",
       "      <td>male</td>\n",
       "      <td>MTT</td>\n",
       "      <td>TetrazoliumSalt</td>\n",
       "      <td>other</td>\n",
       "      <td>64.27</td>\n",
       "      <td>83.799399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ag</td>\n",
       "      <td>None</td>\n",
       "      <td>150.0</td>\n",
       "      <td>149.02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sphere</td>\n",
       "      <td>20.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>A549</td>\n",
       "      <td>Human</td>\n",
       "      <td>Lung</td>\n",
       "      <td>Epithelial</td>\n",
       "      <td>Adult</td>\n",
       "      <td>cancer</td>\n",
       "      <td>male</td>\n",
       "      <td>MTT</td>\n",
       "      <td>TetrazoliumSalt</td>\n",
       "      <td>other</td>\n",
       "      <td>57.01</td>\n",
       "      <td>81.807499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ag</td>\n",
       "      <td>None</td>\n",
       "      <td>150.0</td>\n",
       "      <td>154.27</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sphere</td>\n",
       "      <td>40.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>A549</td>\n",
       "      <td>Human</td>\n",
       "      <td>Lung</td>\n",
       "      <td>Epithelial</td>\n",
       "      <td>Adult</td>\n",
       "      <td>cancer</td>\n",
       "      <td>male</td>\n",
       "      <td>MTT</td>\n",
       "      <td>TetrazoliumSalt</td>\n",
       "      <td>other</td>\n",
       "      <td>35.84</td>\n",
       "      <td>77.189700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>964</th>\n",
       "      <td>Ag</td>\n",
       "      <td>HEC</td>\n",
       "      <td>19.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sphere</td>\n",
       "      <td>100.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>HCT-116</td>\n",
       "      <td>Human</td>\n",
       "      <td>Colon</td>\n",
       "      <td>Epithelial</td>\n",
       "      <td>Adult</td>\n",
       "      <td>cancer</td>\n",
       "      <td>male</td>\n",
       "      <td>WST-1</td>\n",
       "      <td>TetrazoliumSalt</td>\n",
       "      <td>other</td>\n",
       "      <td>95.27</td>\n",
       "      <td>63.947800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>965</th>\n",
       "      <td>Ag</td>\n",
       "      <td>HEC</td>\n",
       "      <td>19.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sphere</td>\n",
       "      <td>100.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>HCT-116</td>\n",
       "      <td>Human</td>\n",
       "      <td>Colon</td>\n",
       "      <td>Epithelial</td>\n",
       "      <td>Adult</td>\n",
       "      <td>cancer</td>\n",
       "      <td>male</td>\n",
       "      <td>WST-1</td>\n",
       "      <td>TetrazoliumSalt</td>\n",
       "      <td>other</td>\n",
       "      <td>92.97</td>\n",
       "      <td>63.947800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>966</th>\n",
       "      <td>Ag</td>\n",
       "      <td>HEC</td>\n",
       "      <td>19.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sphere</td>\n",
       "      <td>100.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>HCT-116</td>\n",
       "      <td>Human</td>\n",
       "      <td>Colon</td>\n",
       "      <td>Epithelial</td>\n",
       "      <td>Adult</td>\n",
       "      <td>cancer</td>\n",
       "      <td>male</td>\n",
       "      <td>WST-1</td>\n",
       "      <td>TetrazoliumSalt</td>\n",
       "      <td>other</td>\n",
       "      <td>63.59</td>\n",
       "      <td>63.947800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>967</th>\n",
       "      <td>Ag</td>\n",
       "      <td>HEC</td>\n",
       "      <td>19.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sphere</td>\n",
       "      <td>100.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>HCT-116</td>\n",
       "      <td>Human</td>\n",
       "      <td>Colon</td>\n",
       "      <td>Epithelial</td>\n",
       "      <td>Adult</td>\n",
       "      <td>cancer</td>\n",
       "      <td>male</td>\n",
       "      <td>WST-1</td>\n",
       "      <td>TetrazoliumSalt</td>\n",
       "      <td>other</td>\n",
       "      <td>84.94</td>\n",
       "      <td>63.947800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>968</th>\n",
       "      <td>Ag</td>\n",
       "      <td>HEC</td>\n",
       "      <td>19.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sphere</td>\n",
       "      <td>100.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>HCT-116</td>\n",
       "      <td>Human</td>\n",
       "      <td>Colon</td>\n",
       "      <td>Epithelial</td>\n",
       "      <td>Adult</td>\n",
       "      <td>cancer</td>\n",
       "      <td>male</td>\n",
       "      <td>WST-1</td>\n",
       "      <td>TetrazoliumSalt</td>\n",
       "      <td>other</td>\n",
       "      <td>83.51</td>\n",
       "      <td>63.947800</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>969 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    NP_type Coating  core_size_nm  hydro_size_nm  Surf_charge_mV  \\\n",
       "0        Ag    None         150.0         144.04             NaN   \n",
       "1        Ag    None         150.0         145.09             NaN   \n",
       "2        Ag    None         150.0         146.40             NaN   \n",
       "3        Ag    None         150.0         149.02             NaN   \n",
       "4        Ag    None         150.0         154.27             NaN   \n",
       "..      ...     ...           ...            ...             ...   \n",
       "964      Ag     HEC          19.0            NaN             NaN   \n",
       "965      Ag     HEC          19.0            NaN             NaN   \n",
       "966      Ag     HEC          19.0            NaN             NaN   \n",
       "967      Ag     HEC          19.0            NaN             NaN   \n",
       "968      Ag     HEC          19.0            NaN             NaN   \n",
       "\n",
       "     Surface_area_m2_g   shape  Dose_microg_mL  Duration_h Cell_name  \\\n",
       "0                  NaN  Sphere             1.0        24.0      A549   \n",
       "1                  NaN  Sphere             5.0        24.0      A549   \n",
       "2                  NaN  Sphere            10.0        24.0      A549   \n",
       "3                  NaN  Sphere            20.0        24.0      A549   \n",
       "4                  NaN  Sphere            40.0        24.0      A549   \n",
       "..                 ...     ...             ...         ...       ...   \n",
       "964                NaN  Sphere           100.0        24.0   HCT-116   \n",
       "965                NaN  Sphere           100.0        24.0   HCT-116   \n",
       "966                NaN  Sphere           100.0        24.0   HCT-116   \n",
       "967                NaN  Sphere           100.0        24.0   HCT-116   \n",
       "968                NaN  Sphere           100.0        24.0   HCT-116   \n",
       "\n",
       "    Cell_species cell_Organ Cell_morphology Cell_age cell_type   sex  Assay  \\\n",
       "0          Human       Lung      Epithelial    Adult    cancer  male    MTT   \n",
       "1          Human       Lung      Epithelial    Adult    cancer  male    MTT   \n",
       "2          Human       Lung      Epithelial    Adult    cancer  male    MTT   \n",
       "3          Human       Lung      Epithelial    Adult    cancer  male    MTT   \n",
       "4          Human       Lung      Epithelial    Adult    cancer  male    MTT   \n",
       "..           ...        ...             ...      ...       ...   ...    ...   \n",
       "964        Human      Colon      Epithelial    Adult    cancer  male  WST-1   \n",
       "965        Human      Colon      Epithelial    Adult    cancer  male  WST-1   \n",
       "966        Human      Colon      Epithelial    Adult    cancer  male  WST-1   \n",
       "967        Human      Colon      Epithelial    Adult    cancer  male  WST-1   \n",
       "968        Human      Colon      Epithelial    Adult    cancer  male  WST-1   \n",
       "\n",
       "      Test_indicator nanomaterial_group  Viability      Label  \n",
       "0    TetrazoliumSalt              other      80.15  85.999500  \n",
       "1    TetrazoliumSalt              other      65.05  83.584699  \n",
       "2    TetrazoliumSalt              other      64.27  83.799399  \n",
       "3    TetrazoliumSalt              other      57.01  81.807499  \n",
       "4    TetrazoliumSalt              other      35.84  77.189700  \n",
       "..               ...                ...        ...        ...  \n",
       "964  TetrazoliumSalt              other      95.27  63.947800  \n",
       "965  TetrazoliumSalt              other      92.97  63.947800  \n",
       "966  TetrazoliumSalt              other      63.59  63.947800  \n",
       "967  TetrazoliumSalt              other      84.94  63.947800  \n",
       "968  TetrazoliumSalt              other      83.51  63.947800  \n",
       "\n",
       "[969 rows x 21 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict_model(final_model, data=data_asina_test_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "9fa4a740",
   "metadata": {},
   "outputs": [],
   "source": [
    "features = ['core_size_nm', 'hydro_size_nm', 'Surf_charge_mV', 'Surface_area_m2_g', 'Dose_microg_mL', 'Duration_h']\n",
    "\n",
    "data_train_df['label'] = 'train'\n",
    "data_asina_test_df['label'] = 'test'\n",
    "\n",
    "x = pd.concat([data_train_df, data_asina_test_df])\n",
    "\n",
    "y = x[['label']].copy()\n",
    "x = x[features].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "23bfc1c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "label    object\n",
       "dtype: object"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "3809a8a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "core_size_nm         float64\n",
       "hydro_size_nm        float64\n",
       "Surf_charge_mV       float64\n",
       "Surface_area_m2_g    float64\n",
       "Dose_microg_mL       float64\n",
       "Duration_h           float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "dbc21f2c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "906"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x['Surf_charge_mV'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "a205ee8f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from sklearn.impute import SimpleImputer\n",
    "imp_mean = SimpleImputer(missing_values=np.nan, strategy='mean')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "8a2ebfa7",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_clean = pd.DataFrame(imp_mean.fit_transform(x))\n",
    "x_clean.columns=x.columns\n",
    "x_clean.index=x.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "98c6af54",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_clean['Surf_charge_mV'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "266ff788",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "86c4aa38",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_clean = pd.DataFrame(StandardScaler().fit_transform(x_clean))\n",
    "x_clean.columns=x.columns\n",
    "x_clean.index=x.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "c329b016",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4693, 6)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "21c6e5af",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>train</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>train</td>\n",
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       "    <tr>\n",
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       "      <td>train</td>\n",
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      ],
      "text/plain": [
       "   label\n",
       "0  train\n",
       "1  train\n",
       "2  train\n",
       "3  train\n",
       "4  train"
      ]
     },
     "execution_count": 37,
     "metadata": {},
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   ],
   "source": [
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "a04ba474",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>core_size_nm</th>\n",
       "      <th>hydro_size_nm</th>\n",
       "      <th>Surf_charge_mV</th>\n",
       "      <th>Surface_area_m2_g</th>\n",
       "      <th>Dose_microg_mL</th>\n",
       "      <th>Duration_h</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.658429</td>\n",
       "      <td>2.303960e-02</td>\n",
       "      <td>0.602094</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.237481</td>\n",
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       "      <th>1</th>\n",
       "      <td>1.091089</td>\n",
       "      <td>2.503764e-16</td>\n",
       "      <td>0.175776</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.794409</td>\n",
       "      <td>-1.467326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.312041</td>\n",
       "      <td>-2.689899e-01</td>\n",
       "      <td>1.954021</td>\n",
       "      <td>-1.651016</td>\n",
       "      <td>-0.250649</td>\n",
       "      <td>-0.364715</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.583981</td>\n",
       "      <td>2.503764e-16</td>\n",
       "      <td>0.106643</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.229748</td>\n",
       "      <td>-0.364715</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.159423</td>\n",
       "      <td>2.503764e-16</td>\n",
       "      <td>-0.863134</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.248559</td>\n",
       "      <td>-0.364715</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.041637</td>\n",
       "      <td>-0.364715</td>\n",
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       "    <tr>\n",
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       "      <td>-0.594321</td>\n",
       "      <td>2.503764e-16</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.041637</td>\n",
       "      <td>-0.364715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>966</th>\n",
       "      <td>-0.594321</td>\n",
       "      <td>2.503764e-16</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.041637</td>\n",
       "      <td>-0.364715</td>\n",
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       "    <tr>\n",
       "      <th>967</th>\n",
       "      <td>-0.594321</td>\n",
       "      <td>2.503764e-16</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.041637</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.041637</td>\n",
       "      <td>-0.364715</td>\n",
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      ],
      "text/plain": [
       "     core_size_nm  hydro_size_nm  Surf_charge_mV  Surface_area_m2_g  \\\n",
       "0       -0.658429   2.303960e-02        0.602094           0.000000   \n",
       "1        1.091089   2.503764e-16        0.175776           0.000000   \n",
       "2       -0.312041  -2.689899e-01        1.954021          -1.651016   \n",
       "3       -0.583981   2.503764e-16        0.106643           0.000000   \n",
       "4       -0.159423   2.503764e-16       -0.863134           0.000000   \n",
       "..            ...            ...             ...                ...   \n",
       "964     -0.594321   2.503764e-16        0.000000           0.000000   \n",
       "965     -0.594321   2.503764e-16        0.000000           0.000000   \n",
       "966     -0.594321   2.503764e-16        0.000000           0.000000   \n",
       "967     -0.594321   2.503764e-16        0.000000           0.000000   \n",
       "968     -0.594321   2.503764e-16        0.000000           0.000000   \n",
       "\n",
       "     Dose_microg_mL  Duration_h  \n",
       "0         -0.237481    0.000000  \n",
       "1          0.794409   -1.467326  \n",
       "2         -0.250649   -0.364715  \n",
       "3         -0.229748   -0.364715  \n",
       "4         -0.248559   -0.364715  \n",
       "..              ...         ...  \n",
       "964       -0.041637   -0.364715  \n",
       "965       -0.041637   -0.364715  \n",
       "966       -0.041637   -0.364715  \n",
       "967       -0.041637   -0.364715  \n",
       "968       -0.041637   -0.364715  \n",
       "\n",
       "[4693 rows x 6 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_clean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "185b0348",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "pca = PCA(n_components=2)\n",
    "principalComponents = pca.fit_transform(x_clean)\n",
    "principalDf = pd.DataFrame(data = principalComponents, columns = ['principal component 1', 'principal component 2'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "09da90cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "      <th>principal component 1</th>\n",
       "      <th>principal component 2</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>0.122350</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.090919</td>\n",
       "      <td>0.058139</td>\n",
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       "      <th>2</th>\n",
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       "      <td>-0.358608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "      <th>4</th>\n",
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       "      <th>...</th>\n",
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       "      <th>4689</th>\n",
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       "      <td>-0.479414</td>\n",
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       "    <tr>\n",
       "      <th>4690</th>\n",
       "      <td>-0.094206</td>\n",
       "      <td>-0.479414</td>\n",
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       "    <tr>\n",
       "      <th>4691</th>\n",
       "      <td>-0.094206</td>\n",
       "      <td>-0.479414</td>\n",
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       "    <tr>\n",
       "      <th>4692</th>\n",
       "      <td>-0.094206</td>\n",
       "      <td>-0.479414</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4693 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      principal component 1  principal component 2\n",
       "0                  0.122350              -0.502440\n",
       "1                  1.090919               0.058139\n",
       "2                  1.581189              -0.358608\n",
       "3                 -0.051296              -0.610887\n",
       "4                 -0.510697              -0.315718\n",
       "...                     ...                    ...\n",
       "4688              -0.094206              -0.479414\n",
       "4689              -0.094206              -0.479414\n",
       "4690              -0.094206              -0.479414\n",
       "4691              -0.094206              -0.479414\n",
       "4692              -0.094206              -0.479414\n",
       "\n",
       "[4693 rows x 2 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "principalDf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "d7e69206",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "principal component 1    float64\n",
       "principal component 2    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "principalDf.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "20b53262",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "label    object\n",
       "dtype: object"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "3338f230",
   "metadata": {},
   "outputs": [],
   "source": [
    "principalDf = principalDf.reset_index(drop=True)\n",
    "y = y.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "89958141",
   "metadata": {},
   "outputs": [],
   "source": [
    "finalDf = pd.concat([principalDf, y], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "77a0adac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>principal component 1</th>\n",
       "      <th>principal component 2</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.122350</td>\n",
       "      <td>-0.502440</td>\n",
       "      <td>train</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.090919</td>\n",
       "      <td>0.058139</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.581189</td>\n",
       "      <td>-0.358608</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.051296</td>\n",
       "      <td>-0.610887</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.510697</td>\n",
       "      <td>-0.315718</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   principal component 1  principal component 2  label\n",
       "0               0.122350              -0.502440  train\n",
       "1               1.090919               0.058139  train\n",
       "2               1.581189              -0.358608  train\n",
       "3              -0.051296              -0.610887  train\n",
       "4              -0.510697              -0.315718  train"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finalDf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "475e5c2d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 712,
       "width": 691
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (8,8))\n",
    "ax = fig.add_subplot(1,1,1) \n",
    "ax.set_xlabel('Principal Component 1', fontsize = 15)\n",
    "ax.set_ylabel('Principal Component 2', fontsize = 15)\n",
    "ax.set_title('2 component PCA', fontsize = 20)\n",
    "targets = ['train', 'test']\n",
    "colors = ['r', 'g', 'b']\n",
    "for target, color in zip(targets,colors):\n",
    "    indicesToKeep = finalDf['label'] == target\n",
    "    ax.scatter(finalDf.loc[indicesToKeep, 'principal component 1']\n",
    "               , finalDf.loc[indicesToKeep, 'principal component 2']\n",
    "               , c = color\n",
    "               , s = 50)\n",
    "ax.legend(targets)\n",
    "ax.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "2b06bd99",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.20894695, 0.17564546])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.explained_variance_ratio_"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.8.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}