7. Pivot table.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lecture 7: Data Aggregation/grouping and Pivot table using Pandas\n",
    "\n",
    "Instructor: Md Shahidullah Kawsar\n",
    "<br>Data Scientist, IDARE, Houston, TX, USA\n",
    "\n",
    "#### Objectives:\n",
    "- Data filtering\n",
    "- Data preprocessing\n",
    "- Data Aggregation/grouping \n",
    "- Pivot table\n",
    "- Data Visualization: Barplot\n",
    "\n",
    "#### References:\n",
    "\n",
    "[1] https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.groupby.html\n",
    "<br>[2] https://pandas.pydata.org/docs/reference/api/pandas.pivot_table.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd \n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "sns.set_context(\"talk\")\n",
    "sns.set_style(\"darkgrid\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
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       "        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>model</th>\n",
       "      <th>year</th>\n",
       "      <th>price</th>\n",
       "      <th>transmission</th>\n",
       "      <th>mileage</th>\n",
       "      <th>fuelType</th>\n",
       "      <th>tax</th>\n",
       "      <th>mpg</th>\n",
       "      <th>engineSize</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5 Series</td>\n",
       "      <td>2014</td>\n",
       "      <td>11200</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>67068</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>125</td>\n",
       "      <td>57.6</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6 Series</td>\n",
       "      <td>2018</td>\n",
       "      <td>27000</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>14827</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>145</td>\n",
       "      <td>42.8</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5 Series</td>\n",
       "      <td>2016</td>\n",
       "      <td>16000</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>62794</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>160</td>\n",
       "      <td>51.4</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1 Series</td>\n",
       "      <td>2017</td>\n",
       "      <td>12750</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>26676</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>145</td>\n",
       "      <td>72.4</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7 Series</td>\n",
       "      <td>2014</td>\n",
       "      <td>14500</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>39554</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>160</td>\n",
       "      <td>50.4</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       model  year  price transmission  mileage fuelType  tax   mpg  \\\n",
       "0   5 Series  2014  11200    Automatic    67068   Diesel  125  57.6   \n",
       "1   6 Series  2018  27000    Automatic    14827   Petrol  145  42.8   \n",
       "2   5 Series  2016  16000    Automatic    62794   Diesel  160  51.4   \n",
       "3   1 Series  2017  12750    Automatic    26676   Diesel  145  72.4   \n",
       "4   7 Series  2014  14500    Automatic    39554   Diesel  160  50.4   \n",
       "\n",
       "   engineSize  \n",
       "0         2.0  \n",
       "1         2.0  \n",
       "2         3.0  \n",
       "3         1.5  \n",
       "4         3.0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.read_csv(\"bmw.csv\")\n",
    "display(df.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### check the number of unique values in the 'model' column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[' 5 Series' ' 6 Series' ' 1 Series' ' 7 Series' ' 2 Series' ' 4 Series'\n",
      " ' X3' ' 3 Series' ' X5' ' X4' ' i3' ' X1' ' M4' ' X2' ' X6' ' 8 Series'\n",
      " ' Z4' ' X7' ' M5' ' i8' ' M2' ' M3' ' M6' ' Z3']\n"
     ]
    }
   ],
   "source": [
    "print(df['model'].unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### data filtering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th>year</th>\n",
       "      <th>price</th>\n",
       "      <th>transmission</th>\n",
       "      <th>mileage</th>\n",
       "      <th>fuelType</th>\n",
       "      <th>tax</th>\n",
       "      <th>mpg</th>\n",
       "      <th>engineSize</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1113</th>\n",
       "      <td>M6</td>\n",
       "      <td>2016</td>\n",
       "      <td>35898</td>\n",
       "      <td>Semi-Auto</td>\n",
       "      <td>26642</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>555</td>\n",
       "      <td>28.5</td>\n",
       "      <td>4.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1195</th>\n",
       "      <td>M6</td>\n",
       "      <td>2016</td>\n",
       "      <td>36995</td>\n",
       "      <td>Semi-Auto</td>\n",
       "      <td>19000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>555</td>\n",
       "      <td>27.4</td>\n",
       "      <td>4.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1210</th>\n",
       "      <td>M6</td>\n",
       "      <td>2015</td>\n",
       "      <td>34147</td>\n",
       "      <td>Semi-Auto</td>\n",
       "      <td>34017</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>555</td>\n",
       "      <td>28.5</td>\n",
       "      <td>4.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6542</th>\n",
       "      <td>M6</td>\n",
       "      <td>2018</td>\n",
       "      <td>46995</td>\n",
       "      <td>Semi-Auto</td>\n",
       "      <td>8207</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>145</td>\n",
       "      <td>28.5</td>\n",
       "      <td>4.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7953</th>\n",
       "      <td>M6</td>\n",
       "      <td>2015</td>\n",
       "      <td>33500</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>28314</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>555</td>\n",
       "      <td>28.5</td>\n",
       "      <td>4.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9229</th>\n",
       "      <td>M6</td>\n",
       "      <td>2006</td>\n",
       "      <td>12495</td>\n",
       "      <td>Semi-Auto</td>\n",
       "      <td>89000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>570</td>\n",
       "      <td>19.1</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9241</th>\n",
       "      <td>M6</td>\n",
       "      <td>2014</td>\n",
       "      <td>28490</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>53249</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>565</td>\n",
       "      <td>28.5</td>\n",
       "      <td>4.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10346</th>\n",
       "      <td>M6</td>\n",
       "      <td>2014</td>\n",
       "      <td>29000</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>34000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>565</td>\n",
       "      <td>28.5</td>\n",
       "      <td>4.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      model  year  price transmission  mileage fuelType  tax   mpg  engineSize\n",
       "1113     M6  2016  35898    Semi-Auto    26642   Petrol  555  28.5         4.4\n",
       "1195     M6  2016  36995    Semi-Auto    19000   Petrol  555  27.4         4.4\n",
       "1210     M6  2015  34147    Semi-Auto    34017   Petrol  555  28.5         4.4\n",
       "6542     M6  2018  46995    Semi-Auto     8207   Petrol  145  28.5         4.4\n",
       "7953     M6  2015  33500    Automatic    28314   Petrol  555  28.5         4.4\n",
       "9229     M6  2006  12495    Semi-Auto    89000   Petrol  570  19.1         5.0\n",
       "9241     M6  2014  28490    Automatic    53249   Petrol  565  28.5         4.4\n",
       "10346    M6  2014  29000    Automatic    34000   Petrol  565  28.5         4.4"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(8, 9)\n"
     ]
    }
   ],
   "source": [
    "df_m6 = df[df['model'] == ' M6']\n",
    "\n",
    "display(df_m6)\n",
    "print(df_m6.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "32190.0"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# find the average price of the ' M6' model cars\n",
    "df_m6['price'].sum()/8"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Data Aggregation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>sum</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1 Series</th>\n",
       "      <td>1969</td>\n",
       "      <td>15821.670391</td>\n",
       "      <td>31152869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2 Series</th>\n",
       "      <td>1229</td>\n",
       "      <td>19539.371847</td>\n",
       "      <td>24013888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3 Series</th>\n",
       "      <td>2443</td>\n",
       "      <td>19880.857962</td>\n",
       "      <td>48568936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4 Series</th>\n",
       "      <td>995</td>\n",
       "      <td>22498.418090</td>\n",
       "      <td>22385926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5 Series</th>\n",
       "      <td>1056</td>\n",
       "      <td>22537.428030</td>\n",
       "      <td>23799524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6 Series</th>\n",
       "      <td>108</td>\n",
       "      <td>24354.370370</td>\n",
       "      <td>2630272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7 Series</th>\n",
       "      <td>106</td>\n",
       "      <td>36934.320755</td>\n",
       "      <td>3915038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8 Series</th>\n",
       "      <td>39</td>\n",
       "      <td>63997.794872</td>\n",
       "      <td>2495914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M2</th>\n",
       "      <td>21</td>\n",
       "      <td>43140.333333</td>\n",
       "      <td>905947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M3</th>\n",
       "      <td>27</td>\n",
       "      <td>30229.777778</td>\n",
       "      <td>816204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M4</th>\n",
       "      <td>125</td>\n",
       "      <td>43274.232000</td>\n",
       "      <td>5409279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M5</th>\n",
       "      <td>29</td>\n",
       "      <td>57760.000000</td>\n",
       "      <td>1675040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M6</th>\n",
       "      <td>8</td>\n",
       "      <td>32190.000000</td>\n",
       "      <td>257520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X1</th>\n",
       "      <td>804</td>\n",
       "      <td>19816.564677</td>\n",
       "      <td>15932518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X2</th>\n",
       "      <td>288</td>\n",
       "      <td>28486.663194</td>\n",
       "      <td>8204159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X3</th>\n",
       "      <td>551</td>\n",
       "      <td>27758.310345</td>\n",
       "      <td>15294829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X4</th>\n",
       "      <td>179</td>\n",
       "      <td>32721.335196</td>\n",
       "      <td>5857119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X5</th>\n",
       "      <td>468</td>\n",
       "      <td>39651.196581</td>\n",
       "      <td>18556760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X6</th>\n",
       "      <td>106</td>\n",
       "      <td>43838.613208</td>\n",
       "      <td>4646893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X7</th>\n",
       "      <td>55</td>\n",
       "      <td>69842.763636</td>\n",
       "      <td>3841352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Z3</th>\n",
       "      <td>7</td>\n",
       "      <td>5826.428571</td>\n",
       "      <td>40785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Z4</th>\n",
       "      <td>108</td>\n",
       "      <td>27001.935185</td>\n",
       "      <td>2916209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>i3</th>\n",
       "      <td>43</td>\n",
       "      <td>18667.116279</td>\n",
       "      <td>802686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>i8</th>\n",
       "      <td>17</td>\n",
       "      <td>57012.588235</td>\n",
       "      <td>969214</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           count          mean       sum\n",
       "model                                   \n",
       " 1 Series   1969  15821.670391  31152869\n",
       " 2 Series   1229  19539.371847  24013888\n",
       " 3 Series   2443  19880.857962  48568936\n",
       " 4 Series    995  22498.418090  22385926\n",
       " 5 Series   1056  22537.428030  23799524\n",
       " 6 Series    108  24354.370370   2630272\n",
       " 7 Series    106  36934.320755   3915038\n",
       " 8 Series     39  63997.794872   2495914\n",
       " M2           21  43140.333333    905947\n",
       " M3           27  30229.777778    816204\n",
       " M4          125  43274.232000   5409279\n",
       " M5           29  57760.000000   1675040\n",
       " M6            8  32190.000000    257520\n",
       " X1          804  19816.564677  15932518\n",
       " X2          288  28486.663194   8204159\n",
       " X3          551  27758.310345  15294829\n",
       " X4          179  32721.335196   5857119\n",
       " X5          468  39651.196581  18556760\n",
       " X6          106  43838.613208   4646893\n",
       " X7           55  69842.763636   3841352\n",
       " Z3            7   5826.428571     40785\n",
       " Z4          108  27001.935185   2916209\n",
       " i3           43  18667.116279    802686\n",
       " i8           17  57012.588235    969214"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_agg = df.groupby('model')['price'].agg(['count', 'mean', 'sum'])\n",
    "\n",
    "display(df_agg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Data preprocessing before visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "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>mean</th>\n",
       "      <th>sum</th>\n",
       "      <th>percent_of_cars</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3 Series</th>\n",
       "      <td>19880.86</td>\n",
       "      <td>48.57</td>\n",
       "      <td>22.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1 Series</th>\n",
       "      <td>15821.67</td>\n",
       "      <td>31.15</td>\n",
       "      <td>18.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2 Series</th>\n",
       "      <td>19539.37</td>\n",
       "      <td>24.01</td>\n",
       "      <td>11.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5 Series</th>\n",
       "      <td>22537.43</td>\n",
       "      <td>23.80</td>\n",
       "      <td>9.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4 Series</th>\n",
       "      <td>22498.42</td>\n",
       "      <td>22.39</td>\n",
       "      <td>9.23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               mean    sum  percent_of_cars\n",
       "model                                      \n",
       " 3 Series  19880.86  48.57            22.66\n",
       " 1 Series  15821.67  31.15            18.26\n",
       " 2 Series  19539.37  24.01            11.40\n",
       " 5 Series  22537.43  23.80             9.80\n",
       " 4 Series  22498.42  22.39             9.23"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# sort the dataframe based on the column 'count'\n",
    "df_agg = df_agg.sort_values(by='count', ascending=False)\n",
    "\n",
    "# create a new column percentage of the cars in each model\n",
    "df_agg['percent_of_cars'] = (df_agg['count']/df_agg['count'].sum())*100\n",
    "\n",
    "# convert the 'sum' column in millions\n",
    "df_agg['sum'] = df_agg['sum']/1000000\n",
    "\n",
    "# keep up to two decimal points\n",
    "df_agg = df_agg.round(2)\n",
    "\n",
    "# remove the 'count' column\n",
    "df_agg = df_agg.drop('count', axis=1)\n",
    "\n",
    "display(df_agg.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Barplot: Percentage of cars, Average and Total selling price of cars by model, transmission and fuelType\n",
    "#### Questions in mind: Based on model, transmission and fuelType,\n",
    "\n",
    "- What are the top 5 selling car models in the dataset?\n",
    "- What's the average selling price of the top 5 selling car models?\n",
    "- What's the total sale of the top 5 selling car models?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,8))\n",
    "\n",
    "plt.subplot(1, 3, 1)\n",
    "sns.barplot(x='percent_of_cars', y=df_agg.index, data=df_agg, orient='h', color='blue')\n",
    "plt.title(\"Percentage of cars in each model\")\n",
    "plt.xlabel(\"Percentage of cars (%)\")\n",
    "# plt.grid()\n",
    "\n",
    "plt.subplot(1, 3, 2)\n",
    "sns.barplot(x='mean', y=df_agg.index, data=df_agg, orient='h', color='orange')\n",
    "plt.title(\"Average resale value of the cars of each model\")\n",
    "plt.xlabel(\"Average price ($)\")\n",
    "plt.ylabel(\"\")\n",
    "\n",
    "plt.subplot(1, 3, 3)\n",
    "sns.barplot(x='sum', y=df_agg.index, data=df_agg, orient='h', color='green')\n",
    "plt.title(\"total resale value of the cars by each model\")\n",
    "plt.xlabel(\"Total price in million $\")\n",
    "plt.ylabel(\"\")\n",
    "\n",
    "plt.suptitle(\"Barplot of the BMW used car\")\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create a pivot table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>sum</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>price</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1 Series</th>\n",
       "      <td>1969</td>\n",
       "      <td>15821.670391</td>\n",
       "      <td>31152869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2 Series</th>\n",
       "      <td>1229</td>\n",
       "      <td>19539.371847</td>\n",
       "      <td>24013888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3 Series</th>\n",
       "      <td>2443</td>\n",
       "      <td>19880.857962</td>\n",
       "      <td>48568936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4 Series</th>\n",
       "      <td>995</td>\n",
       "      <td>22498.418090</td>\n",
       "      <td>22385926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5 Series</th>\n",
       "      <td>1056</td>\n",
       "      <td>22537.428030</td>\n",
       "      <td>23799524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6 Series</th>\n",
       "      <td>108</td>\n",
       "      <td>24354.370370</td>\n",
       "      <td>2630272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7 Series</th>\n",
       "      <td>106</td>\n",
       "      <td>36934.320755</td>\n",
       "      <td>3915038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8 Series</th>\n",
       "      <td>39</td>\n",
       "      <td>63997.794872</td>\n",
       "      <td>2495914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M2</th>\n",
       "      <td>21</td>\n",
       "      <td>43140.333333</td>\n",
       "      <td>905947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M3</th>\n",
       "      <td>27</td>\n",
       "      <td>30229.777778</td>\n",
       "      <td>816204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M4</th>\n",
       "      <td>125</td>\n",
       "      <td>43274.232000</td>\n",
       "      <td>5409279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M5</th>\n",
       "      <td>29</td>\n",
       "      <td>57760.000000</td>\n",
       "      <td>1675040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M6</th>\n",
       "      <td>8</td>\n",
       "      <td>32190.000000</td>\n",
       "      <td>257520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X1</th>\n",
       "      <td>804</td>\n",
       "      <td>19816.564677</td>\n",
       "      <td>15932518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X2</th>\n",
       "      <td>288</td>\n",
       "      <td>28486.663194</td>\n",
       "      <td>8204159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X3</th>\n",
       "      <td>551</td>\n",
       "      <td>27758.310345</td>\n",
       "      <td>15294829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X4</th>\n",
       "      <td>179</td>\n",
       "      <td>32721.335196</td>\n",
       "      <td>5857119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X5</th>\n",
       "      <td>468</td>\n",
       "      <td>39651.196581</td>\n",
       "      <td>18556760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X6</th>\n",
       "      <td>106</td>\n",
       "      <td>43838.613208</td>\n",
       "      <td>4646893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X7</th>\n",
       "      <td>55</td>\n",
       "      <td>69842.763636</td>\n",
       "      <td>3841352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Z3</th>\n",
       "      <td>7</td>\n",
       "      <td>5826.428571</td>\n",
       "      <td>40785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Z4</th>\n",
       "      <td>108</td>\n",
       "      <td>27001.935185</td>\n",
       "      <td>2916209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>i3</th>\n",
       "      <td>43</td>\n",
       "      <td>18667.116279</td>\n",
       "      <td>802686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>i8</th>\n",
       "      <td>17</td>\n",
       "      <td>57012.588235</td>\n",
       "      <td>969214</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          count          mean       sum\n",
       "          price         price     price\n",
       "model                                  \n",
       " 1 Series  1969  15821.670391  31152869\n",
       " 2 Series  1229  19539.371847  24013888\n",
       " 3 Series  2443  19880.857962  48568936\n",
       " 4 Series   995  22498.418090  22385926\n",
       " 5 Series  1056  22537.428030  23799524\n",
       " 6 Series   108  24354.370370   2630272\n",
       " 7 Series   106  36934.320755   3915038\n",
       " 8 Series    39  63997.794872   2495914\n",
       " M2          21  43140.333333    905947\n",
       " M3          27  30229.777778    816204\n",
       " M4         125  43274.232000   5409279\n",
       " M5          29  57760.000000   1675040\n",
       " M6           8  32190.000000    257520\n",
       " X1         804  19816.564677  15932518\n",
       " X2         288  28486.663194   8204159\n",
       " X3         551  27758.310345  15294829\n",
       " X4         179  32721.335196   5857119\n",
       " X5         468  39651.196581  18556760\n",
       " X6         106  43838.613208   4646893\n",
       " X7          55  69842.763636   3841352\n",
       " Z3           7   5826.428571     40785\n",
       " Z4         108  27001.935185   2916209\n",
       " i3          43  18667.116279    802686\n",
       " i8          17  57012.588235    969214"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_pivot = pd.pivot_table(df, index='model', values='price', aggfunc=['count', 'mean', 'sum'])\n",
    "display(df_pivot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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