{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e1b04b1d",
   "metadata": {},
   "source": [
    "# Grouped Bar chart"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17bd885e",
   "metadata": {},
   "source": [
    "- Specify the categorical Column in 'hue'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "dab91803",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "data = {'Month':['Jan','Jan','March','April'],\n",
    "        'Sales': [99, 102, 95, 120],\n",
    "        'Profit': [10,20,30,40],\n",
    "        'Country': ['India','Nepal','Nepal','India']\n",
    "       }\n",
    "df=pd.DataFrame(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "39ba15f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(\n",
    "x='Month',\n",
    "y='Sales',\n",
    "data=df.sort_values(by='Profit'), # Sorting the data in charts\n",
    "hue='Country'\n",
    ");"
   ]
  }
 ],
 "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": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}