pandasai.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "data = pd.read_csv(\"population.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>Country</th>\n",
       "      <th>Population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>United States</td>\n",
       "      <td>339996563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>China</td>\n",
       "      <td>1425671352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Germany</td>\n",
       "      <td>83294633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Turkey</td>\n",
       "      <td>85816199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Japan</td>\n",
       "      <td>123294513</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Country  Population\n",
       "0  United States   339996563\n",
       "1          China  1425671352\n",
       "2        Germany    83294633\n",
       "3         Turkey    85816199\n",
       "4          Japan   123294513"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain_community.llms import Ollama \n",
    "\n",
    "llm = Ollama(model=\"mistral\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pandasai import SmartDataframe \n",
    "\n",
    "df = SmartDataframe(data, config={\"llm\": llm})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 5 countries by population:\n",
      "         Country  Population\n",
      "8          India  1428627663\n",
      "1          China  1425671352\n",
      "0  United States   339996563\n",
      "5      Indonesia   277534122\n",
      "6       Pakistan   240485658\n"
     ]
    },
    {
     "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>Country</th>\n",
       "      <th>Population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>India</td>\n",
       "      <td>1428627663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>China</td>\n",
       "      <td>1425671352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>United States</td>\n",
       "      <td>339996563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Indonesia</td>\n",
       "      <td>277534122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Pakistan</td>\n",
       "      <td>240485658</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Country  Population\n",
       "8          India  1428627663\n",
       "1          China  1425671352\n",
       "0  United States   339996563\n",
       "5      Indonesia   277534122\n",
       "6       Pakistan   240485658"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.chat(\"Which are the top 5 countries by population\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2058073260"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.chat(\"What is the total populations of the top 5 countries by population?\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "genai",
   "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": "3.10.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}