{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "50871605",
   "metadata": {},
   "source": [
    "# Bar Chart"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae8000c7",
   "metadata": {},
   "source": [
    "- Pro tip: Use groupby() and agg() to aggregate your data and push the labels to axis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8a408b5a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c14822ba",
   "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>Sales</th>\n",
       "      <th>Profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Jan</th>\n",
       "      <td>99</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Feb</th>\n",
       "      <td>98</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>March</th>\n",
       "      <td>95</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>April</th>\n",
       "      <td>90</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Sales  Profit\n",
       "Jan       99      10\n",
       "Feb       98      20\n",
       "March     95      30\n",
       "April     90      40"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = {'Month':['Jan','Feb','March','April'],\n",
    "        'Sales': [99, 98, 95, 90],\n",
    "        'Profit': [10,20,30,40]\n",
    "       }\n",
    "df=pd.DataFrame(data,columns=['Sales','Profit'],index=data['Month'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ff1c96d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 4 artists>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "fig,ax=plt.subplots()\n",
    "ax.bar(df.index,df['Sales']) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4e26444",
   "metadata": {},
   "source": [
    "## Customizations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c024d94e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Sales')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "fig,ax=plt.subplots()\n",
    "ax.barh(df.index,df['Sales'][::-1], # acs/desc\n",
    "        color=['grey','grey','grey','orange']) # Horizontal bar chart\n",
    "ax.set_title('Bar chart')\n",
    "ax.set_xlabel('Sales')"
   ]
  }
 ],
 "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
}