tutorial_02_pandas.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "stock-benchmark",
   "metadata": {},
   "source": [
    "**An Introduction to Pandas for Cheminformatics**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "convinced-effects",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from rdkit import Chem\n",
    "from tqdm.auto import tqdm\n",
    "from rdkit.Chem.Draw import MolsToGridImage\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "decent-reservation",
   "metadata": {},
   "source": [
    "Import data from a CSV file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "outside-smooth",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"curated-solubility-dataset.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "developing-missile",
   "metadata": {},
   "source": [
    "Read the first few lines of the dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "automotive-lawyer",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Name</th>\n",
       "      <th>InChI</th>\n",
       "      <th>InChIKey</th>\n",
       "      <th>SMILES</th>\n",
       "      <th>Solubility</th>\n",
       "      <th>SD</th>\n",
       "      <th>Ocurrences</th>\n",
       "      <th>Group</th>\n",
       "      <th>MolWt</th>\n",
       "      <th>...</th>\n",
       "      <th>NumRotatableBonds</th>\n",
       "      <th>NumValenceElectrons</th>\n",
       "      <th>NumAromaticRings</th>\n",
       "      <th>NumSaturatedRings</th>\n",
       "      <th>NumAliphaticRings</th>\n",
       "      <th>RingCount</th>\n",
       "      <th>TPSA</th>\n",
       "      <th>LabuteASA</th>\n",
       "      <th>BalabanJ</th>\n",
       "      <th>BertzCT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A-3</td>\n",
       "      <td>N,N,N-trimethyloctadecan-1-aminium bromide</td>\n",
       "      <td>InChI=1S/C21H46N.BrH/c1-5-6-7-8-9-10-11-12-13-...</td>\n",
       "      <td>SZEMGTQCPRNXEG-UHFFFAOYSA-M</td>\n",
       "      <td>[Br-].CCCCCCCCCCCCCCCCCC[N+](C)(C)C</td>\n",
       "      <td>-3.616127</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>G1</td>\n",
       "      <td>392.510</td>\n",
       "      <td>...</td>\n",
       "      <td>17.0</td>\n",
       "      <td>142.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>158.520601</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>210.377334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A-4</td>\n",
       "      <td>Benzo[cd]indol-2(1H)-one</td>\n",
       "      <td>InChI=1S/C11H7NO/c13-11-8-5-1-3-7-4-2-6-9(12-1...</td>\n",
       "      <td>GPYLCFQEKPUWLD-UHFFFAOYSA-N</td>\n",
       "      <td>O=C1Nc2cccc3cccc1c23</td>\n",
       "      <td>-3.254767</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>G1</td>\n",
       "      <td>169.183</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>29.10</td>\n",
       "      <td>75.183563</td>\n",
       "      <td>2.582996e+00</td>\n",
       "      <td>511.229248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A-5</td>\n",
       "      <td>4-chlorobenzaldehyde</td>\n",
       "      <td>InChI=1S/C7H5ClO/c8-7-3-1-6(5-9)2-4-7/h1-5H</td>\n",
       "      <td>AVPYQKSLYISFPO-UHFFFAOYSA-N</td>\n",
       "      <td>Clc1ccc(C=O)cc1</td>\n",
       "      <td>-2.177078</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>G1</td>\n",
       "      <td>140.569</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>17.07</td>\n",
       "      <td>58.261134</td>\n",
       "      <td>3.009782e+00</td>\n",
       "      <td>202.661065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A-8</td>\n",
       "      <td>zinc bis[2-hydroxy-3,5-bis(1-phenylethyl)benzo...</td>\n",
       "      <td>InChI=1S/2C23H22O3.Zn/c2*1-15(17-9-5-3-6-10-17...</td>\n",
       "      <td>XTUPUYCJWKHGSW-UHFFFAOYSA-L</td>\n",
       "      <td>[Zn++].CC(c1ccccc1)c2cc(C(C)c3ccccc3)c(O)c(c2)...</td>\n",
       "      <td>-3.924409</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>G1</td>\n",
       "      <td>756.226</td>\n",
       "      <td>...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>264.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>120.72</td>\n",
       "      <td>323.755434</td>\n",
       "      <td>2.322963e-07</td>\n",
       "      <td>1964.648666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A-9</td>\n",
       "      <td>4-({4-[bis(oxiran-2-ylmethyl)amino]phenyl}meth...</td>\n",
       "      <td>InChI=1S/C25H30N2O4/c1-5-20(26(10-22-14-28-22)...</td>\n",
       "      <td>FAUAZXVRLVIARB-UHFFFAOYSA-N</td>\n",
       "      <td>C1OC1CN(CC2CO2)c3ccc(Cc4ccc(cc4)N(CC5CO5)CC6CO...</td>\n",
       "      <td>-4.662065</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>G1</td>\n",
       "      <td>422.525</td>\n",
       "      <td>...</td>\n",
       "      <td>12.0</td>\n",
       "      <td>164.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>56.60</td>\n",
       "      <td>183.183268</td>\n",
       "      <td>1.084427e+00</td>\n",
       "      <td>769.899934</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    ID                                               Name  \\\n",
       "0  A-3         N,N,N-trimethyloctadecan-1-aminium bromide   \n",
       "1  A-4                           Benzo[cd]indol-2(1H)-one   \n",
       "2  A-5                               4-chlorobenzaldehyde   \n",
       "3  A-8  zinc bis[2-hydroxy-3,5-bis(1-phenylethyl)benzo...   \n",
       "4  A-9  4-({4-[bis(oxiran-2-ylmethyl)amino]phenyl}meth...   \n",
       "\n",
       "                                               InChI  \\\n",
       "0  InChI=1S/C21H46N.BrH/c1-5-6-7-8-9-10-11-12-13-...   \n",
       "1  InChI=1S/C11H7NO/c13-11-8-5-1-3-7-4-2-6-9(12-1...   \n",
       "2        InChI=1S/C7H5ClO/c8-7-3-1-6(5-9)2-4-7/h1-5H   \n",
       "3  InChI=1S/2C23H22O3.Zn/c2*1-15(17-9-5-3-6-10-17...   \n",
       "4  InChI=1S/C25H30N2O4/c1-5-20(26(10-22-14-28-22)...   \n",
       "\n",
       "                      InChIKey  \\\n",
       "0  SZEMGTQCPRNXEG-UHFFFAOYSA-M   \n",
       "1  GPYLCFQEKPUWLD-UHFFFAOYSA-N   \n",
       "2  AVPYQKSLYISFPO-UHFFFAOYSA-N   \n",
       "3  XTUPUYCJWKHGSW-UHFFFAOYSA-L   \n",
       "4  FAUAZXVRLVIARB-UHFFFAOYSA-N   \n",
       "\n",
       "                                              SMILES  Solubility   SD  \\\n",
       "0                [Br-].CCCCCCCCCCCCCCCCCC[N+](C)(C)C   -3.616127  0.0   \n",
       "1                               O=C1Nc2cccc3cccc1c23   -3.254767  0.0   \n",
       "2                                    Clc1ccc(C=O)cc1   -2.177078  0.0   \n",
       "3  [Zn++].CC(c1ccccc1)c2cc(C(C)c3ccccc3)c(O)c(c2)...   -3.924409  0.0   \n",
       "4  C1OC1CN(CC2CO2)c3ccc(Cc4ccc(cc4)N(CC5CO5)CC6CO...   -4.662065  0.0   \n",
       "\n",
       "   Ocurrences Group    MolWt  ...  NumRotatableBonds  NumValenceElectrons  \\\n",
       "0           1    G1  392.510  ...               17.0                142.0   \n",
       "1           1    G1  169.183  ...                0.0                 62.0   \n",
       "2           1    G1  140.569  ...                1.0                 46.0   \n",
       "3           1    G1  756.226  ...               10.0                264.0   \n",
       "4           1    G1  422.525  ...               12.0                164.0   \n",
       "\n",
       "   NumAromaticRings  NumSaturatedRings  NumAliphaticRings  RingCount    TPSA  \\\n",
       "0               0.0                0.0                0.0        0.0    0.00   \n",
       "1               2.0                0.0                1.0        3.0   29.10   \n",
       "2               1.0                0.0                0.0        1.0   17.07   \n",
       "3               6.0                0.0                0.0        6.0  120.72   \n",
       "4               2.0                4.0                4.0        6.0   56.60   \n",
       "\n",
       "    LabuteASA      BalabanJ      BertzCT  \n",
       "0  158.520601  0.000000e+00   210.377334  \n",
       "1   75.183563  2.582996e+00   511.229248  \n",
       "2   58.261134  3.009782e+00   202.661065  \n",
       "3  323.755434  2.322963e-07  1964.648666  \n",
       "4  183.183268  1.084427e+00   769.899934  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "technical-romantic",
   "metadata": {},
   "source": [
    "Examine the datatypes for the dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "elementary-temperature",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID                      object\n",
       "Name                    object\n",
       "InChI                   object\n",
       "InChIKey                object\n",
       "SMILES                  object\n",
       "Solubility             float64\n",
       "SD                     float64\n",
       "Ocurrences               int64\n",
       "Group                   object\n",
       "MolWt                  float64\n",
       "MolLogP                float64\n",
       "MolMR                  float64\n",
       "HeavyAtomCount         float64\n",
       "NumHAcceptors          float64\n",
       "NumHDonors             float64\n",
       "NumHeteroatoms         float64\n",
       "NumRotatableBonds      float64\n",
       "NumValenceElectrons    float64\n",
       "NumAromaticRings       float64\n",
       "NumSaturatedRings      float64\n",
       "NumAliphaticRings      float64\n",
       "RingCount              float64\n",
       "TPSA                   float64\n",
       "LabuteASA              float64\n",
       "BalabanJ               float64\n",
       "BertzCT                float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "disciplinary-colorado",
   "metadata": {},
   "source": [
    "The Pandas **describe** function provides summary statistics for numeric dataframe columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "sharp-storm",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Solubility</th>\n",
       "      <th>SD</th>\n",
       "      <th>Ocurrences</th>\n",
       "      <th>MolWt</th>\n",
       "      <th>MolLogP</th>\n",
       "      <th>MolMR</th>\n",
       "      <th>HeavyAtomCount</th>\n",
       "      <th>NumHAcceptors</th>\n",
       "      <th>NumHDonors</th>\n",
       "      <th>NumHeteroatoms</th>\n",
       "      <th>NumRotatableBonds</th>\n",
       "      <th>NumValenceElectrons</th>\n",
       "      <th>NumAromaticRings</th>\n",
       "      <th>NumSaturatedRings</th>\n",
       "      <th>NumAliphaticRings</th>\n",
       "      <th>RingCount</th>\n",
       "      <th>TPSA</th>\n",
       "      <th>LabuteASA</th>\n",
       "      <th>BalabanJ</th>\n",
       "      <th>BertzCT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>9982.000000</td>\n",
       "      <td>9982.000000</td>\n",
       "      <td>9982.000000</td>\n",
       "      <td>9982.000000</td>\n",
       "      <td>9982.000000</td>\n",
       "      <td>9982.000000</td>\n",
       "      <td>9982.000000</td>\n",
       "      <td>9982.000000</td>\n",
       "      <td>9982.000000</td>\n",
       "      <td>9982.000000</td>\n",
       "      <td>9982.000000</td>\n",
       "      <td>9982.000000</td>\n",
       "      <td>9982.000000</td>\n",
       "      <td>9982.000000</td>\n",
       "      <td>9982.000000</td>\n",
       "      <td>9982.000000</td>\n",
       "      <td>9982.000000</td>\n",
       "      <td>9982.000000</td>\n",
       "      <td>9982.000000</td>\n",
       "      <td>9982.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-2.889909</td>\n",
       "      <td>0.067449</td>\n",
       "      <td>1.378081</td>\n",
       "      <td>266.665946</td>\n",
       "      <td>1.979167</td>\n",
       "      <td>66.794594</td>\n",
       "      <td>17.374674</td>\n",
       "      <td>3.486776</td>\n",
       "      <td>1.108595</td>\n",
       "      <td>5.196955</td>\n",
       "      <td>4.073031</td>\n",
       "      <td>94.243438</td>\n",
       "      <td>1.068323</td>\n",
       "      <td>0.292627</td>\n",
       "      <td>0.447606</td>\n",
       "      <td>1.515929</td>\n",
       "      <td>62.458601</td>\n",
       "      <td>108.912586</td>\n",
       "      <td>2.392199</td>\n",
       "      <td>467.336782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.368154</td>\n",
       "      <td>0.234702</td>\n",
       "      <td>1.023476</td>\n",
       "      <td>184.179024</td>\n",
       "      <td>3.517738</td>\n",
       "      <td>46.523021</td>\n",
       "      <td>12.241536</td>\n",
       "      <td>3.498203</td>\n",
       "      <td>1.488973</td>\n",
       "      <td>4.736275</td>\n",
       "      <td>5.646925</td>\n",
       "      <td>64.748563</td>\n",
       "      <td>1.309427</td>\n",
       "      <td>0.879599</td>\n",
       "      <td>1.054667</td>\n",
       "      <td>1.644334</td>\n",
       "      <td>63.348307</td>\n",
       "      <td>76.462726</td>\n",
       "      <td>1.091123</td>\n",
       "      <td>546.631696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-13.171900</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>9.012000</td>\n",
       "      <td>-40.873200</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.504228</td>\n",
       "      <td>-0.000004</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-4.326325</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>162.276000</td>\n",
       "      <td>0.619750</td>\n",
       "      <td>40.602475</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>26.300000</td>\n",
       "      <td>66.623721</td>\n",
       "      <td>2.004357</td>\n",
       "      <td>163.243343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-2.618173</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>228.682000</td>\n",
       "      <td>1.949220</td>\n",
       "      <td>58.633400</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>82.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>50.720000</td>\n",
       "      <td>93.299873</td>\n",
       "      <td>2.539539</td>\n",
       "      <td>351.640433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>-1.209735</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>320.436000</td>\n",
       "      <td>3.419030</td>\n",
       "      <td>81.938000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>112.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>80.390000</td>\n",
       "      <td>129.118374</td>\n",
       "      <td>3.032456</td>\n",
       "      <td>606.562848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.137682</td>\n",
       "      <td>3.870145</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>5299.456000</td>\n",
       "      <td>68.541140</td>\n",
       "      <td>1419.351700</td>\n",
       "      <td>388.000000</td>\n",
       "      <td>86.000000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>89.000000</td>\n",
       "      <td>141.000000</td>\n",
       "      <td>2012.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>1214.340000</td>\n",
       "      <td>2230.685124</td>\n",
       "      <td>7.517310</td>\n",
       "      <td>20720.267708</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Solubility           SD   Ocurrences        MolWt      MolLogP  \\\n",
       "count  9982.000000  9982.000000  9982.000000  9982.000000  9982.000000   \n",
       "mean     -2.889909     0.067449     1.378081   266.665946     1.979167   \n",
       "std       2.368154     0.234702     1.023476   184.179024     3.517738   \n",
       "min     -13.171900     0.000000     1.000000     9.012000   -40.873200   \n",
       "25%      -4.326325     0.000000     1.000000   162.276000     0.619750   \n",
       "50%      -2.618173     0.000000     1.000000   228.682000     1.949220   \n",
       "75%      -1.209735     0.000000     1.000000   320.436000     3.419030   \n",
       "max       2.137682     3.870145    38.000000  5299.456000    68.541140   \n",
       "\n",
       "             MolMR  HeavyAtomCount  NumHAcceptors   NumHDonors  \\\n",
       "count  9982.000000     9982.000000    9982.000000  9982.000000   \n",
       "mean     66.794594       17.374674       3.486776     1.108595   \n",
       "std      46.523021       12.241536       3.498203     1.488973   \n",
       "min       0.000000        1.000000       0.000000     0.000000   \n",
       "25%      40.602475       11.000000       2.000000     0.000000   \n",
       "50%      58.633400       15.000000       3.000000     1.000000   \n",
       "75%      81.938000       21.000000       4.000000     2.000000   \n",
       "max    1419.351700      388.000000      86.000000    26.000000   \n",
       "\n",
       "       NumHeteroatoms  NumRotatableBonds  NumValenceElectrons  \\\n",
       "count     9982.000000        9982.000000          9982.000000   \n",
       "mean         5.196955           4.073031            94.243438   \n",
       "std          4.736275           5.646925            64.748563   \n",
       "min          0.000000           0.000000             0.000000   \n",
       "25%          3.000000           1.000000            58.000000   \n",
       "50%          4.000000           3.000000            82.000000   \n",
       "75%          6.000000           5.000000           112.000000   \n",
       "max         89.000000         141.000000          2012.000000   \n",
       "\n",
       "       NumAromaticRings  NumSaturatedRings  NumAliphaticRings    RingCount  \\\n",
       "count       9982.000000        9982.000000        9982.000000  9982.000000   \n",
       "mean           1.068323           0.292627           0.447606     1.515929   \n",
       "std            1.309427           0.879599           1.054667     1.644334   \n",
       "min            0.000000           0.000000           0.000000     0.000000   \n",
       "25%            0.000000           0.000000           0.000000     0.000000   \n",
       "50%            1.000000           0.000000           0.000000     1.000000   \n",
       "75%            2.000000           0.000000           1.000000     2.000000   \n",
       "max           35.000000          30.000000          30.000000    36.000000   \n",
       "\n",
       "              TPSA    LabuteASA     BalabanJ       BertzCT  \n",
       "count  9982.000000  9982.000000  9982.000000   9982.000000  \n",
       "mean     62.458601   108.912586     2.392199    467.336782  \n",
       "std      63.348307    76.462726     1.091123    546.631696  \n",
       "min       0.000000     7.504228    -0.000004      0.000000  \n",
       "25%      26.300000    66.623721     2.004357    163.243343  \n",
       "50%      50.720000    93.299873     2.539539    351.640433  \n",
       "75%      80.390000   129.118374     3.032456    606.562848  \n",
       "max    1214.340000  2230.685124     7.517310  20720.267708  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "undefined-class",
   "metadata": {},
   "source": [
    "The **columns** attribute of dataframe provides the column names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "collected-pride",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ID', 'Name', 'InChI', 'InChIKey', 'SMILES', 'Solubility', 'SD',\n",
       "       'Ocurrences', 'Group', 'MolWt', 'MolLogP', 'MolMR', 'HeavyAtomCount',\n",
       "       'NumHAcceptors', 'NumHDonors', 'NumHeteroatoms', 'NumRotatableBonds',\n",
       "       'NumValenceElectrons', 'NumAromaticRings', 'NumSaturatedRings',\n",
       "       'NumAliphaticRings', 'RingCount', 'TPSA', 'LabuteASA', 'BalabanJ',\n",
       "       'BertzCT'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fallen-premiere",
   "metadata": {},
   "source": [
    "The **sort_values** function sorts a dataframe.  Note that this returns another dataframe.  To sort the dataframe in place set **inplace=True**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "criminal-kruger",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Name</th>\n",
       "      <th>InChI</th>\n",
       "      <th>InChIKey</th>\n",
       "      <th>SMILES</th>\n",
       "      <th>Solubility</th>\n",
       "      <th>SD</th>\n",
       "      <th>Ocurrences</th>\n",
       "      <th>Group</th>\n",
       "      <th>MolWt</th>\n",
       "      <th>...</th>\n",
       "      <th>NumRotatableBonds</th>\n",
       "      <th>NumValenceElectrons</th>\n",
       "      <th>NumAromaticRings</th>\n",
       "      <th>NumSaturatedRings</th>\n",
       "      <th>NumAliphaticRings</th>\n",
       "      <th>RingCount</th>\n",
       "      <th>TPSA</th>\n",
       "      <th>LabuteASA</th>\n",
       "      <th>BalabanJ</th>\n",
       "      <th>BertzCT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9981</th>\n",
       "      <td>I-94</td>\n",
       "      <td>warfarin</td>\n",
       "      <td>InChI=1S/C19H16O4/c1-12(20)11-15(13-7-3-2-4-8-...</td>\n",
       "      <td>PJVWKTKQMONHTI-UHFFFAOYSA-N</td>\n",
       "      <td>CC(=O)CC(c1ccccc1)c1c(O)c2ccccc2oc1=O</td>\n",
       "      <td>-4.7800</td>\n",
       "      <td>0.450506</td>\n",
       "      <td>3</td>\n",
       "      <td>G5</td>\n",
       "      <td>308.333</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>116.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>67.51</td>\n",
       "      <td>132.552025</td>\n",
       "      <td>2.258072</td>\n",
       "      <td>909.550973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8710</th>\n",
       "      <td>E-140</td>\n",
       "      <td>trichloroethylene</td>\n",
       "      <td>InChI=1S/C2HCl3/c3-1-2(4)5/h1H</td>\n",
       "      <td>XSTXAVWGXDQKEL-UHFFFAOYSA-N</td>\n",
       "      <td>ClC=C(Cl)Cl</td>\n",
       "      <td>-1.9600</td>\n",
       "      <td>0.063496</td>\n",
       "      <td>3</td>\n",
       "      <td>G5</td>\n",
       "      <td>131.389</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>45.324389</td>\n",
       "      <td>3.143197</td>\n",
       "      <td>42.854753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8765</th>\n",
       "      <td>E-530</td>\n",
       "      <td>ephedrine</td>\n",
       "      <td>InChI=1S/C10H15NO/c1-8(11-2)10(12)9-6-4-3-5-7-...</td>\n",
       "      <td>KWGRBVOPPLSCSI-UHFFFAOYSA-N</td>\n",
       "      <td>OC(c(cccc1)c1)C(NC)C</td>\n",
       "      <td>-0.4200</td>\n",
       "      <td>0.049216</td>\n",
       "      <td>3</td>\n",
       "      <td>G5</td>\n",
       "      <td>165.236</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>32.26</td>\n",
       "      <td>73.235268</td>\n",
       "      <td>2.711041</td>\n",
       "      <td>222.540006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8764</th>\n",
       "      <td>E-521</td>\n",
       "      <td>phenallymal</td>\n",
       "      <td>InChI=1S/C13H12N2O3/c1-2-8-13(9-6-4-3-5-7-9)10...</td>\n",
       "      <td>WOIGZSBYKGQJGL-UHFFFAOYSA-N</td>\n",
       "      <td>c1ccccc1C2(CC=C)C(=O)NC(=O)NC2=O</td>\n",
       "      <td>-2.1800</td>\n",
       "      <td>0.096968</td>\n",
       "      <td>4</td>\n",
       "      <td>G5</td>\n",
       "      <td>244.250</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>75.27</td>\n",
       "      <td>103.874854</td>\n",
       "      <td>2.560321</td>\n",
       "      <td>502.297711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8763</th>\n",
       "      <td>E-496</td>\n",
       "      <td>strychnine</td>\n",
       "      <td>InChI=1S/C21H22N2O2/c24-18-10-16-19-13-9-17-21...</td>\n",
       "      <td>QMGVPVSNSZLJIA-UHFFFAOYSA-N</td>\n",
       "      <td>O=C(N(c(c(C1(C(N(C2)CC(C3C4C5OC6)=C6)C3)C2)ccc...</td>\n",
       "      <td>-3.3200</td>\n",
       "      <td>0.440782</td>\n",
       "      <td>3</td>\n",
       "      <td>G5</td>\n",
       "      <td>334.419</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>32.78</td>\n",
       "      <td>146.975680</td>\n",
       "      <td>1.552423</td>\n",
       "      <td>848.052734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4009</th>\n",
       "      <td>B-570</td>\n",
       "      <td>2-methylpyrazine</td>\n",
       "      <td>InChI=1S/C5H6N2/c1-5-4-6-2-3-7-5/h2-4H,1H3</td>\n",
       "      <td>CAWHJQAVHZEVTJ-UHFFFAOYSA-N</td>\n",
       "      <td>Cc1cnccn1</td>\n",
       "      <td>1.0263</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>G1</td>\n",
       "      <td>94.117</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>25.78</td>\n",
       "      <td>42.235641</td>\n",
       "      <td>3.021465</td>\n",
       "      <td>136.007504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4007</th>\n",
       "      <td>B-567</td>\n",
       "      <td>n-methylmorpholine</td>\n",
       "      <td>InChI=1S/C5H11NO/c1-6-2-4-7-5-3-6/h2-5H2,1H3</td>\n",
       "      <td>SJRJJKPEHAURKC-UHFFFAOYSA-N</td>\n",
       "      <td>CN1CCOCC1</td>\n",
       "      <td>0.9950</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>G1</td>\n",
       "      <td>101.149</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12.47</td>\n",
       "      <td>44.066567</td>\n",
       "      <td>2.122918</td>\n",
       "      <td>50.041844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4006</th>\n",
       "      <td>B-566</td>\n",
       "      <td>3-methylpyridine</td>\n",
       "      <td>InChI=1S/C6H7N/c1-6-3-2-4-7-5-6/h2-5H,1H3</td>\n",
       "      <td>ITQTTZVARXURQS-UHFFFAOYSA-N</td>\n",
       "      <td>Cc1cccnc1</td>\n",
       "      <td>1.0309</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>G1</td>\n",
       "      <td>93.129</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12.89</td>\n",
       "      <td>43.015993</td>\n",
       "      <td>3.021465</td>\n",
       "      <td>134.107370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4005</th>\n",
       "      <td>B-565</td>\n",
       "      <td>thiophenol</td>\n",
       "      <td>InChI=1S/C6H6S/c7-6-4-2-1-3-5-6/h1-5,7H</td>\n",
       "      <td>RMVRSNDYEFQCLF-UHFFFAOYSA-N</td>\n",
       "      <td>Sc1ccccc1</td>\n",
       "      <td>-2.1204</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>G1</td>\n",
       "      <td>110.181</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>48.586470</td>\n",
       "      <td>3.021465</td>\n",
       "      <td>134.107370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4991</th>\n",
       "      <td>B-1809</td>\n",
       "      <td>msma</td>\n",
       "      <td>InChI=1S/CH5AsO3.Na/c1-2(3,4)5;/h1H3,(H2,3,4,5...</td>\n",
       "      <td>JITOKQVGRJSHHA-UHFFFAOYSA-M</td>\n",
       "      <td>[Na+].C[As](O)([O-])=O</td>\n",
       "      <td>0.5540</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>G1</td>\n",
       "      <td>161.952</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>60.36</td>\n",
       "      <td>61.037451</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>56.928787</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9982 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          ID                Name  \\\n",
       "9981    I-94            warfarin   \n",
       "8710   E-140   trichloroethylene   \n",
       "8765   E-530           ephedrine   \n",
       "8764   E-521         phenallymal   \n",
       "8763   E-496          strychnine   \n",
       "...      ...                 ...   \n",
       "4009   B-570    2-methylpyrazine   \n",
       "4007   B-567  n-methylmorpholine   \n",
       "4006   B-566    3-methylpyridine   \n",
       "4005   B-565          thiophenol   \n",
       "4991  B-1809                msma   \n",
       "\n",
       "                                                  InChI  \\\n",
       "9981  InChI=1S/C19H16O4/c1-12(20)11-15(13-7-3-2-4-8-...   \n",
       "8710                     InChI=1S/C2HCl3/c3-1-2(4)5/h1H   \n",
       "8765  InChI=1S/C10H15NO/c1-8(11-2)10(12)9-6-4-3-5-7-...   \n",
       "8764  InChI=1S/C13H12N2O3/c1-2-8-13(9-6-4-3-5-7-9)10...   \n",
       "8763  InChI=1S/C21H22N2O2/c24-18-10-16-19-13-9-17-21...   \n",
       "...                                                 ...   \n",
       "4009         InChI=1S/C5H6N2/c1-5-4-6-2-3-7-5/h2-4H,1H3   \n",
       "4007       InChI=1S/C5H11NO/c1-6-2-4-7-5-3-6/h2-5H2,1H3   \n",
       "4006          InChI=1S/C6H7N/c1-6-3-2-4-7-5-6/h2-5H,1H3   \n",
       "4005            InChI=1S/C6H6S/c7-6-4-2-1-3-5-6/h1-5,7H   \n",
       "4991  InChI=1S/CH5AsO3.Na/c1-2(3,4)5;/h1H3,(H2,3,4,5...   \n",
       "\n",
       "                         InChIKey  \\\n",
       "9981  PJVWKTKQMONHTI-UHFFFAOYSA-N   \n",
       "8710  XSTXAVWGXDQKEL-UHFFFAOYSA-N   \n",
       "8765  KWGRBVOPPLSCSI-UHFFFAOYSA-N   \n",
       "8764  WOIGZSBYKGQJGL-UHFFFAOYSA-N   \n",
       "8763  QMGVPVSNSZLJIA-UHFFFAOYSA-N   \n",
       "...                           ...   \n",
       "4009  CAWHJQAVHZEVTJ-UHFFFAOYSA-N   \n",
       "4007  SJRJJKPEHAURKC-UHFFFAOYSA-N   \n",
       "4006  ITQTTZVARXURQS-UHFFFAOYSA-N   \n",
       "4005  RMVRSNDYEFQCLF-UHFFFAOYSA-N   \n",
       "4991  JITOKQVGRJSHHA-UHFFFAOYSA-M   \n",
       "\n",
       "                                                 SMILES  Solubility        SD  \\\n",
       "9981              CC(=O)CC(c1ccccc1)c1c(O)c2ccccc2oc1=O     -4.7800  0.450506   \n",
       "8710                                        ClC=C(Cl)Cl     -1.9600  0.063496   \n",
       "8765                               OC(c(cccc1)c1)C(NC)C     -0.4200  0.049216   \n",
       "8764                   c1ccccc1C2(CC=C)C(=O)NC(=O)NC2=O     -2.1800  0.096968   \n",
       "8763  O=C(N(c(c(C1(C(N(C2)CC(C3C4C5OC6)=C6)C3)C2)ccc...     -3.3200  0.440782   \n",
       "...                                                 ...         ...       ...   \n",
       "4009                                          Cc1cnccn1      1.0263  0.000000   \n",
       "4007                                          CN1CCOCC1      0.9950  0.000000   \n",
       "4006                                          Cc1cccnc1      1.0309  0.000000   \n",
       "4005                                          Sc1ccccc1     -2.1204  0.000000   \n",
       "4991                             [Na+].C[As](O)([O-])=O      0.5540  0.000000   \n",
       "\n",
       "      Ocurrences Group    MolWt  ...  NumRotatableBonds  NumValenceElectrons  \\\n",
       "9981           3    G5  308.333  ...                4.0                116.0   \n",
       "8710           3    G5  131.389  ...                0.0                 30.0   \n",
       "8765           3    G5  165.236  ...                3.0                 66.0   \n",
       "8764           4    G5  244.250  ...                3.0                 92.0   \n",
       "8763           3    G5  334.419  ...                0.0                128.0   \n",
       "...          ...   ...      ...  ...                ...                  ...   \n",
       "4009           1    G1   94.117  ...                0.0                 36.0   \n",
       "4007           1    G1  101.149  ...                0.0                 42.0   \n",
       "4006           1    G1   93.129  ...                0.0                 36.0   \n",
       "4005           1    G1  110.181  ...                0.0                 36.0   \n",
       "4991           1    G1  161.952  ...                0.0                 32.0   \n",
       "\n",
       "      NumAromaticRings  NumSaturatedRings  NumAliphaticRings  RingCount  \\\n",
       "9981               3.0                0.0                0.0        3.0   \n",
       "8710               0.0                0.0                0.0        0.0   \n",
       "8765               1.0                0.0                0.0        1.0   \n",
       "8764               1.0                1.0                1.0        2.0   \n",
       "8763               1.0                4.0                6.0        7.0   \n",
       "...                ...                ...                ...        ...   \n",
       "4009               1.0                0.0                0.0        1.0   \n",
       "4007               0.0                1.0                1.0        1.0   \n",
       "4006               1.0                0.0                0.0        1.0   \n",
       "4005               1.0                0.0                0.0        1.0   \n",
       "4991               0.0                0.0                0.0        0.0   \n",
       "\n",
       "       TPSA   LabuteASA  BalabanJ     BertzCT  \n",
       "9981  67.51  132.552025  2.258072  909.550973  \n",
       "8710   0.00   45.324389  3.143197   42.854753  \n",
       "8765  32.26   73.235268  2.711041  222.540006  \n",
       "8764  75.27  103.874854  2.560321  502.297711  \n",
       "8763  32.78  146.975680  1.552423  848.052734  \n",
       "...     ...         ...       ...         ...  \n",
       "4009  25.78   42.235641  3.021465  136.007504  \n",
       "4007  12.47   44.066567  2.122918   50.041844  \n",
       "4006  12.89   43.015993  3.021465  134.107370  \n",
       "4005   0.00   48.586470  3.021465  134.107370  \n",
       "4991  60.36   61.037451  0.000000   56.928787  \n",
       "\n",
       "[9982 rows x 26 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(\"Group\",ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "fabulous-delaware",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SMILES</th>\n",
       "      <th>MF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[O--].[O--].[O--].[O--].[O--].[Al+3].[Al+3].[C...</td>\n",
       "      <td>Al2Ca2O5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[O--].[O--].[O--].[O--].[O--].[O--].[Al+3].[Al...</td>\n",
       "      <td>Al2Ca6O18S3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[O--].[O--].[O--].[O--].[O--].[Al+3].[Al+3].[C...</td>\n",
       "      <td>Al2Co2O5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[O--].[O--].[O--].[O--].[O--].[O--].[O--].[O--...</td>\n",
       "      <td>Al2Mo2O9-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[O--].[O--].[O--].[O--].[O--].[Al+3].[Al+3].[Z...</td>\n",
       "      <td>Al2O5Zn2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9977</th>\n",
       "      <td>[Ta]</td>\n",
       "      <td>Ta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9978</th>\n",
       "      <td>[Te]</td>\n",
       "      <td>Te</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9979</th>\n",
       "      <td>[V]</td>\n",
       "      <td>V</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9980</th>\n",
       "      <td>[Y]</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9981</th>\n",
       "      <td>[Zr]</td>\n",
       "      <td>Zr</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9982 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 SMILES           MF\n",
       "0     [O--].[O--].[O--].[O--].[O--].[Al+3].[Al+3].[C...     Al2Ca2O5\n",
       "1     [O--].[O--].[O--].[O--].[O--].[O--].[Al+3].[Al...  Al2Ca6O18S3\n",
       "2     [O--].[O--].[O--].[O--].[O--].[Al+3].[Al+3].[C...     Al2Co2O5\n",
       "3     [O--].[O--].[O--].[O--].[O--].[O--].[O--].[O--...  Al2Mo2O9-12\n",
       "4     [O--].[O--].[O--].[O--].[O--].[Al+3].[Al+3].[Z...     Al2O5Zn2\n",
       "...                                                 ...          ...\n",
       "9977                                               [Ta]           Ta\n",
       "9978                                               [Te]           Te\n",
       "9979                                                [V]            V\n",
       "9980                                                [Y]            Y\n",
       "9981                                               [Zr]           Zr\n",
       "\n",
       "[9982 rows x 2 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mf_df = pd.read_csv(\"mol_formula.csv\")\n",
    "mf_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "specific-mobile",
   "metadata": {},
   "source": [
    "I like to be able have progress bar for operations that take more than a couple of seconds.  The tqdm library provides a simple way of adding a progress bar to a python program. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "congressional-business",
   "metadata": {},
   "outputs": [],
   "source": [
    "tqdm.pandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "tough-declaration",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4b9a077fc97e4b22a8d77c9623f7ce1e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/9982 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "RDKit WARNING: [20:26:47] WARNING: not removing hydrogen atom without neighbors\n",
      "RDKit WARNING: [20:26:47] WARNING: not removing hydrogen atom without neighbors\n",
      "RDKit WARNING: [20:26:47] WARNING: not removing hydrogen atom without neighbors\n",
      "RDKit WARNING: [20:26:47] WARNING: not removing hydrogen atom without neighbors\n",
      "RDKit WARNING: [20:26:47] WARNING: not removing hydrogen atom without neighbors\n",
      "RDKit WARNING: [20:26:47] WARNING: not removing hydrogen atom without neighbors\n",
      "RDKit WARNING: [20:26:47] WARNING: not removing hydrogen atom without neighbors\n",
      "RDKit WARNING: [20:26:47] WARNING: not removing hydrogen atom without neighbors\n",
      "RDKit WARNING: [20:26:47] WARNING: not removing hydrogen atom without neighbors\n",
      "RDKit WARNING: [20:26:47] WARNING: not removing hydrogen atom without neighbors\n",
      "RDKit WARNING: [20:26:47] WARNING: not removing hydrogen atom without neighbors\n",
      "RDKit WARNING: [20:26:47] WARNING: not removing hydrogen atom without neighbors\n",
      "RDKit WARNING: [20:26:47] WARNING: not removing hydrogen atom without neighbors\n",
      "RDKit WARNING: [20:26:47] WARNING: not removing hydrogen atom without neighbors\n",
      "RDKit WARNING: [20:26:47] WARNING: not removing hydrogen atom without neighbors\n",
      "RDKit WARNING: [20:26:47] WARNING: not removing hydrogen atom without neighbors\n",
      "RDKit WARNING: [20:26:47] WARNING: not removing hydrogen atom without neighbors\n",
      "RDKit WARNING: [20:26:47] WARNING: not removing hydrogen atom without neighbors\n",
      "RDKit WARNING: [20:26:47] WARNING: not removing hydrogen atom without neighbors\n",
      "RDKit WARNING: [20:26:47] WARNING: not removing hydrogen atom without neighbors\n",
      "RDKit WARNING: [20:26:47] WARNING: not removing hydrogen atom without neighbors\n",
      "RDKit WARNING: [20:26:47] WARNING: not removing hydrogen atom without neighbors\n",
      "RDKit WARNING: [20:26:47] WARNING: not removing hydrogen atom without neighbors\n",
      "RDKit WARNING: [20:26:47] WARNING: not removing hydrogen atom without neighbors\n",
      "RDKit WARNING: [20:26:47] WARNING: not removing hydrogen atom without neighbors\n",
      "RDKit WARNING: [20:26:47] WARNING: not removing hydrogen atom without neighbors\n",
      "RDKit WARNING: [20:26:47] WARNING: not removing hydrogen atom without neighbors\n",
      "RDKit WARNING: [20:26:47] WARNING: not removing hydrogen atom without neighbors\n",
      "RDKit WARNING: [20:26:47] WARNING: not removing hydrogen atom without neighbors\n",
      "RDKit WARNING: [20:26:47] WARNING: not removing hydrogen atom without neighbors\n",
      "RDKit WARNING: [20:26:47] WARNING: not removing hydrogen atom without neighbors\n",
      "RDKit WARNING: [20:26:47] WARNING: not removing hydrogen atom without neighbors\n",
      "RDKit WARNING: [20:26:47] WARNING: not removing hydrogen atom without neighbors\n",
      "RDKit WARNING: [20:26:47] WARNING: not removing hydrogen atom without neighbors\n",
      "RDKit WARNING: [20:26:47] WARNING: not removing hydrogen atom without neighbors\n",
      "RDKit WARNING: [20:26:47] WARNING: not removing hydrogen atom without neighbors\n",
      "RDKit WARNING: [20:26:47] WARNING: not removing hydrogen atom without neighbors\n",
      "RDKit WARNING: [20:26:47] WARNING: not removing hydrogen atom without neighbors\n",
      "RDKit WARNING: [20:26:47] WARNING: not removing hydrogen atom without neighbors\n",
      "RDKit WARNING: [20:26:47] WARNING: not removing hydrogen atom without neighbors\n",
      "RDKit WARNING: [20:26:47] WARNING: not removing hydrogen atom without neighbors\n",
      "RDKit WARNING: [20:26:47] WARNING: not removing hydrogen atom without neighbors\n",
      "RDKit WARNING: [20:26:47] WARNING: not removing hydrogen atom without neighbors\n",
      "RDKit WARNING: [20:26:47] WARNING: not removing hydrogen atom without neighbors\n",
      "RDKit WARNING: [20:26:47] WARNING: not removing hydrogen atom without neighbors\n",
      "RDKit WARNING: [20:26:47] WARNING: not removing hydrogen atom without neighbors\n",
      "RDKit WARNING: [20:26:47] WARNING: not removing hydrogen atom without neighbors\n",
      "RDKit WARNING: [20:26:47] WARNING: not removing hydrogen atom without neighbors\n",
      "RDKit WARNING: [20:26:47] WARNING: not removing hydrogen atom without neighbors\n",
      "RDKit WARNING: [20:26:47] WARNING: not removing hydrogen atom without neighbors\n",
      "RDKit WARNING: [20:26:47] WARNING: not removing hydrogen atom without neighbors\n",
      "RDKit WARNING: [20:26:47] WARNING: not removing hydrogen atom without neighbors\n",
      "RDKit WARNING: [20:26:47] WARNING: not removing hydrogen atom without neighbors\n",
      "RDKit WARNING: [20:26:47] WARNING: not removing hydrogen atom without neighbors\n",
      "RDKit WARNING: [20:26:47] WARNING: not removing hydrogen atom without neighbors\n",
      "RDKit WARNING: [20:26:47] WARNING: not removing hydrogen atom without neighbors\n",
      "RDKit WARNING: [20:26:47] WARNING: not removing hydrogen atom without neighbors\n",
      "RDKit WARNING: [20:26:47] WARNING: not removing hydrogen atom without neighbors\n",
      "RDKit WARNING: [20:26:47] WARNING: not removing hydrogen atom without neighbors\n",
      "RDKit WARNING: [20:26:47] WARNING: not removing hydrogen atom without neighbors\n",
      "RDKit WARNING: [20:26:47] WARNING: not removing hydrogen atom without neighbors\n",
      "RDKit WARNING: [20:26:47] WARNING: not removing hydrogen atom without neighbors\n",
      "RDKit WARNING: [20:26:47] WARNING: not removing hydrogen atom without neighbors\n",
      "RDKit WARNING: [20:26:47] WARNING: not removing hydrogen atom without neighbors\n",
      "RDKit WARNING: [20:26:47] WARNING: not removing hydrogen atom without neighbors\n"
     ]
    }
   ],
   "source": [
    "df['Mol'] = df.SMILES.progress_apply(Chem.MolFromSmiles)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "incident-mouth",
   "metadata": {},
   "outputs": [],
   "source": [
    "query = Chem.MolFromSmarts(\"c1ccccn1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "inner-teacher",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<rdkit.Chem.rdchem.Mol at 0x15f552a00>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "defined-economics",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "dfaf98c63bb043139bae7d5af5f5fc75",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/9982 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['pyridine'] = [x.HasSubstructMatch(query) for x in tqdm(df.Mol)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "postal-direction",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hits = df.query(\"pyridine\").head(10)\n",
    "MolsToGridImage(hits.Mol,legends=hits.ID.to_list(),molsPerRow=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "democratic-nightmare",
   "metadata": {},
   "outputs": [],
   "source": [
    "match_list = [x.GetSubstructMatch(query) for x in hits.Mol]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "infectious-packet",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MolsToGridImage(hits.Mol,legends=hits.ID.to_list(),molsPerRow=3,highlightAtomLists=match_list,subImgSize=(400, 400))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "posted-bulgarian",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Group</th>\n",
       "      <th>Size</th>\n",
       "      <th>Mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>G1</td>\n",
       "      <td>7746</td>\n",
       "      <td>-2.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>G2</td>\n",
       "      <td>235</td>\n",
       "      <td>-4.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>G3</td>\n",
       "      <td>1182</td>\n",
       "      <td>-2.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>G4</td>\n",
       "      <td>183</td>\n",
       "      <td>-3.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>G5</td>\n",
       "      <td>636</td>\n",
       "      <td>-2.68</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Group  Size  Mean\n",
       "0    G1  7746 -2.86\n",
       "1    G2   235 -4.05\n",
       "2    G3  1182 -2.79\n",
       "3    G4   183 -3.81\n",
       "4    G5   636 -2.68"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res = []\n",
    "for k,v in df.groupby(\"Group\"):\n",
    "    res.append([k,len(v),v.Solubility.mean()])\n",
    "res_df = pd.DataFrame(res,columns=[\"Group\",\"Size\",\"Mean\"]).round(decimals=2)\n",
    "res_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "owned-ceremony",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Solubility'}, xlabel='Group'>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.boxplot(column=\"Solubility\",by=\"Group\",figsize=(8,6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "operational-display",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Group', ylabel='Solubility'>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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uuZG0tbUVXW40c+bMYc6cOaHLkDLZ+LTQ9aujo8P7+vqC7b/QicfNmzcHqCS8FStWMDw8PLbc0tLCpk2bAlYUzs6dO/n4xz8+tnzjjTfS3t4esCJJitGWUOgLFMxsq7t3FFqnloPE6uyzz560fO655waqJLzTTz99rLXQ1tamYJC6onCQWD399NOTlieeg2hEV1xxBU1NTXzyk58s/WKRBNEJaYlVNpstutxotmzZgrtz1113cdZZuvE/bdJ8cl4tB4mVTsKOGxwcZP369bg769evb+jLemWyejg5r5aDxOrzn//8pJOw11xzTcBqwurp6WH0go+RkRF6enr49Kc/HbgqiVOaL7FVy0FidcIJJ0xaPv744wNVEl5vby+HDx8GckOKbNiwIXBFIuVTOEispg6Z0chDaKxcuZJZs2YBMGvWLFatWhW4IpHyKRwkVhs3bpy03NvbG6iS8Do7OzEzAJqamujs7AxckUj5FA4Sq9EPw+mWG0lraysXX3wxZsbFF1+cmhFqpTEoHCRWF1xwwaTlCy+cdrrwhtDZ2cmZZ56pVoPUnSDhYGYfNrMdZjZiZh1T1l1lZv1m9piZXRSiPqncmjVrii43mtbWVr761a+q1SB1J1TL4SHgQ8CkAYjM7E3ApcAZwLuBvzOz5tqXNzNNTU1FlxtJa2sr8+bNA2DevHn6UBSpU0E+xdz9EXd/rMCqDwA3ufshd/8p0A+cU9vqZm5kZKTociPZuXMnBw8eBODgwYMV3T0qIuEl7U/cRcDEwXmy+ecizGy1mfWZWV8jD5GdNNdff/2k5euuuy5QJSJyNKp2h7SZbQQWFlh1tbvfMt1mBZ4rOKa4u3cD3ZAbsruiIiV2u3btKrosIvWhauHg7pVcppIFXjdhOQPsiaei6mlububIkSOTlhtVJpOZNNheJpMJWI2IVCpp3Ur/AVxqZsea2RJgKfCTwDWVNDUMGjkcps5ZsHTp0kCViMjRCHUp6wfNLAu8A7jVzG4HcPcdwHeAh4H/BP7Q3Y9M/5OS4ZRTTim63Eh+8pPJWX7PPfcEqkREjkaoq5W+5+4Zdz/W3U9294smrPszd3+9u7/B3deHqG+mnnvuuaLLjWTZsmWTlgtNoSoiyZe0bqW6NPUD8Pzzzw9USXiHDh0quiwi9UHhILG6++67Jy1v2bIlUCUicjQUDjGY+gG4efPmaV6ZfqOT20y33GgGBwe58sorNQuc1B2FQwxWrlxJS0vuquCWlpaGHrd/6kB7K1euDFRJMvT09PDAAw/Q09MTuhSRGVE4xKCzs3NsPKXm5uaGHoFzzZo1Y8eiqampoQfe0xzSUs8UDjHQuP3jWltbx1oLq1atauhjUWgOaZF6oXCIicbtH7dmzRre8pa3NHSrATSHtNQ3hUNMNG7/OB2LHM0hLfVM4SBSJZpDWuqZwiEmumRRpmptbWX58uUALF++vOFbUlJfFA4xWbduHffffz/r1q0LXYqIyFFTOMRgcHCQ3t5eADZs2KDWgwC598WmTZsA2LRpk94XUlcUDjFYt27d2NSgIyMjaj0IkLuUdXh4GMhdraRLWaWeKBxicMcdd0xa3rhxY6BKJEk2bNgwdp+Du3P77bcHrkikfAqHGGg8ISnk5JNPLroskmQKhxhoPCEpRPN8SD1TOMRA4wlJIatWrRq7z8HMuOiii0psIZIcoaYJ/bCZ7TCzETPrmPD8SjPbamYP5r+vCFHfTLW2to5NDXrKKac0/PXsuucjp7Ozc9Id0roJTupJqJbDQ8CHgKkTHwwC73P3XwY6gW/WurBKDA4OjnUZ7N27t+E/FDVMdc7EARnf8573NPwfDVJfQs0h/Yi7P1bg+W3uvie/uAOYbWbH1ra6mZv4IejuDf2hqGGqJ9OAjFKvknzO4RJgm7sXnITYzFabWZ+Z9Q0MDNS4tMk0+uY4DVM9mQYhlHpVtXAws41m9lCBrw+Use0ZwF8A057Zdfdud+9w944FCxbEWfqMafTNcQpKkXSoWji4+4Xu/uYCX7cU287MMsD3gN919yeqVV+cNPrmOAWlSDokqlvJzOYDtwJXuft/BS6nbJoJbpyCUiQdQl3K+kEzywLvAG41s9FxBa4A2oE/NbPt+a+TQtQ4UzrxmKOgFEkHS8NQDx0dHd7X1xe6DMkbHBzk2muv5Qtf+ILCQSTBzGyru3cUWtdS62Ik/Uav0BGR+pWocw4iIpIMCoeYaMgIEUkThUNMNGSEiKSJwiEGGjJCRNJG4RCDnp6esWlCjxw5otaDiNQ9hUMMent7x+YKHh4e1pARIlL3FA4xWLZs2aTl8847L1AlIiLxUDiIiEiEwiEGW7ZsmbS8efPUOYxEROqLwiEGK1eupKUld7N5S0uLRiIVkbqncIhBZ2cnTU25Q9nc3Nzwg++JSP1TOMRAI5GKSNpo4L2YdHZ2smvXLrUaRCQVFA4x0UikIpIm6lYSEZEIhYOIiEQoHEREJELhICIiEamYQ9rMBoCnQtcBtAKDoYtICB2LcToW43QsxiXhWJzq7gsKrUhFOCSFmfVNN1l3o9GxGKdjMU7HYlzSj4W6lUREJELhICIiEQqHeHWHLiBBdCzG6ViM07EYl+hjoXMOIiISoZaDiIhEKBxERCRC4VABMzvZzP7FzJ40s61m9t9m9kEzO9HM7jSzA2b2t6HrrIUix+IcM9ue/7rfzD4YutZqK3Is2szs5QnH4+uha6226Y7FhPWL878nfxyyzloo8r5YmV9+MP99RehaJ9KorDNkZgZ8H+hx99/OP3cq8H7gFeBPgTfnv1KtxLG4Hehw92Ez+0XgfjP7gbsPByu4ikoci23AE+7+1mAF1lCJYzHqK8D62ldXWyWOxd3A+9x9j5m9mdzvzKJQtU6lcJi5FcCr7j7215+7PwWMjtd9t5m1B6ms9kodi1GzgbRf+TDtsTCztmBVhVH0fWFmvw48CRwMUl1tlfs7sgOYbWbHuvuhWhY4HXUrzdwZwH2hi0iIosfCzM41sx3Ag8AfpLXVkFfqfbHEzLaZ2V1mtqxWRQUy7bEws3nAZ4Bra1pROOV+XlwCbEtKMIBaDkfNzL4GvJPcXwdnh64npKnHwt3vAc4ws18Cesxsvbu/ErbK2ph4LPLfF7v7PjM7C/i+mZ3h7i8GLbJGphyLu4CvuPuBXI9LYyn0eWFmZwB/AawKWdtUajnM3A7g7aML7v6HwAVAwcGrUq6sY+Huj5DrQkjzeZhpj4W7H3L3ffnntwJPAKcHqbI2ir0vzgX+0sx2AZ8CPmdmVwSosVaK/o6YWQb4HvC77v5EkAqnoXCYuU3k+gY/MeG5uaGKCWzaY2FmS8ysJf/4VOANwK6aV1g7xY7FAjNrzj8+DVhKrs89raY9Fu6+zN3b3L0N+Gvgz909zVf2FXtfzAduBa5y9/8KUFtRukO6Avmrb75C7q+gAXJ/FX/d3f8t/xfRa4BjgP3AKnd/OFCpVTfdsSD37/8scBgYAa5z9+8HKrMmihyLYeC6/PcjwP919x+EqrMWiv2OTHjNF4AD7v7FIEXWSJH3xVLgKuDxCS9f5e57a15kAQoHERGJULeSiIhEKBxERCRC4SAiIhEKBxERiVA4iIhIhMJBpEylRhoVSROFg0gZJoyuudndT3P3s4BLgcyU12lIGkkF3ecgUgYzuwC4xt3PL7DuMuDXyI0+Ow/4DeBG4DRgCFjt7g9MvenLzB4C3pv/Mf8J3AO8DdhJbjiFoWr+m0SKUctBpDylRtd8B9Dp7ivIjTi6zd3PBD4HfKOMn/8GoDu/zYvA/zrKekWOisJBpAJm9rX8DHf35p/qdffn84/fCXwTwN03ASea2WtL/MinJ4yv8638zxAJRuEgUp5SI9BOnLim0FjUTm5spYm/c7OnrJ/6epFgFA4i5ZnJaLybgd8BMLN3AYP5uRt2kQ8YM3s7sGTCNovN7B35xx8hN4WkSDA6IS1SpiKja84hN1/2FfnXnQD8E7kP/4knpOcAtwAnAfeS6zq6OP/jbyMXKr9KbpTOj+mEtISkcBAJLD/H9A/dPc2TIUmdUbeSiIhEqOUgIiIRajmIiEiEwkFERCIUDiIiEqFwEBGRCIWDiIhE/H/vFvY8YlX/1wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=\"Group\",y=\"Solubility\",data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ecological-partner",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}