Pandas-AI-with-Ollama.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "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": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "data = pd.read_csv(\"population.csv\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain_community.llms import Ollama\n",
    "llm = Ollama(model=\"mistral\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pandasai import SmartDataframe\n",
    "df = SmartDataframe(data, config={\"llm\": llm})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Country, Population]\n",
       "Index: []"
      ]
     },
     "execution_count": 4,
     "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 top 5 countries by population?')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2058073260\n"
     ]
    }
   ],
   "source": [
    "print(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
}