PandasAI-with-Groq.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from dotenv import load_dotenv\n",
    "\n",
    "load_dotenv(override=True)"
   ]
  },
  {
   "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": [
    "import pandas as pd\n",
    "data = pd.read_csv(\"population.csv\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from langchain_groq.chat_models import ChatGroq\n",
    "\n",
    "llm = ChatGroq(\n",
    "    model_name=\"mixtral-8x7b-32768\", \n",
    "    api_key = os.environ[\"GROQ_API_KEY\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pandasai import SmartDataframe\n",
    "df = SmartDataframe(data, config={\"llm\": llm})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.chat('Which are the top 5 countries by population?')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3194295578"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.chat(\n",
    "    \"First sort the countries by population and then \\\n",
    "calculate the total population of the top 3 countries by population\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'d:/Pandas-AI-Projects/exports/charts/temp_chart.png'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.chat(\"First sort countries by population and \\\n",
    "create a bar plot of the top 5 countries\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'d:/Pandas-AI-Projects/exports/charts/temp_chart.png'"
      ]
     },
     "execution_count": 11,
     "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(\"First sort countries by population and \\\n",
    "create a bar plot of the top 5 countries\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'d:/Pandas-AI-Projects/exports/charts/temp_chart.png'"
      ]
     },
     "execution_count": 12,
     "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(\"First sort countries by population and \\\n",
    "create a pie plot of the top 5 countries\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's connect [YouTube](http://youtube.com/tirendazacademy) | [Medium](http://tirendazacademy.medium.com) | [X](http://x.com/tirendazacademy) | [Linkedin](https://www.linkedin.com/in/tirendaz-academy) 😎"
   ]
  }
 ],
 "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
}