2023_06_23-Hazemann-PandasAI.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "3d551a5e",
   "metadata": {},
   "source": [
    "# Talk to your data with PandasAI"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33344618",
   "metadata": {},
   "source": [
    "\n",
    "<img src=\"https://github.com/gventuri/pandas-ai/blob/main/images/pandas-ai.png?raw=true\" alt=\"Pandasai\" />"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c69b0304",
   "metadata": {},
   "source": [
    "Source: https://github.com/gventuri/pandas-ai"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77140d97",
   "metadata": {},
   "source": [
    "## Prerequisite:\n",
    "\n",
    "conda create --name pandasai python=3.10<br>\n",
    "conda activate pandasai<br>\n",
    "pip install pandasai<br>\n",
    "conda install jupyter<br>\n",
    "create an OpenAI account and get your own API key"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "38c31c79",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from pandasai import PandasAI\n",
    "\n",
    "#Instantiate a LLM\n",
    "from pandasai.llm.openai import OpenAI\n",
    "llm = OpenAI(api_token=\"replace_with_your_key\")\n",
    "pandas_ai = PandasAI(llm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7f275d41",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('~/data/hERG_ChEMBL_simple.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "09f17433",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Molecule ChEMBL ID</th>\n",
       "      <th>Molecule Name</th>\n",
       "      <th>Molecular Weight</th>\n",
       "      <th>AlogP</th>\n",
       "      <th>Smiles</th>\n",
       "      <th>hERG_IC50_nM</th>\n",
       "      <th>Assay Type</th>\n",
       "      <th>Target Name</th>\n",
       "      <th>Document Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CHEMBL597737</td>\n",
       "      <td>NaN</td>\n",
       "      <td>338.39</td>\n",
       "      <td>3.31</td>\n",
       "      <td>Fc1ccccc1-c1ccc2oc(N3CCN4CCC3CC4)nc2n1</td>\n",
       "      <td>2800.00</td>\n",
       "      <td>B</td>\n",
       "      <td>HERG</td>\n",
       "      <td>2010.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CHEMBL403181</td>\n",
       "      <td>NaN</td>\n",
       "      <td>512.68</td>\n",
       "      <td>5.63</td>\n",
       "      <td>CCc1noc2cc3c(cc12)CCN(CCCSc1nnc(-c2cccc4nc(C)c...</td>\n",
       "      <td>1000.00</td>\n",
       "      <td>B</td>\n",
       "      <td>HERG</td>\n",
       "      <td>2008.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CHEMBL257946</td>\n",
       "      <td>NaN</td>\n",
       "      <td>498.66</td>\n",
       "      <td>5.37</td>\n",
       "      <td>Cc1ccc2c(-c3nnc(SCCCN4CCc5cc6onc(C)c6cc5CC4)n3...</td>\n",
       "      <td>794.33</td>\n",
       "      <td>B</td>\n",
       "      <td>HERG</td>\n",
       "      <td>2008.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CHEMBL555347</td>\n",
       "      <td>NaN</td>\n",
       "      <td>417.39</td>\n",
       "      <td>4.56</td>\n",
       "      <td>O=C(Nc1ccc(C(F)(F)F)cc1)N1CCC(c2nc(-c3ccccn3)n...</td>\n",
       "      <td>8000.00</td>\n",
       "      <td>B</td>\n",
       "      <td>HERG</td>\n",
       "      <td>2009.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CHEMBL241330</td>\n",
       "      <td>NaN</td>\n",
       "      <td>450.99</td>\n",
       "      <td>3.54</td>\n",
       "      <td>CCOc1cc(CN2CCC(NC(=O)c3cccc(S(C)(=O)=O)c3)CC2)...</td>\n",
       "      <td>1300.00</td>\n",
       "      <td>B</td>\n",
       "      <td>HERG</td>\n",
       "      <td>2007.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9497</th>\n",
       "      <td>CHEMBL5200451</td>\n",
       "      <td>NaN</td>\n",
       "      <td>434.43</td>\n",
       "      <td>3.95</td>\n",
       "      <td>Cc1onc(-c2ccc(F)cc2)c1COc1ccc2c(n1)CCN(C(=O)c1...</td>\n",
       "      <td>6400.00</td>\n",
       "      <td>T</td>\n",
       "      <td>HERG</td>\n",
       "      <td>2022.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9498</th>\n",
       "      <td>CHEMBL5174560</td>\n",
       "      <td>NaN</td>\n",
       "      <td>410.45</td>\n",
       "      <td>3.8</td>\n",
       "      <td>Cc1onc(-c2ccc(F)cc2)c1COc1ccc2c(n1)CCN(C(=O)N(...</td>\n",
       "      <td>8300.00</td>\n",
       "      <td>T</td>\n",
       "      <td>HERG</td>\n",
       "      <td>2022.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9499</th>\n",
       "      <td>CHEMBL5176630</td>\n",
       "      <td>NaN</td>\n",
       "      <td>452.49</td>\n",
       "      <td>3.57</td>\n",
       "      <td>Cc1onc(-c2ccc(F)cc2)c1COc1ccc2c(n1)CCN(C(=O)N1...</td>\n",
       "      <td>9100.00</td>\n",
       "      <td>T</td>\n",
       "      <td>HERG</td>\n",
       "      <td>2022.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9500</th>\n",
       "      <td>CHEMBL5179688</td>\n",
       "      <td>NaN</td>\n",
       "      <td>367.38</td>\n",
       "      <td>2.55</td>\n",
       "      <td>CC(=O)N1Cc2ccc(OCc3c(C)nnn3-c3ccc(F)cc3)nc2C1</td>\n",
       "      <td>30000.00</td>\n",
       "      <td>T</td>\n",
       "      <td>HERG</td>\n",
       "      <td>2022.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9501</th>\n",
       "      <td>CHEMBL5206831</td>\n",
       "      <td>NaN</td>\n",
       "      <td>464.50</td>\n",
       "      <td>2.05</td>\n",
       "      <td>Cc1nnn(-c2ccc(F)cc2)c1COc1ccc2c(n1)CCN(C(=O)C1...</td>\n",
       "      <td>30000.00</td>\n",
       "      <td>T</td>\n",
       "      <td>HERG</td>\n",
       "      <td>2022.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9502 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Molecule ChEMBL ID Molecule Name  Molecular Weight AlogP  \\\n",
       "0          CHEMBL597737           NaN            338.39  3.31   \n",
       "1          CHEMBL403181           NaN            512.68  5.63   \n",
       "2          CHEMBL257946           NaN            498.66  5.37   \n",
       "3          CHEMBL555347           NaN            417.39  4.56   \n",
       "4          CHEMBL241330           NaN            450.99  3.54   \n",
       "...                 ...           ...               ...   ...   \n",
       "9497      CHEMBL5200451           NaN            434.43  3.95   \n",
       "9498      CHEMBL5174560           NaN            410.45   3.8   \n",
       "9499      CHEMBL5176630           NaN            452.49  3.57   \n",
       "9500      CHEMBL5179688           NaN            367.38  2.55   \n",
       "9501      CHEMBL5206831           NaN            464.50  2.05   \n",
       "\n",
       "                                                 Smiles  hERG_IC50_nM  \\\n",
       "0                Fc1ccccc1-c1ccc2oc(N3CCN4CCC3CC4)nc2n1       2800.00   \n",
       "1     CCc1noc2cc3c(cc12)CCN(CCCSc1nnc(-c2cccc4nc(C)c...       1000.00   \n",
       "2     Cc1ccc2c(-c3nnc(SCCCN4CCc5cc6onc(C)c6cc5CC4)n3...        794.33   \n",
       "3     O=C(Nc1ccc(C(F)(F)F)cc1)N1CCC(c2nc(-c3ccccn3)n...       8000.00   \n",
       "4     CCOc1cc(CN2CCC(NC(=O)c3cccc(S(C)(=O)=O)c3)CC2)...       1300.00   \n",
       "...                                                 ...           ...   \n",
       "9497  Cc1onc(-c2ccc(F)cc2)c1COc1ccc2c(n1)CCN(C(=O)c1...       6400.00   \n",
       "9498  Cc1onc(-c2ccc(F)cc2)c1COc1ccc2c(n1)CCN(C(=O)N(...       8300.00   \n",
       "9499  Cc1onc(-c2ccc(F)cc2)c1COc1ccc2c(n1)CCN(C(=O)N1...       9100.00   \n",
       "9500      CC(=O)N1Cc2ccc(OCc3c(C)nnn3-c3ccc(F)cc3)nc2C1      30000.00   \n",
       "9501  Cc1nnn(-c2ccc(F)cc2)c1COc1ccc2c(n1)CCN(C(=O)C1...      30000.00   \n",
       "\n",
       "     Assay Type Target Name  Document Year  \n",
       "0             B        HERG         2010.0  \n",
       "1             B        HERG         2008.0  \n",
       "2             B        HERG         2008.0  \n",
       "3             B        HERG         2009.0  \n",
       "4             B        HERG         2007.0  \n",
       "...         ...         ...            ...  \n",
       "9497          T        HERG         2022.0  \n",
       "9498          T        HERG         2022.0  \n",
       "9499          T        HERG         2022.0  \n",
       "9500          T        HERG         2022.0  \n",
       "9501          T        HERG         2022.0  \n",
       "\n",
       "[9502 rows x 9 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e1329841",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9502"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pandas_ai.run(df, prompt='How many rows in df?')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "69f459cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "''"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pandas_ai.run(df, prompt='Drop Molecule Name column')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "59f7ce51",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Molecule ChEMBL ID</th>\n",
       "      <th>Molecular Weight</th>\n",
       "      <th>AlogP</th>\n",
       "      <th>Smiles</th>\n",
       "      <th>hERG_IC50_nM</th>\n",
       "      <th>Assay Type</th>\n",
       "      <th>Target Name</th>\n",
       "      <th>Document Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CHEMBL597737</td>\n",
       "      <td>338.39</td>\n",
       "      <td>3.31</td>\n",
       "      <td>Fc1ccccc1-c1ccc2oc(N3CCN4CCC3CC4)nc2n1</td>\n",
       "      <td>2800.00</td>\n",
       "      <td>B</td>\n",
       "      <td>HERG</td>\n",
       "      <td>2010.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CHEMBL403181</td>\n",
       "      <td>512.68</td>\n",
       "      <td>5.63</td>\n",
       "      <td>CCc1noc2cc3c(cc12)CCN(CCCSc1nnc(-c2cccc4nc(C)c...</td>\n",
       "      <td>1000.00</td>\n",
       "      <td>B</td>\n",
       "      <td>HERG</td>\n",
       "      <td>2008.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CHEMBL257946</td>\n",
       "      <td>498.66</td>\n",
       "      <td>5.37</td>\n",
       "      <td>Cc1ccc2c(-c3nnc(SCCCN4CCc5cc6onc(C)c6cc5CC4)n3...</td>\n",
       "      <td>794.33</td>\n",
       "      <td>B</td>\n",
       "      <td>HERG</td>\n",
       "      <td>2008.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CHEMBL555347</td>\n",
       "      <td>417.39</td>\n",
       "      <td>4.56</td>\n",
       "      <td>O=C(Nc1ccc(C(F)(F)F)cc1)N1CCC(c2nc(-c3ccccn3)n...</td>\n",
       "      <td>8000.00</td>\n",
       "      <td>B</td>\n",
       "      <td>HERG</td>\n",
       "      <td>2009.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CHEMBL241330</td>\n",
       "      <td>450.99</td>\n",
       "      <td>3.54</td>\n",
       "      <td>CCOc1cc(CN2CCC(NC(=O)c3cccc(S(C)(=O)=O)c3)CC2)...</td>\n",
       "      <td>1300.00</td>\n",
       "      <td>B</td>\n",
       "      <td>HERG</td>\n",
       "      <td>2007.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9497</th>\n",
       "      <td>CHEMBL5200451</td>\n",
       "      <td>434.43</td>\n",
       "      <td>3.95</td>\n",
       "      <td>Cc1onc(-c2ccc(F)cc2)c1COc1ccc2c(n1)CCN(C(=O)c1...</td>\n",
       "      <td>6400.00</td>\n",
       "      <td>T</td>\n",
       "      <td>HERG</td>\n",
       "      <td>2022.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9498</th>\n",
       "      <td>CHEMBL5174560</td>\n",
       "      <td>410.45</td>\n",
       "      <td>3.8</td>\n",
       "      <td>Cc1onc(-c2ccc(F)cc2)c1COc1ccc2c(n1)CCN(C(=O)N(...</td>\n",
       "      <td>8300.00</td>\n",
       "      <td>T</td>\n",
       "      <td>HERG</td>\n",
       "      <td>2022.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9499</th>\n",
       "      <td>CHEMBL5176630</td>\n",
       "      <td>452.49</td>\n",
       "      <td>3.57</td>\n",
       "      <td>Cc1onc(-c2ccc(F)cc2)c1COc1ccc2c(n1)CCN(C(=O)N1...</td>\n",
       "      <td>9100.00</td>\n",
       "      <td>T</td>\n",
       "      <td>HERG</td>\n",
       "      <td>2022.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9500</th>\n",
       "      <td>CHEMBL5179688</td>\n",
       "      <td>367.38</td>\n",
       "      <td>2.55</td>\n",
       "      <td>CC(=O)N1Cc2ccc(OCc3c(C)nnn3-c3ccc(F)cc3)nc2C1</td>\n",
       "      <td>30000.00</td>\n",
       "      <td>T</td>\n",
       "      <td>HERG</td>\n",
       "      <td>2022.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9501</th>\n",
       "      <td>CHEMBL5206831</td>\n",
       "      <td>464.50</td>\n",
       "      <td>2.05</td>\n",
       "      <td>Cc1nnn(-c2ccc(F)cc2)c1COc1ccc2c(n1)CCN(C(=O)C1...</td>\n",
       "      <td>30000.00</td>\n",
       "      <td>T</td>\n",
       "      <td>HERG</td>\n",
       "      <td>2022.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9502 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Molecule ChEMBL ID  Molecular Weight AlogP  \\\n",
       "0          CHEMBL597737            338.39  3.31   \n",
       "1          CHEMBL403181            512.68  5.63   \n",
       "2          CHEMBL257946            498.66  5.37   \n",
       "3          CHEMBL555347            417.39  4.56   \n",
       "4          CHEMBL241330            450.99  3.54   \n",
       "...                 ...               ...   ...   \n",
       "9497      CHEMBL5200451            434.43  3.95   \n",
       "9498      CHEMBL5174560            410.45   3.8   \n",
       "9499      CHEMBL5176630            452.49  3.57   \n",
       "9500      CHEMBL5179688            367.38  2.55   \n",
       "9501      CHEMBL5206831            464.50  2.05   \n",
       "\n",
       "                                                 Smiles  hERG_IC50_nM  \\\n",
       "0                Fc1ccccc1-c1ccc2oc(N3CCN4CCC3CC4)nc2n1       2800.00   \n",
       "1     CCc1noc2cc3c(cc12)CCN(CCCSc1nnc(-c2cccc4nc(C)c...       1000.00   \n",
       "2     Cc1ccc2c(-c3nnc(SCCCN4CCc5cc6onc(C)c6cc5CC4)n3...        794.33   \n",
       "3     O=C(Nc1ccc(C(F)(F)F)cc1)N1CCC(c2nc(-c3ccccn3)n...       8000.00   \n",
       "4     CCOc1cc(CN2CCC(NC(=O)c3cccc(S(C)(=O)=O)c3)CC2)...       1300.00   \n",
       "...                                                 ...           ...   \n",
       "9497  Cc1onc(-c2ccc(F)cc2)c1COc1ccc2c(n1)CCN(C(=O)c1...       6400.00   \n",
       "9498  Cc1onc(-c2ccc(F)cc2)c1COc1ccc2c(n1)CCN(C(=O)N(...       8300.00   \n",
       "9499  Cc1onc(-c2ccc(F)cc2)c1COc1ccc2c(n1)CCN(C(=O)N1...       9100.00   \n",
       "9500      CC(=O)N1Cc2ccc(OCc3c(C)nnn3-c3ccc(F)cc3)nc2C1      30000.00   \n",
       "9501  Cc1nnn(-c2ccc(F)cc2)c1COc1ccc2c(n1)CCN(C(=O)C1...      30000.00   \n",
       "\n",
       "     Assay Type Target Name  Document Year  \n",
       "0             B        HERG         2010.0  \n",
       "1             B        HERG         2008.0  \n",
       "2             B        HERG         2008.0  \n",
       "3             B        HERG         2009.0  \n",
       "4             B        HERG         2007.0  \n",
       "...         ...         ...            ...  \n",
       "9497          T        HERG         2022.0  \n",
       "9498          T        HERG         2022.0  \n",
       "9499          T        HERG         2022.0  \n",
       "9500          T        HERG         2022.0  \n",
       "9501          T        HERG         2022.0  \n",
       "\n",
       "[9502 rows x 8 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1dac2452",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pandas_ai.run(df, prompt='Plot the distribution of rows over Document Year')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9d78bc7c",
   "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>Molecule ChEMBL ID</th>\n",
       "      <th>Molecular Weight</th>\n",
       "      <th>AlogP</th>\n",
       "      <th>Smiles</th>\n",
       "      <th>hERG_IC50_nM</th>\n",
       "      <th>Assay Type</th>\n",
       "      <th>Target Name</th>\n",
       "      <th>Document Year</th>\n",
       "      <th>Risk Level</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CHEMBL597737</td>\n",
       "      <td>338.39</td>\n",
       "      <td>3.31</td>\n",
       "      <td>Fc1ccccc1-c1ccc2oc(N3CCN4CCC3CC4)nc2n1</td>\n",
       "      <td>2800.00</td>\n",
       "      <td>B</td>\n",
       "      <td>HERG</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>risky</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CHEMBL403181</td>\n",
       "      <td>512.68</td>\n",
       "      <td>5.63</td>\n",
       "      <td>CCc1noc2cc3c(cc12)CCN(CCCSc1nnc(-c2cccc4nc(C)c...</td>\n",
       "      <td>1000.00</td>\n",
       "      <td>B</td>\n",
       "      <td>HERG</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>risky</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CHEMBL257946</td>\n",
       "      <td>498.66</td>\n",
       "      <td>5.37</td>\n",
       "      <td>Cc1ccc2c(-c3nnc(SCCCN4CCc5cc6onc(C)c6cc5CC4)n3...</td>\n",
       "      <td>794.33</td>\n",
       "      <td>B</td>\n",
       "      <td>HERG</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>risky</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CHEMBL555347</td>\n",
       "      <td>417.39</td>\n",
       "      <td>4.56</td>\n",
       "      <td>O=C(Nc1ccc(C(F)(F)F)cc1)N1CCC(c2nc(-c3ccccn3)n...</td>\n",
       "      <td>8000.00</td>\n",
       "      <td>B</td>\n",
       "      <td>HERG</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>moderate_risk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CHEMBL241330</td>\n",
       "      <td>450.99</td>\n",
       "      <td>3.54</td>\n",
       "      <td>CCOc1cc(CN2CCC(NC(=O)c3cccc(S(C)(=O)=O)c3)CC2)...</td>\n",
       "      <td>1300.00</td>\n",
       "      <td>B</td>\n",
       "      <td>HERG</td>\n",
       "      <td>2007.0</td>\n",
       "      <td>risky</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9497</th>\n",
       "      <td>CHEMBL5200451</td>\n",
       "      <td>434.43</td>\n",
       "      <td>3.95</td>\n",
       "      <td>Cc1onc(-c2ccc(F)cc2)c1COc1ccc2c(n1)CCN(C(=O)c1...</td>\n",
       "      <td>6400.00</td>\n",
       "      <td>T</td>\n",
       "      <td>HERG</td>\n",
       "      <td>2022.0</td>\n",
       "      <td>moderate_risk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9498</th>\n",
       "      <td>CHEMBL5174560</td>\n",
       "      <td>410.45</td>\n",
       "      <td>3.8</td>\n",
       "      <td>Cc1onc(-c2ccc(F)cc2)c1COc1ccc2c(n1)CCN(C(=O)N(...</td>\n",
       "      <td>8300.00</td>\n",
       "      <td>T</td>\n",
       "      <td>HERG</td>\n",
       "      <td>2022.0</td>\n",
       "      <td>moderate_risk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9499</th>\n",
       "      <td>CHEMBL5176630</td>\n",
       "      <td>452.49</td>\n",
       "      <td>3.57</td>\n",
       "      <td>Cc1onc(-c2ccc(F)cc2)c1COc1ccc2c(n1)CCN(C(=O)N1...</td>\n",
       "      <td>9100.00</td>\n",
       "      <td>T</td>\n",
       "      <td>HERG</td>\n",
       "      <td>2022.0</td>\n",
       "      <td>moderate_risk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9500</th>\n",
       "      <td>CHEMBL5179688</td>\n",
       "      <td>367.38</td>\n",
       "      <td>2.55</td>\n",
       "      <td>CC(=O)N1Cc2ccc(OCc3c(C)nnn3-c3ccc(F)cc3)nc2C1</td>\n",
       "      <td>30000.00</td>\n",
       "      <td>T</td>\n",
       "      <td>HERG</td>\n",
       "      <td>2022.0</td>\n",
       "      <td>no_risk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9501</th>\n",
       "      <td>CHEMBL5206831</td>\n",
       "      <td>464.50</td>\n",
       "      <td>2.05</td>\n",
       "      <td>Cc1nnn(-c2ccc(F)cc2)c1COc1ccc2c(n1)CCN(C(=O)C1...</td>\n",
       "      <td>30000.00</td>\n",
       "      <td>T</td>\n",
       "      <td>HERG</td>\n",
       "      <td>2022.0</td>\n",
       "      <td>no_risk</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9502 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Molecule ChEMBL ID  Molecular Weight AlogP  \\\n",
       "0          CHEMBL597737            338.39  3.31   \n",
       "1          CHEMBL403181            512.68  5.63   \n",
       "2          CHEMBL257946            498.66  5.37   \n",
       "3          CHEMBL555347            417.39  4.56   \n",
       "4          CHEMBL241330            450.99  3.54   \n",
       "...                 ...               ...   ...   \n",
       "9497      CHEMBL5200451            434.43  3.95   \n",
       "9498      CHEMBL5174560            410.45   3.8   \n",
       "9499      CHEMBL5176630            452.49  3.57   \n",
       "9500      CHEMBL5179688            367.38  2.55   \n",
       "9501      CHEMBL5206831            464.50  2.05   \n",
       "\n",
       "                                                 Smiles  hERG_IC50_nM  \\\n",
       "0                Fc1ccccc1-c1ccc2oc(N3CCN4CCC3CC4)nc2n1       2800.00   \n",
       "1     CCc1noc2cc3c(cc12)CCN(CCCSc1nnc(-c2cccc4nc(C)c...       1000.00   \n",
       "2     Cc1ccc2c(-c3nnc(SCCCN4CCc5cc6onc(C)c6cc5CC4)n3...        794.33   \n",
       "3     O=C(Nc1ccc(C(F)(F)F)cc1)N1CCC(c2nc(-c3ccccn3)n...       8000.00   \n",
       "4     CCOc1cc(CN2CCC(NC(=O)c3cccc(S(C)(=O)=O)c3)CC2)...       1300.00   \n",
       "...                                                 ...           ...   \n",
       "9497  Cc1onc(-c2ccc(F)cc2)c1COc1ccc2c(n1)CCN(C(=O)c1...       6400.00   \n",
       "9498  Cc1onc(-c2ccc(F)cc2)c1COc1ccc2c(n1)CCN(C(=O)N(...       8300.00   \n",
       "9499  Cc1onc(-c2ccc(F)cc2)c1COc1ccc2c(n1)CCN(C(=O)N1...       9100.00   \n",
       "9500      CC(=O)N1Cc2ccc(OCc3c(C)nnn3-c3ccc(F)cc3)nc2C1      30000.00   \n",
       "9501  Cc1nnn(-c2ccc(F)cc2)c1COc1ccc2c(n1)CCN(C(=O)C1...      30000.00   \n",
       "\n",
       "     Assay Type Target Name  Document Year     Risk Level  \n",
       "0             B        HERG         2010.0          risky  \n",
       "1             B        HERG         2008.0          risky  \n",
       "2             B        HERG         2008.0          risky  \n",
       "3             B        HERG         2009.0  moderate_risk  \n",
       "4             B        HERG         2007.0          risky  \n",
       "...         ...         ...            ...            ...  \n",
       "9497          T        HERG         2022.0  moderate_risk  \n",
       "9498          T        HERG         2022.0  moderate_risk  \n",
       "9499          T        HERG         2022.0  moderate_risk  \n",
       "9500          T        HERG         2022.0        no_risk  \n",
       "9501          T        HERG         2022.0        no_risk  \n",
       "\n",
       "[9502 rows x 9 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pandas_ai.run(df, prompt='Label in a new column with herg_IC50_nM below 5000 as risky, herg_IC50_nM above 5000 but below 10000 as moderate_risk and herg_IC50_nM above 10000 as no_risk')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4c0ea09b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pandas_ai.run(df, prompt='Plot the distribution of Risk Level and color the bars with different colors')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4ca2e27f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "''"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pandas_ai.run(df, prompt='Convert hERG_IC50_nM to pIC50')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c4c21f65",
   "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>Molecule ChEMBL ID</th>\n",
       "      <th>Molecular Weight</th>\n",
       "      <th>AlogP</th>\n",
       "      <th>Smiles</th>\n",
       "      <th>hERG_IC50_nM</th>\n",
       "      <th>Assay Type</th>\n",
       "      <th>Target Name</th>\n",
       "      <th>Document Year</th>\n",
       "      <th>Risk Level</th>\n",
       "      <th>pIC50</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CHEMBL597737</td>\n",
       "      <td>338.39</td>\n",
       "      <td>3.31</td>\n",
       "      <td>Fc1ccccc1-c1ccc2oc(N3CCN4CCC3CC4)nc2n1</td>\n",
       "      <td>2800.00</td>\n",
       "      <td>B</td>\n",
       "      <td>HERG</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>risky</td>\n",
       "      <td>5.552842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CHEMBL403181</td>\n",
       "      <td>512.68</td>\n",
       "      <td>5.63</td>\n",
       "      <td>CCc1noc2cc3c(cc12)CCN(CCCSc1nnc(-c2cccc4nc(C)c...</td>\n",
       "      <td>1000.00</td>\n",
       "      <td>B</td>\n",
       "      <td>HERG</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>risky</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CHEMBL257946</td>\n",
       "      <td>498.66</td>\n",
       "      <td>5.37</td>\n",
       "      <td>Cc1ccc2c(-c3nnc(SCCCN4CCc5cc6onc(C)c6cc5CC4)n3...</td>\n",
       "      <td>794.33</td>\n",
       "      <td>B</td>\n",
       "      <td>HERG</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>risky</td>\n",
       "      <td>6.099999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CHEMBL555347</td>\n",
       "      <td>417.39</td>\n",
       "      <td>4.56</td>\n",
       "      <td>O=C(Nc1ccc(C(F)(F)F)cc1)N1CCC(c2nc(-c3ccccn3)n...</td>\n",
       "      <td>8000.00</td>\n",
       "      <td>B</td>\n",
       "      <td>HERG</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>moderate_risk</td>\n",
       "      <td>5.096910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CHEMBL241330</td>\n",
       "      <td>450.99</td>\n",
       "      <td>3.54</td>\n",
       "      <td>CCOc1cc(CN2CCC(NC(=O)c3cccc(S(C)(=O)=O)c3)CC2)...</td>\n",
       "      <td>1300.00</td>\n",
       "      <td>B</td>\n",
       "      <td>HERG</td>\n",
       "      <td>2007.0</td>\n",
       "      <td>risky</td>\n",
       "      <td>5.886057</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9497</th>\n",
       "      <td>CHEMBL5200451</td>\n",
       "      <td>434.43</td>\n",
       "      <td>3.95</td>\n",
       "      <td>Cc1onc(-c2ccc(F)cc2)c1COc1ccc2c(n1)CCN(C(=O)c1...</td>\n",
       "      <td>6400.00</td>\n",
       "      <td>T</td>\n",
       "      <td>HERG</td>\n",
       "      <td>2022.0</td>\n",
       "      <td>moderate_risk</td>\n",
       "      <td>5.193820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9498</th>\n",
       "      <td>CHEMBL5174560</td>\n",
       "      <td>410.45</td>\n",
       "      <td>3.8</td>\n",
       "      <td>Cc1onc(-c2ccc(F)cc2)c1COc1ccc2c(n1)CCN(C(=O)N(...</td>\n",
       "      <td>8300.00</td>\n",
       "      <td>T</td>\n",
       "      <td>HERG</td>\n",
       "      <td>2022.0</td>\n",
       "      <td>moderate_risk</td>\n",
       "      <td>5.080922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9499</th>\n",
       "      <td>CHEMBL5176630</td>\n",
       "      <td>452.49</td>\n",
       "      <td>3.57</td>\n",
       "      <td>Cc1onc(-c2ccc(F)cc2)c1COc1ccc2c(n1)CCN(C(=O)N1...</td>\n",
       "      <td>9100.00</td>\n",
       "      <td>T</td>\n",
       "      <td>HERG</td>\n",
       "      <td>2022.0</td>\n",
       "      <td>moderate_risk</td>\n",
       "      <td>5.040959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9500</th>\n",
       "      <td>CHEMBL5179688</td>\n",
       "      <td>367.38</td>\n",
       "      <td>2.55</td>\n",
       "      <td>CC(=O)N1Cc2ccc(OCc3c(C)nnn3-c3ccc(F)cc3)nc2C1</td>\n",
       "      <td>30000.00</td>\n",
       "      <td>T</td>\n",
       "      <td>HERG</td>\n",
       "      <td>2022.0</td>\n",
       "      <td>no_risk</td>\n",
       "      <td>4.522879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9501</th>\n",
       "      <td>CHEMBL5206831</td>\n",
       "      <td>464.50</td>\n",
       "      <td>2.05</td>\n",
       "      <td>Cc1nnn(-c2ccc(F)cc2)c1COc1ccc2c(n1)CCN(C(=O)C1...</td>\n",
       "      <td>30000.00</td>\n",
       "      <td>T</td>\n",
       "      <td>HERG</td>\n",
       "      <td>2022.0</td>\n",
       "      <td>no_risk</td>\n",
       "      <td>4.522879</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9502 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Molecule ChEMBL ID  Molecular Weight AlogP  \\\n",
       "0          CHEMBL597737            338.39  3.31   \n",
       "1          CHEMBL403181            512.68  5.63   \n",
       "2          CHEMBL257946            498.66  5.37   \n",
       "3          CHEMBL555347            417.39  4.56   \n",
       "4          CHEMBL241330            450.99  3.54   \n",
       "...                 ...               ...   ...   \n",
       "9497      CHEMBL5200451            434.43  3.95   \n",
       "9498      CHEMBL5174560            410.45   3.8   \n",
       "9499      CHEMBL5176630            452.49  3.57   \n",
       "9500      CHEMBL5179688            367.38  2.55   \n",
       "9501      CHEMBL5206831            464.50  2.05   \n",
       "\n",
       "                                                 Smiles  hERG_IC50_nM  \\\n",
       "0                Fc1ccccc1-c1ccc2oc(N3CCN4CCC3CC4)nc2n1       2800.00   \n",
       "1     CCc1noc2cc3c(cc12)CCN(CCCSc1nnc(-c2cccc4nc(C)c...       1000.00   \n",
       "2     Cc1ccc2c(-c3nnc(SCCCN4CCc5cc6onc(C)c6cc5CC4)n3...        794.33   \n",
       "3     O=C(Nc1ccc(C(F)(F)F)cc1)N1CCC(c2nc(-c3ccccn3)n...       8000.00   \n",
       "4     CCOc1cc(CN2CCC(NC(=O)c3cccc(S(C)(=O)=O)c3)CC2)...       1300.00   \n",
       "...                                                 ...           ...   \n",
       "9497  Cc1onc(-c2ccc(F)cc2)c1COc1ccc2c(n1)CCN(C(=O)c1...       6400.00   \n",
       "9498  Cc1onc(-c2ccc(F)cc2)c1COc1ccc2c(n1)CCN(C(=O)N(...       8300.00   \n",
       "9499  Cc1onc(-c2ccc(F)cc2)c1COc1ccc2c(n1)CCN(C(=O)N1...       9100.00   \n",
       "9500      CC(=O)N1Cc2ccc(OCc3c(C)nnn3-c3ccc(F)cc3)nc2C1      30000.00   \n",
       "9501  Cc1nnn(-c2ccc(F)cc2)c1COc1ccc2c(n1)CCN(C(=O)C1...      30000.00   \n",
       "\n",
       "     Assay Type Target Name  Document Year     Risk Level     pIC50  \n",
       "0             B        HERG         2010.0          risky  5.552842  \n",
       "1             B        HERG         2008.0          risky  6.000000  \n",
       "2             B        HERG         2008.0          risky  6.099999  \n",
       "3             B        HERG         2009.0  moderate_risk  5.096910  \n",
       "4             B        HERG         2007.0          risky  5.886057  \n",
       "...         ...         ...            ...            ...       ...  \n",
       "9497          T        HERG         2022.0  moderate_risk  5.193820  \n",
       "9498          T        HERG         2022.0  moderate_risk  5.080922  \n",
       "9499          T        HERG         2022.0  moderate_risk  5.040959  \n",
       "9500          T        HERG         2022.0        no_risk  4.522879  \n",
       "9501          T        HERG         2022.0        no_risk  4.522879  \n",
       "\n",
       "[9502 rows x 10 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "66171173",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pandas_ai.run(df, prompt='scatter plot pIC50 and AlogP')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "4bf3c8f2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generated code includes import of rdkit which is not in whitelist.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'Unfortunately, I was not able to answer your question, because of the following error:\\n\\nGenerated code includes import of rdkit which is not in whitelist.\\n'"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pandas_ai.run(df, prompt='Can you depict the molecules in the dataframe?')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "94737fcc",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.10.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}