Data Analysis/Project - Case Study 2 - Fuel Economy/Case Study - Fuel Economy.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# What is Fuel Economy?\n",
    "\n",
    "[Wikipedia](https://en.wikipedia.org/wiki/Fuel_economy_in_automobiles): The fuel economy of an automobile is the fuel efficiency relationship between the distance traveled and the amount of fuel consumed by the vehicle. Consumption can be expressed in terms of volume of fuel to travel a distance, or the distance travelled per unit volume of fuel consumed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![img](fuel-consumption.png)\n",
    "\n",
    "- Fuel Economy : [Information](https://www.epa.gov/compliance-and-fuel-economy-data/data-cars-used-testing-fuel-economy)\n",
    "- Fuel Economy: [Dataset](https://www.fueleconomy.gov/feg/download.shtml/)\n",
    "    - [Data Description](http://www.fueleconomy.gov/feg/epadata/Readme.txt)\n",
    "    - [PDF](http://www.fueleconomy.gov/feg/EPAGreenGuide/GreenVehicleGuideDocumentation.pdf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "--------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1.Accessing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_2008 = pd.read_csv(\"Data/all_alpha_08.csv\")\n",
    "df_2018 = pd.read_csv(\"Data/all_alpha_18.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data Size:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(43272, 28998)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#number of samples in each dataset\n",
    "print(\"Data Size:\")\n",
    "df_2008.size, df_2018.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Numer of rows and columns:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "((2404, 18), (1611, 18))"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# number of columns in each dataset\n",
    "print(\"Numer of rows and columns:\")\n",
    "df_2008.shape, df_2018.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Duplicate information 2008:  25\n",
      "Duplicate information 2018:  0\n"
     ]
    }
   ],
   "source": [
    "# duplicate rows in each dataset\n",
    "print(\"Duplicate information 2008: \", df_2008.duplicated().sum())\n",
    "print(\"Duplicate information 2018: \", df_2018.duplicated().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Datatypes information: \n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2404 entries, 0 to 2403\n",
      "Data columns (total 18 columns):\n",
      " #   Column                Non-Null Count  Dtype  \n",
      "---  ------                --------------  -----  \n",
      " 0   Model                 2404 non-null   object \n",
      " 1   Displ                 2404 non-null   float64\n",
      " 2   Cyl                   2205 non-null   object \n",
      " 3   Trans                 2205 non-null   object \n",
      " 4   Drive                 2311 non-null   object \n",
      " 5   Fuel                  2404 non-null   object \n",
      " 6   Sales Area            2404 non-null   object \n",
      " 7   Stnd                  2404 non-null   object \n",
      " 8   Underhood ID          2404 non-null   object \n",
      " 9   Veh Class             2404 non-null   object \n",
      " 10  Air Pollution Score   2404 non-null   object \n",
      " 11  FE Calc Appr          2205 non-null   object \n",
      " 12  City MPG              2205 non-null   object \n",
      " 13  Hwy MPG               2205 non-null   object \n",
      " 14  Cmb MPG               2205 non-null   object \n",
      " 15  Unadj Cmb MPG         2205 non-null   float64\n",
      " 16  Greenhouse Gas Score  2205 non-null   object \n",
      " 17  SmartWay              2404 non-null   object \n",
      "dtypes: float64(2), object(16)\n",
      "memory usage: 338.2+ KB\n"
     ]
    }
   ],
   "source": [
    "# datatypes of columns\n",
    "print(\"Datatypes information: \")\n",
    "df_2008.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1611 entries, 0 to 1610\n",
      "Data columns (total 18 columns):\n",
      " #   Column                Non-Null Count  Dtype  \n",
      "---  ------                --------------  -----  \n",
      " 0   Model                 1611 non-null   object \n",
      " 1   Displ                 1609 non-null   float64\n",
      " 2   Cyl                   1609 non-null   float64\n",
      " 3   Trans                 1611 non-null   object \n",
      " 4   Drive                 1611 non-null   object \n",
      " 5   Fuel                  1611 non-null   object \n",
      " 6   Cert Region           1611 non-null   object \n",
      " 7   Stnd                  1611 non-null   object \n",
      " 8   Stnd Description      1611 non-null   object \n",
      " 9   Underhood ID          1611 non-null   object \n",
      " 10  Veh Class             1611 non-null   object \n",
      " 11  Air Pollution Score   1611 non-null   int64  \n",
      " 12  City MPG              1611 non-null   object \n",
      " 13  Hwy MPG               1611 non-null   object \n",
      " 14  Cmb MPG               1611 non-null   object \n",
      " 15  Greenhouse Gas Score  1611 non-null   int64  \n",
      " 16  SmartWay              1611 non-null   object \n",
      " 17  Comb CO2              1611 non-null   object \n",
      "dtypes: float64(2), int64(2), object(14)\n",
      "memory usage: 226.7+ KB\n"
     ]
    }
   ],
   "source": [
    "df_2018.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Features with missing values for 2008:  Model                     0\n",
      "Displ                     0\n",
      "Cyl                     199\n",
      "Trans                   199\n",
      "Drive                    93\n",
      "Fuel                      0\n",
      "Sales Area                0\n",
      "Stnd                      0\n",
      "Underhood ID              0\n",
      "Veh Class                 0\n",
      "Air Pollution Score       0\n",
      "FE Calc Appr            199\n",
      "City MPG                199\n",
      "Hwy MPG                 199\n",
      "Cmb MPG                 199\n",
      "Unadj Cmb MPG           199\n",
      "Greenhouse Gas Score    199\n",
      "SmartWay                  0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# features with missing values\n",
    "print(\"Features with missing values for 2008: \", df_2008.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Features with missing values for 2018:  Model                   0\n",
      "Displ                   2\n",
      "Cyl                     2\n",
      "Trans                   0\n",
      "Drive                   0\n",
      "Fuel                    0\n",
      "Cert Region             0\n",
      "Stnd                    0\n",
      "Stnd Description        0\n",
      "Underhood ID            0\n",
      "Veh Class               0\n",
      "Air Pollution Score     0\n",
      "City MPG                0\n",
      "Hwy MPG                 0\n",
      "Cmb MPG                 0\n",
      "Greenhouse Gas Score    0\n",
      "SmartWay                0\n",
      "Comb CO2                0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(\"Features with missing values for 2018: \", df_2018.isna().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of non null unique values for features 2008:  Model                   436\n",
      "Displ                    47\n",
      "Cyl                       8\n",
      "Trans                    14\n",
      "Drive                     2\n",
      "Fuel                      5\n",
      "Sales Area                3\n",
      "Stnd                     12\n",
      "Underhood ID            343\n",
      "Veh Class                 9\n",
      "Air Pollution Score      13\n",
      "FE Calc Appr              2\n",
      "City MPG                 39\n",
      "Hwy MPG                  43\n",
      "Cmb MPG                  38\n",
      "Unadj Cmb MPG           721\n",
      "Greenhouse Gas Score     20\n",
      "SmartWay                  2\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# number of non-null unique values for features in each dataset\n",
    "# what those unique values are and counts for each\n",
    "print(\"Number of non null unique values for features 2008: \", df_2008.nunique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of non null unique values for features 2018:  Model                   367\n",
      "Displ                    36\n",
      "Cyl                       7\n",
      "Trans                    26\n",
      "Drive                     2\n",
      "Fuel                      5\n",
      "Cert Region               2\n",
      "Stnd                     19\n",
      "Stnd Description         19\n",
      "Underhood ID            230\n",
      "Veh Class                 9\n",
      "Air Pollution Score       6\n",
      "City MPG                 58\n",
      "Hwy MPG                  62\n",
      "Cmb MPG                  57\n",
      "Greenhouse Gas Score     10\n",
      "SmartWay                  3\n",
      "Comb CO2                299\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# number of non-null unique values for features in each dataset\n",
    "# what those unique values are and counts for each\n",
    "print(\"Number of non null unique values for features 2018: \", df_2018.nunique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "--------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Cleaning Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.1. Drop Extra columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_2008.drop([\"Stnd\", \"Underhood ID\", \"FE Calc Appr\", \"Unadj Cmb MPG\"], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>Displ</th>\n",
       "      <th>Cyl</th>\n",
       "      <th>Trans</th>\n",
       "      <th>Drive</th>\n",
       "      <th>Fuel</th>\n",
       "      <th>Sales Area</th>\n",
       "      <th>Veh Class</th>\n",
       "      <th>Air Pollution Score</th>\n",
       "      <th>City MPG</th>\n",
       "      <th>Hwy MPG</th>\n",
       "      <th>Cmb MPG</th>\n",
       "      <th>Greenhouse Gas Score</th>\n",
       "      <th>SmartWay</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ACURA MDX</td>\n",
       "      <td>3.7</td>\n",
       "      <td>(6 cyl)</td>\n",
       "      <td>Auto-S5</td>\n",
       "      <td>4WD</td>\n",
       "      <td>Gasoline</td>\n",
       "      <td>CA</td>\n",
       "      <td>SUV</td>\n",
       "      <td>7</td>\n",
       "      <td>15</td>\n",
       "      <td>20</td>\n",
       "      <td>17</td>\n",
       "      <td>4</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ACURA MDX</td>\n",
       "      <td>3.7</td>\n",
       "      <td>(6 cyl)</td>\n",
       "      <td>Auto-S5</td>\n",
       "      <td>4WD</td>\n",
       "      <td>Gasoline</td>\n",
       "      <td>FA</td>\n",
       "      <td>SUV</td>\n",
       "      <td>6</td>\n",
       "      <td>15</td>\n",
       "      <td>20</td>\n",
       "      <td>17</td>\n",
       "      <td>4</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "       Model  Displ      Cyl    Trans Drive      Fuel Sales Area Veh Class  \\\n",
       "0  ACURA MDX    3.7  (6 cyl)  Auto-S5   4WD  Gasoline         CA       SUV   \n",
       "1  ACURA MDX    3.7  (6 cyl)  Auto-S5   4WD  Gasoline         FA       SUV   \n",
       "\n",
       "  Air Pollution Score City MPG Hwy MPG Cmb MPG Greenhouse Gas Score SmartWay  \n",
       "0                   7       15      20      17                    4       no  \n",
       "1                   6       15      20      17                    4       no  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2008.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_2018.drop([\"Stnd\", \"Stnd Description\", \"Underhood ID\", \"Comb CO2\"], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>Displ</th>\n",
       "      <th>Cyl</th>\n",
       "      <th>Trans</th>\n",
       "      <th>Drive</th>\n",
       "      <th>Fuel</th>\n",
       "      <th>Cert Region</th>\n",
       "      <th>Veh Class</th>\n",
       "      <th>Air Pollution Score</th>\n",
       "      <th>City MPG</th>\n",
       "      <th>Hwy MPG</th>\n",
       "      <th>Cmb MPG</th>\n",
       "      <th>Greenhouse Gas Score</th>\n",
       "      <th>SmartWay</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ACURA RDX</td>\n",
       "      <td>3.5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>SemiAuto-6</td>\n",
       "      <td>2WD</td>\n",
       "      <td>Gasoline</td>\n",
       "      <td>FA</td>\n",
       "      <td>small SUV</td>\n",
       "      <td>3</td>\n",
       "      <td>20</td>\n",
       "      <td>28</td>\n",
       "      <td>23</td>\n",
       "      <td>5</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ACURA RDX</td>\n",
       "      <td>3.5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>SemiAuto-6</td>\n",
       "      <td>2WD</td>\n",
       "      <td>Gasoline</td>\n",
       "      <td>CA</td>\n",
       "      <td>small SUV</td>\n",
       "      <td>3</td>\n",
       "      <td>20</td>\n",
       "      <td>28</td>\n",
       "      <td>23</td>\n",
       "      <td>5</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Model  Displ  Cyl       Trans Drive      Fuel Cert Region  Veh Class  \\\n",
       "0  ACURA RDX    3.5  6.0  SemiAuto-6   2WD  Gasoline          FA  small SUV   \n",
       "1  ACURA RDX    3.5  6.0  SemiAuto-6   2WD  Gasoline          CA  small SUV   \n",
       "\n",
       "   Air Pollution Score City MPG Hwy MPG Cmb MPG  Greenhouse Gas Score SmartWay  \n",
       "0                    3       20      28      23                     5       No  \n",
       "1                    3       20      28      23                     5       No  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2018.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.2. Rename the columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>Displ</th>\n",
       "      <th>Cyl</th>\n",
       "      <th>Trans</th>\n",
       "      <th>Drive</th>\n",
       "      <th>Fuel</th>\n",
       "      <th>Cert Region</th>\n",
       "      <th>Veh Class</th>\n",
       "      <th>Air Pollution Score</th>\n",
       "      <th>City MPG</th>\n",
       "      <th>Hwy MPG</th>\n",
       "      <th>Cmb MPG</th>\n",
       "      <th>Greenhouse Gas Score</th>\n",
       "      <th>SmartWay</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ACURA MDX</td>\n",
       "      <td>3.7</td>\n",
       "      <td>(6 cyl)</td>\n",
       "      <td>Auto-S5</td>\n",
       "      <td>4WD</td>\n",
       "      <td>Gasoline</td>\n",
       "      <td>CA</td>\n",
       "      <td>SUV</td>\n",
       "      <td>7</td>\n",
       "      <td>15</td>\n",
       "      <td>20</td>\n",
       "      <td>17</td>\n",
       "      <td>4</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ACURA MDX</td>\n",
       "      <td>3.7</td>\n",
       "      <td>(6 cyl)</td>\n",
       "      <td>Auto-S5</td>\n",
       "      <td>4WD</td>\n",
       "      <td>Gasoline</td>\n",
       "      <td>FA</td>\n",
       "      <td>SUV</td>\n",
       "      <td>6</td>\n",
       "      <td>15</td>\n",
       "      <td>20</td>\n",
       "      <td>17</td>\n",
       "      <td>4</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "       Model  Displ      Cyl    Trans Drive      Fuel Cert Region Veh Class  \\\n",
       "0  ACURA MDX    3.7  (6 cyl)  Auto-S5   4WD  Gasoline          CA       SUV   \n",
       "1  ACURA MDX    3.7  (6 cyl)  Auto-S5   4WD  Gasoline          FA       SUV   \n",
       "\n",
       "  Air Pollution Score City MPG Hwy MPG Cmb MPG Greenhouse Gas Score SmartWay  \n",
       "0                   7       15      20      17                    4       no  \n",
       "1                   6       15      20      17                    4       no  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#make column name consistent between two data sets\n",
    "df_2008.rename(columns = {\"Sales Area\": \"Cert Region\"}, inplace = True)\n",
    "df_2008.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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",
       "    .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>model</th>\n",
       "      <th>displ</th>\n",
       "      <th>cyl</th>\n",
       "      <th>trans</th>\n",
       "      <th>drive</th>\n",
       "      <th>fuel</th>\n",
       "      <th>cert_region</th>\n",
       "      <th>veh_class</th>\n",
       "      <th>air_pollution_score</th>\n",
       "      <th>city_mpg</th>\n",
       "      <th>hwy_mpg</th>\n",
       "      <th>cmb_mpg</th>\n",
       "      <th>greenhouse_gas_score</th>\n",
       "      <th>smartway</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ACURA MDX</td>\n",
       "      <td>3.7</td>\n",
       "      <td>(6 cyl)</td>\n",
       "      <td>Auto-S5</td>\n",
       "      <td>4WD</td>\n",
       "      <td>Gasoline</td>\n",
       "      <td>CA</td>\n",
       "      <td>SUV</td>\n",
       "      <td>7</td>\n",
       "      <td>15</td>\n",
       "      <td>20</td>\n",
       "      <td>17</td>\n",
       "      <td>4</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ACURA MDX</td>\n",
       "      <td>3.7</td>\n",
       "      <td>(6 cyl)</td>\n",
       "      <td>Auto-S5</td>\n",
       "      <td>4WD</td>\n",
       "      <td>Gasoline</td>\n",
       "      <td>FA</td>\n",
       "      <td>SUV</td>\n",
       "      <td>6</td>\n",
       "      <td>15</td>\n",
       "      <td>20</td>\n",
       "      <td>17</td>\n",
       "      <td>4</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       model  displ      cyl    trans drive      fuel cert_region veh_class  \\\n",
       "0  ACURA MDX    3.7  (6 cyl)  Auto-S5   4WD  Gasoline          CA       SUV   \n",
       "1  ACURA MDX    3.7  (6 cyl)  Auto-S5   4WD  Gasoline          FA       SUV   \n",
       "\n",
       "  air_pollution_score city_mpg hwy_mpg cmb_mpg greenhouse_gas_score smartway  \n",
       "0                   7       15      20      17                    4       no  \n",
       "1                   6       15      20      17                    4       no  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# replace space with underscore _ and lowercase for all column names\n",
    "df_2008.rename(columns = lambda x: x.strip().lower().replace(\" \", \"_\"), inplace = True)\n",
    "df_2008.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>model</th>\n",
       "      <th>displ</th>\n",
       "      <th>cyl</th>\n",
       "      <th>trans</th>\n",
       "      <th>drive</th>\n",
       "      <th>fuel</th>\n",
       "      <th>cert_region</th>\n",
       "      <th>veh_class</th>\n",
       "      <th>air_pollution_score</th>\n",
       "      <th>city_mpg</th>\n",
       "      <th>hwy_mpg</th>\n",
       "      <th>cmb_mpg</th>\n",
       "      <th>greenhouse_gas_score</th>\n",
       "      <th>smartway</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ACURA RDX</td>\n",
       "      <td>3.5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>SemiAuto-6</td>\n",
       "      <td>2WD</td>\n",
       "      <td>Gasoline</td>\n",
       "      <td>FA</td>\n",
       "      <td>small SUV</td>\n",
       "      <td>3</td>\n",
       "      <td>20</td>\n",
       "      <td>28</td>\n",
       "      <td>23</td>\n",
       "      <td>5</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ACURA RDX</td>\n",
       "      <td>3.5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>SemiAuto-6</td>\n",
       "      <td>2WD</td>\n",
       "      <td>Gasoline</td>\n",
       "      <td>CA</td>\n",
       "      <td>small SUV</td>\n",
       "      <td>3</td>\n",
       "      <td>20</td>\n",
       "      <td>28</td>\n",
       "      <td>23</td>\n",
       "      <td>5</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       model  displ  cyl       trans drive      fuel cert_region  veh_class  \\\n",
       "0  ACURA RDX    3.5  6.0  SemiAuto-6   2WD  Gasoline          FA  small SUV   \n",
       "1  ACURA RDX    3.5  6.0  SemiAuto-6   2WD  Gasoline          CA  small SUV   \n",
       "\n",
       "   air_pollution_score city_mpg hwy_mpg cmb_mpg  greenhouse_gas_score smartway  \n",
       "0                    3       20      28      23                     5       No  \n",
       "1                    3       20      28      23                     5       No  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2018.rename(columns = lambda x : x.strip().lower().replace(\" \", \"_\"), inplace = True)\n",
    "df_2018.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# confirm all columns between 2008 and 2018 are identical\n",
    "(df_2008.columns == df_2018.columns).all()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save the progress datasets\n",
    "df_2008.to_csv(\"Data/data_08.csv\", index = False)\n",
    "df_2018.to_csv(\"Data/data_18.csv\", index = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Filter, Drop Nulls, Dedupe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.1. Filter only for california region\n",
    "\n",
    "For consistency, only compare cars certified by California standards. Filter both datasets using query to select only rows where cert_region is CA. Then, drop the cert_region columns, since it will no longer provide any useful information (we'll know every value is 'CA')."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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",
       "\n",
       "    .dataframe thead th {\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>model</th>\n",
       "      <th>displ</th>\n",
       "      <th>cyl</th>\n",
       "      <th>trans</th>\n",
       "      <th>drive</th>\n",
       "      <th>fuel</th>\n",
       "      <th>cert_region</th>\n",
       "      <th>veh_class</th>\n",
       "      <th>air_pollution_score</th>\n",
       "      <th>city_mpg</th>\n",
       "      <th>hwy_mpg</th>\n",
       "      <th>cmb_mpg</th>\n",
       "      <th>greenhouse_gas_score</th>\n",
       "      <th>smartway</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ACURA MDX</td>\n",
       "      <td>3.7</td>\n",
       "      <td>(6 cyl)</td>\n",
       "      <td>Auto-S5</td>\n",
       "      <td>4WD</td>\n",
       "      <td>Gasoline</td>\n",
       "      <td>CA</td>\n",
       "      <td>SUV</td>\n",
       "      <td>7</td>\n",
       "      <td>15</td>\n",
       "      <td>20</td>\n",
       "      <td>17</td>\n",
       "      <td>4</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ACURA MDX</td>\n",
       "      <td>3.7</td>\n",
       "      <td>(6 cyl)</td>\n",
       "      <td>Auto-S5</td>\n",
       "      <td>4WD</td>\n",
       "      <td>Gasoline</td>\n",
       "      <td>FA</td>\n",
       "      <td>SUV</td>\n",
       "      <td>6</td>\n",
       "      <td>15</td>\n",
       "      <td>20</td>\n",
       "      <td>17</td>\n",
       "      <td>4</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       model  displ      cyl    trans drive      fuel cert_region veh_class  \\\n",
       "0  ACURA MDX    3.7  (6 cyl)  Auto-S5   4WD  Gasoline          CA       SUV   \n",
       "1  ACURA MDX    3.7  (6 cyl)  Auto-S5   4WD  Gasoline          FA       SUV   \n",
       "\n",
       "  air_pollution_score city_mpg hwy_mpg cmb_mpg greenhouse_gas_score smartway  \n",
       "0                   7       15      20      17                    4       no  \n",
       "1                   6       15      20      17                    4       no  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get only for CA region\n",
    "df_2008.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th>displ</th>\n",
       "      <th>cyl</th>\n",
       "      <th>trans</th>\n",
       "      <th>drive</th>\n",
       "      <th>fuel</th>\n",
       "      <th>cert_region</th>\n",
       "      <th>veh_class</th>\n",
       "      <th>air_pollution_score</th>\n",
       "      <th>city_mpg</th>\n",
       "      <th>hwy_mpg</th>\n",
       "      <th>cmb_mpg</th>\n",
       "      <th>greenhouse_gas_score</th>\n",
       "      <th>smartway</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ACURA MDX</td>\n",
       "      <td>3.7</td>\n",
       "      <td>(6 cyl)</td>\n",
       "      <td>Auto-S5</td>\n",
       "      <td>4WD</td>\n",
       "      <td>Gasoline</td>\n",
       "      <td>CA</td>\n",
       "      <td>SUV</td>\n",
       "      <td>7</td>\n",
       "      <td>15</td>\n",
       "      <td>20</td>\n",
       "      <td>17</td>\n",
       "      <td>4</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ACURA RDX</td>\n",
       "      <td>2.3</td>\n",
       "      <td>(4 cyl)</td>\n",
       "      <td>Auto-S5</td>\n",
       "      <td>4WD</td>\n",
       "      <td>Gasoline</td>\n",
       "      <td>CA</td>\n",
       "      <td>SUV</td>\n",
       "      <td>7</td>\n",
       "      <td>17</td>\n",
       "      <td>22</td>\n",
       "      <td>19</td>\n",
       "      <td>5</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       model  displ      cyl    trans drive      fuel cert_region veh_class  \\\n",
       "0  ACURA MDX    3.7  (6 cyl)  Auto-S5   4WD  Gasoline          CA       SUV   \n",
       "2  ACURA RDX    2.3  (4 cyl)  Auto-S5   4WD  Gasoline          CA       SUV   \n",
       "\n",
       "  air_pollution_score city_mpg hwy_mpg cmb_mpg greenhouse_gas_score smartway  \n",
       "0                   7       15      20      17                    4       no  \n",
       "2                   7       17      22      19                    5       no  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2008 = df_2008.query('cert_region == \"CA\"')\n",
    "df_2008.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\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>model</th>\n",
       "      <th>displ</th>\n",
       "      <th>cyl</th>\n",
       "      <th>trans</th>\n",
       "      <th>drive</th>\n",
       "      <th>fuel</th>\n",
       "      <th>veh_class</th>\n",
       "      <th>air_pollution_score</th>\n",
       "      <th>city_mpg</th>\n",
       "      <th>hwy_mpg</th>\n",
       "      <th>cmb_mpg</th>\n",
       "      <th>greenhouse_gas_score</th>\n",
       "      <th>smartway</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ACURA MDX</td>\n",
       "      <td>3.7</td>\n",
       "      <td>(6 cyl)</td>\n",
       "      <td>Auto-S5</td>\n",
       "      <td>4WD</td>\n",
       "      <td>Gasoline</td>\n",
       "      <td>SUV</td>\n",
       "      <td>7</td>\n",
       "      <td>15</td>\n",
       "      <td>20</td>\n",
       "      <td>17</td>\n",
       "      <td>4</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ACURA RDX</td>\n",
       "      <td>2.3</td>\n",
       "      <td>(4 cyl)</td>\n",
       "      <td>Auto-S5</td>\n",
       "      <td>4WD</td>\n",
       "      <td>Gasoline</td>\n",
       "      <td>SUV</td>\n",
       "      <td>7</td>\n",
       "      <td>17</td>\n",
       "      <td>22</td>\n",
       "      <td>19</td>\n",
       "      <td>5</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       model  displ      cyl    trans drive      fuel veh_class  \\\n",
       "0  ACURA MDX    3.7  (6 cyl)  Auto-S5   4WD  Gasoline       SUV   \n",
       "2  ACURA RDX    2.3  (4 cyl)  Auto-S5   4WD  Gasoline       SUV   \n",
       "\n",
       "  air_pollution_score city_mpg hwy_mpg cmb_mpg greenhouse_gas_score smartway  \n",
       "0                   7       15      20      17                    4       no  \n",
       "2                   7       17      22      19                    5       no  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2008.drop(\"cert_region\", axis=1, inplace=True)\n",
    "df_2008.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th>displ</th>\n",
       "      <th>cyl</th>\n",
       "      <th>trans</th>\n",
       "      <th>drive</th>\n",
       "      <th>fuel</th>\n",
       "      <th>cert_region</th>\n",
       "      <th>veh_class</th>\n",
       "      <th>air_pollution_score</th>\n",
       "      <th>city_mpg</th>\n",
       "      <th>hwy_mpg</th>\n",
       "      <th>cmb_mpg</th>\n",
       "      <th>greenhouse_gas_score</th>\n",
       "      <th>smartway</th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ACURA RDX</td>\n",
       "      <td>3.5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>SemiAuto-6</td>\n",
       "      <td>2WD</td>\n",
       "      <td>Gasoline</td>\n",
       "      <td>CA</td>\n",
       "      <td>small SUV</td>\n",
       "      <td>3</td>\n",
       "      <td>20</td>\n",
       "      <td>28</td>\n",
       "      <td>23</td>\n",
       "      <td>5</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ACURA RDX</td>\n",
       "      <td>3.5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>SemiAuto-6</td>\n",
       "      <td>4WD</td>\n",
       "      <td>Gasoline</td>\n",
       "      <td>CA</td>\n",
       "      <td>small SUV</td>\n",
       "      <td>3</td>\n",
       "      <td>19</td>\n",
       "      <td>27</td>\n",
       "      <td>22</td>\n",
       "      <td>4</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       model  displ  cyl       trans drive      fuel cert_region  veh_class  \\\n",
       "1  ACURA RDX    3.5  6.0  SemiAuto-6   2WD  Gasoline          CA  small SUV   \n",
       "3  ACURA RDX    3.5  6.0  SemiAuto-6   4WD  Gasoline          CA  small SUV   \n",
       "\n",
       "   air_pollution_score city_mpg hwy_mpg cmb_mpg  greenhouse_gas_score smartway  \n",
       "1                    3       20      28      23                     5       No  \n",
       "3                    3       19      27      22                     4       No  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2018 dataset\n",
    "df_2018 = df_2018.query('cert_region == \"CA\"')\n",
    "df_2018.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>model</th>\n",
       "      <th>displ</th>\n",
       "      <th>cyl</th>\n",
       "      <th>trans</th>\n",
       "      <th>drive</th>\n",
       "      <th>fuel</th>\n",
       "      <th>veh_class</th>\n",
       "      <th>air_pollution_score</th>\n",
       "      <th>city_mpg</th>\n",
       "      <th>hwy_mpg</th>\n",
       "      <th>cmb_mpg</th>\n",
       "      <th>greenhouse_gas_score</th>\n",
       "      <th>smartway</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ACURA RDX</td>\n",
       "      <td>3.5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>SemiAuto-6</td>\n",
       "      <td>2WD</td>\n",
       "      <td>Gasoline</td>\n",
       "      <td>small SUV</td>\n",
       "      <td>3</td>\n",
       "      <td>20</td>\n",
       "      <td>28</td>\n",
       "      <td>23</td>\n",
       "      <td>5</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ACURA RDX</td>\n",
       "      <td>3.5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>SemiAuto-6</td>\n",
       "      <td>4WD</td>\n",
       "      <td>Gasoline</td>\n",
       "      <td>small SUV</td>\n",
       "      <td>3</td>\n",
       "      <td>19</td>\n",
       "      <td>27</td>\n",
       "      <td>22</td>\n",
       "      <td>4</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       model  displ  cyl       trans drive      fuel  veh_class  \\\n",
       "1  ACURA RDX    3.5  6.0  SemiAuto-6   2WD  Gasoline  small SUV   \n",
       "3  ACURA RDX    3.5  6.0  SemiAuto-6   4WD  Gasoline  small SUV   \n",
       "\n",
       "   air_pollution_score city_mpg hwy_mpg cmb_mpg  greenhouse_gas_score smartway  \n",
       "1                    3       20      28      23                     5       No  \n",
       "3                    3       19      27      22                     4       No  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2018.drop(\"cert_region\", axis = 1, inplace = True)\n",
    "df_2018.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.2. Drop Nulls\n",
    "\n",
    "Drop any rows in both datasets that contain missing values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "model                    0\n",
       "displ                    0\n",
       "cyl                     75\n",
       "trans                   75\n",
       "drive                   37\n",
       "fuel                     0\n",
       "veh_class                0\n",
       "air_pollution_score      0\n",
       "city_mpg                75\n",
       "hwy_mpg                 75\n",
       "cmb_mpg                 75\n",
       "greenhouse_gas_score    75\n",
       "smartway                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2008 data, check for na values\n",
    "df_2008.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop null values rows\n",
    "df_2008.dropna(axis=0, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checks if any of columns in 2008 have null values - should print False\n",
    "df_2008.isna().sum().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2018 data\n",
    "df_2018.dropna(axis = 0, inplace = True)\n",
    "df_2018.isnull().sum().any()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.3. Dedupe\n",
    "Drop any duplicate rows in both datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "duplicated rows for 2008:  23\n",
      "duplicated rows for 2018:  3\n"
     ]
    }
   ],
   "source": [
    "# check for duplicate rows\n",
    "print(\"duplicated rows for 2008: \", df_2008.duplicated().sum())\n",
    "print(\"duplicated rows for 2018: \", df_2018.duplicated().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop the duplicate rows for 2008 data\n",
    "df_2008.drop_duplicates(inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# print number of duplicates again to confirm dedupe - should be 0\n",
    "df_2008.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save the progress data\n",
    "df_2008.to_csv(\"Data/data_08.csv\", index = False)\n",
    "df_2018.to_csv(\"Data/data_18.csv\", index = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Inspecting Data Types\n",
    "\n",
    "inspect the datatypes of features in each dataset and think about what changes should be made to make them practical and consistent (in both datasets)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\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>model</th>\n",
       "      <th>displ</th>\n",
       "      <th>cyl</th>\n",
       "      <th>trans</th>\n",
       "      <th>drive</th>\n",
       "      <th>fuel</th>\n",
       "      <th>veh_class</th>\n",
       "      <th>air_pollution_score</th>\n",
       "      <th>city_mpg</th>\n",
       "      <th>hwy_mpg</th>\n",
       "      <th>cmb_mpg</th>\n",
       "      <th>greenhouse_gas_score</th>\n",
       "      <th>smartway</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ACURA MDX</td>\n",
       "      <td>3.7</td>\n",
       "      <td>(6 cyl)</td>\n",
       "      <td>Auto-S5</td>\n",
       "      <td>4WD</td>\n",
       "      <td>Gasoline</td>\n",
       "      <td>SUV</td>\n",
       "      <td>7</td>\n",
       "      <td>15</td>\n",
       "      <td>20</td>\n",
       "      <td>17</td>\n",
       "      <td>4</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ACURA RDX</td>\n",
       "      <td>2.3</td>\n",
       "      <td>(4 cyl)</td>\n",
       "      <td>Auto-S5</td>\n",
       "      <td>4WD</td>\n",
       "      <td>Gasoline</td>\n",
       "      <td>SUV</td>\n",
       "      <td>7</td>\n",
       "      <td>17</td>\n",
       "      <td>22</td>\n",
       "      <td>19</td>\n",
       "      <td>5</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       model  displ      cyl    trans drive      fuel veh_class  \\\n",
       "0  ACURA MDX    3.7  (6 cyl)  Auto-S5   4WD  Gasoline       SUV   \n",
       "2  ACURA RDX    2.3  (4 cyl)  Auto-S5   4WD  Gasoline       SUV   \n",
       "\n",
       "  air_pollution_score city_mpg hwy_mpg cmb_mpg greenhouse_gas_score smartway  \n",
       "0                   7       15      20      17                    4       no  \n",
       "2                   7       17      22      19                    5       no  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2008.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\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>model</th>\n",
       "      <th>displ</th>\n",
       "      <th>cyl</th>\n",
       "      <th>trans</th>\n",
       "      <th>drive</th>\n",
       "      <th>fuel</th>\n",
       "      <th>veh_class</th>\n",
       "      <th>air_pollution_score</th>\n",
       "      <th>city_mpg</th>\n",
       "      <th>hwy_mpg</th>\n",
       "      <th>cmb_mpg</th>\n",
       "      <th>greenhouse_gas_score</th>\n",
       "      <th>smartway</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ACURA RDX</td>\n",
       "      <td>3.5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>SemiAuto-6</td>\n",
       "      <td>2WD</td>\n",
       "      <td>Gasoline</td>\n",
       "      <td>small SUV</td>\n",
       "      <td>3</td>\n",
       "      <td>20</td>\n",
       "      <td>28</td>\n",
       "      <td>23</td>\n",
       "      <td>5</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ACURA RDX</td>\n",
       "      <td>3.5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>SemiAuto-6</td>\n",
       "      <td>4WD</td>\n",
       "      <td>Gasoline</td>\n",
       "      <td>small SUV</td>\n",
       "      <td>3</td>\n",
       "      <td>19</td>\n",
       "      <td>27</td>\n",
       "      <td>22</td>\n",
       "      <td>4</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       model  displ  cyl       trans drive      fuel  veh_class  \\\n",
       "1  ACURA RDX    3.5  6.0  SemiAuto-6   2WD  Gasoline  small SUV   \n",
       "3  ACURA RDX    3.5  6.0  SemiAuto-6   4WD  Gasoline  small SUV   \n",
       "\n",
       "   air_pollution_score city_mpg hwy_mpg cmb_mpg  greenhouse_gas_score smartway  \n",
       "1                    3       20      28      23                     5       No  \n",
       "3                    3       19      27      22                     4       No  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2018.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 986 entries, 0 to 2400\n",
      "Data columns (total 13 columns):\n",
      " #   Column                Non-Null Count  Dtype  \n",
      "---  ------                --------------  -----  \n",
      " 0   model                 986 non-null    object \n",
      " 1   displ                 986 non-null    float64\n",
      " 2   cyl                   986 non-null    object \n",
      " 3   trans                 986 non-null    object \n",
      " 4   drive                 986 non-null    object \n",
      " 5   fuel                  986 non-null    object \n",
      " 6   veh_class             986 non-null    object \n",
      " 7   air_pollution_score   986 non-null    object \n",
      " 8   city_mpg              986 non-null    object \n",
      " 9   hwy_mpg               986 non-null    object \n",
      " 10  cmb_mpg               986 non-null    object \n",
      " 11  greenhouse_gas_score  986 non-null    object \n",
      " 12  smartway              986 non-null    object \n",
      "dtypes: float64(1), object(12)\n",
      "memory usage: 107.8+ KB\n"
     ]
    }
   ],
   "source": [
    "df_2008.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 797 entries, 1 to 1609\n",
      "Data columns (total 13 columns):\n",
      " #   Column                Non-Null Count  Dtype  \n",
      "---  ------                --------------  -----  \n",
      " 0   model                 797 non-null    object \n",
      " 1   displ                 797 non-null    float64\n",
      " 2   cyl                   797 non-null    float64\n",
      " 3   trans                 797 non-null    object \n",
      " 4   drive                 797 non-null    object \n",
      " 5   fuel                  797 non-null    object \n",
      " 6   veh_class             797 non-null    object \n",
      " 7   air_pollution_score   797 non-null    int64  \n",
      " 8   city_mpg              797 non-null    object \n",
      " 9   hwy_mpg               797 non-null    object \n",
      " 10  cmb_mpg               797 non-null    object \n",
      " 11  greenhouse_gas_score  797 non-null    int64  \n",
      " 12  smartway              797 non-null    object \n",
      "dtypes: float64(2), int64(2), object(9)\n",
      "memory usage: 87.2+ KB\n"
     ]
    }
   ],
   "source": [
    "df_2018.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inspecting Results:\n",
    "\n",
    "- we can make data consistency for `cyl` column, using int data type\n",
    "-  for `air pollution` column, we can convert to float data type\n",
    "-  for `city_mpg` , `hwy_mpg`, `cmb_mpg` - need to convert them to float data type\n",
    "- for `greenhouse_gas_score` column, need to conver it to int data type"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. Fixing Data Types"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.1. Fix `cyl` datatype\n",
    "- 2008: extract int from string.\n",
    "- 2018: convert float to int."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2008: extract int from string."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6 cyl)     409\n",
       "(4 cyl)     283\n",
       "(8 cyl)     199\n",
       "(5 cyl)      48\n",
       "(12 cyl)     30\n",
       "(10 cyl)     14\n",
       "(2 cyl)       2\n",
       "(16 cyl)      1\n",
       "Name: cyl, dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check value counts for the 2008 cyl column\n",
    "df_2008[\"cyl\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th>displ</th>\n",
       "      <th>cyl</th>\n",
       "      <th>trans</th>\n",
       "      <th>drive</th>\n",
       "      <th>fuel</th>\n",
       "      <th>veh_class</th>\n",
       "      <th>air_pollution_score</th>\n",
       "      <th>city_mpg</th>\n",
       "      <th>hwy_mpg</th>\n",
       "      <th>cmb_mpg</th>\n",
       "      <th>greenhouse_gas_score</th>\n",
       "      <th>smartway</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ACURA MDX</td>\n",
       "      <td>3.7</td>\n",
       "      <td>(6 cyl)</td>\n",
       "      <td>Auto-S5</td>\n",
       "      <td>4WD</td>\n",
       "      <td>Gasoline</td>\n",
       "      <td>SUV</td>\n",
       "      <td>7</td>\n",
       "      <td>15</td>\n",
       "      <td>20</td>\n",
       "      <td>17</td>\n",
       "      <td>4</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ACURA RDX</td>\n",
       "      <td>2.3</td>\n",
       "      <td>(4 cyl)</td>\n",
       "      <td>Auto-S5</td>\n",
       "      <td>4WD</td>\n",
       "      <td>Gasoline</td>\n",
       "      <td>SUV</td>\n",
       "      <td>7</td>\n",
       "      <td>17</td>\n",
       "      <td>22</td>\n",
       "      <td>19</td>\n",
       "      <td>5</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       model  displ      cyl    trans drive      fuel veh_class  \\\n",
       "0  ACURA MDX    3.7  (6 cyl)  Auto-S5   4WD  Gasoline       SUV   \n",
       "2  ACURA RDX    2.3  (4 cyl)  Auto-S5   4WD  Gasoline       SUV   \n",
       "\n",
       "  air_pollution_score city_mpg hwy_mpg cmb_mpg greenhouse_gas_score smartway  \n",
       "0                   7       15      20      17                    4       no  \n",
       "2                   7       17      22      19                    5       no  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2008.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df_2008[\"cyl\"].str[1:-5].astype(int)\n",
    "\n",
    "df_2008['cyl'] = df_2008['cyl'].str.extract('(\\d+)').astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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>model</th>\n",
       "      <th>displ</th>\n",
       "      <th>cyl</th>\n",
       "      <th>trans</th>\n",
       "      <th>drive</th>\n",
       "      <th>fuel</th>\n",
       "      <th>veh_class</th>\n",
       "      <th>air_pollution_score</th>\n",
       "      <th>city_mpg</th>\n",
       "      <th>hwy_mpg</th>\n",
       "      <th>cmb_mpg</th>\n",
       "      <th>greenhouse_gas_score</th>\n",
       "      <th>smartway</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ACURA MDX</td>\n",
       "      <td>3.7</td>\n",
       "      <td>6</td>\n",
       "      <td>Auto-S5</td>\n",
       "      <td>4WD</td>\n",
       "      <td>Gasoline</td>\n",
       "      <td>SUV</td>\n",
       "      <td>7</td>\n",
       "      <td>15</td>\n",
       "      <td>20</td>\n",
       "      <td>17</td>\n",
       "      <td>4</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ACURA RDX</td>\n",
       "      <td>2.3</td>\n",
       "      <td>4</td>\n",
       "      <td>Auto-S5</td>\n",
       "      <td>4WD</td>\n",
       "      <td>Gasoline</td>\n",
       "      <td>SUV</td>\n",
       "      <td>7</td>\n",
       "      <td>17</td>\n",
       "      <td>22</td>\n",
       "      <td>19</td>\n",
       "      <td>5</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       model  displ  cyl    trans drive      fuel veh_class  \\\n",
       "0  ACURA MDX    3.7    6  Auto-S5   4WD  Gasoline       SUV   \n",
       "2  ACURA RDX    2.3    4  Auto-S5   4WD  Gasoline       SUV   \n",
       "\n",
       "  air_pollution_score city_mpg hwy_mpg cmb_mpg greenhouse_gas_score smartway  \n",
       "0                   7       15      20      17                    4       no  \n",
       "2                   7       17      22      19                    5       no  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2008.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6     409\n",
       "4     283\n",
       "8     199\n",
       "5      48\n",
       "12     30\n",
       "10     14\n",
       "2       2\n",
       "16      1\n",
       "Name: cyl, dtype: int64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#confirm the value counts of cyl again\n",
    "df_2008[\"cyl\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2018: convert float to int."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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>model</th>\n",
       "      <th>displ</th>\n",
       "      <th>cyl</th>\n",
       "      <th>trans</th>\n",
       "      <th>drive</th>\n",
       "      <th>fuel</th>\n",
       "      <th>veh_class</th>\n",
       "      <th>air_pollution_score</th>\n",
       "      <th>city_mpg</th>\n",
       "      <th>hwy_mpg</th>\n",
       "      <th>cmb_mpg</th>\n",
       "      <th>greenhouse_gas_score</th>\n",
       "      <th>smartway</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ACURA RDX</td>\n",
       "      <td>3.5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>SemiAuto-6</td>\n",
       "      <td>2WD</td>\n",
       "      <td>Gasoline</td>\n",
       "      <td>small SUV</td>\n",
       "      <td>3</td>\n",
       "      <td>20</td>\n",
       "      <td>28</td>\n",
       "      <td>23</td>\n",
       "      <td>5</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ACURA RDX</td>\n",
       "      <td>3.5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>SemiAuto-6</td>\n",
       "      <td>4WD</td>\n",
       "      <td>Gasoline</td>\n",
       "      <td>small SUV</td>\n",
       "      <td>3</td>\n",
       "      <td>19</td>\n",
       "      <td>27</td>\n",
       "      <td>22</td>\n",
       "      <td>4</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       model  displ  cyl       trans drive      fuel  veh_class  \\\n",
       "1  ACURA RDX    3.5  6.0  SemiAuto-6   2WD  Gasoline  small SUV   \n",
       "3  ACURA RDX    3.5  6.0  SemiAuto-6   4WD  Gasoline  small SUV   \n",
       "\n",
       "   air_pollution_score city_mpg hwy_mpg cmb_mpg  greenhouse_gas_score smartway  \n",
       "1                    3       20      28      23                     5       No  \n",
       "3                    3       19      27      22                     4       No  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2018.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_2018[\"cyl\"] = df_2018[\"cyl\"].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int32')"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2018[\"cyl\"].dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save the progress data\n",
    "df_2008.to_csv(\"Data/data_08.csv\", index = False)\n",
    "df_2018.to_csv(\"Data/data_18.csv\", index = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.2. Fix `air_pollution_score` datatype\n",
    "\n",
    "- 2008: convert string to float.\n",
    "- 2018: convert int to float."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2008: convert string to float."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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>model</th>\n",
       "      <th>displ</th>\n",
       "      <th>cyl</th>\n",
       "      <th>trans</th>\n",
       "      <th>drive</th>\n",
       "      <th>fuel</th>\n",
       "      <th>veh_class</th>\n",
       "      <th>air_pollution_score</th>\n",
       "      <th>city_mpg</th>\n",
       "      <th>hwy_mpg</th>\n",
       "      <th>cmb_mpg</th>\n",
       "      <th>greenhouse_gas_score</th>\n",
       "      <th>smartway</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ACURA MDX</td>\n",
       "      <td>3.7</td>\n",
       "      <td>6</td>\n",
       "      <td>Auto-S5</td>\n",
       "      <td>4WD</td>\n",
       "      <td>Gasoline</td>\n",
       "      <td>SUV</td>\n",
       "      <td>7</td>\n",
       "      <td>15</td>\n",
       "      <td>20</td>\n",
       "      <td>17</td>\n",
       "      <td>4</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ACURA RDX</td>\n",
       "      <td>2.3</td>\n",
       "      <td>4</td>\n",
       "      <td>Auto-S5</td>\n",
       "      <td>4WD</td>\n",
       "      <td>Gasoline</td>\n",
       "      <td>SUV</td>\n",
       "      <td>7</td>\n",
       "      <td>17</td>\n",
       "      <td>22</td>\n",
       "      <td>19</td>\n",
       "      <td>5</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       model  displ  cyl    trans drive      fuel veh_class  \\\n",
       "0  ACURA MDX    3.7    6  Auto-S5   4WD  Gasoline       SUV   \n",
       "2  ACURA RDX    2.3    4  Auto-S5   4WD  Gasoline       SUV   \n",
       "\n",
       "  air_pollution_score city_mpg hwy_mpg cmb_mpg greenhouse_gas_score smartway  \n",
       "0                   7       15      20      17                    4       no  \n",
       "2                   7       17      22      19                    5       no  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2008.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "could not convert string to float: '6/4'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-48-b89f0424d0ad>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf_2008\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"air_pollution_score\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdf_2008\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"air_pollution_score\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfloat\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\.conda\\envs\\venv-datascience\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36mastype\u001b[1;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[0;32m   5696\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5697\u001b[0m             \u001b[1;31m# else, only a single dtype is given\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 5698\u001b[1;33m             \u001b[0mnew_data\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   5699\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_constructor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnew_data\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__finalize__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5700\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\.conda\\envs\\venv-datascience\\lib\\site-packages\\pandas\\core\\internals\\managers.py\u001b[0m in \u001b[0;36mastype\u001b[1;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[0;32m    580\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    581\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mastype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mstr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"raise\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 582\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"astype\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    583\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    584\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mconvert\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\.conda\\envs\\venv-datascience\\lib\\site-packages\\pandas\\core\\internals\\managers.py\u001b[0m in \u001b[0;36mapply\u001b[1;34m(self, f, filter, **kwargs)\u001b[0m\n\u001b[0;32m    440\u001b[0m                 \u001b[0mapplied\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mb\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    441\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 442\u001b[1;33m                 \u001b[0mapplied\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mb\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    443\u001b[0m             \u001b[0mresult_blocks\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_extend_blocks\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mapplied\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mresult_blocks\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    444\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\.conda\\envs\\venv-datascience\\lib\\site-packages\\pandas\\core\\internals\\blocks.py\u001b[0m in \u001b[0;36mastype\u001b[1;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[0;32m    623\u001b[0m             \u001b[0mvals1d\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mvalues\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    624\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 625\u001b[1;33m                 \u001b[0mvalues\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mastype_nansafe\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvals1d\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    626\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mValueError\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    627\u001b[0m                 \u001b[1;31m# e.g. astype_nansafe can fail on object-dtype of strings\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\.conda\\envs\\venv-datascience\\lib\\site-packages\\pandas\\core\\dtypes\\cast.py\u001b[0m in \u001b[0;36mastype_nansafe\u001b[1;34m(arr, dtype, copy, skipna)\u001b[0m\n\u001b[0;32m    895\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mcopy\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mis_object_dtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marr\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mis_object_dtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    896\u001b[0m         \u001b[1;31m# Explicit copy, or required since NumPy can't view from / to object.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 897\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0marr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    898\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    899\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0marr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mview\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: could not convert string to float: '6/4'"
     ]
    }
   ],
   "source": [
    "df_2008[\"air_pollution_score\"] = df_2008[\"air_pollution_score\"].astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>model</th>\n",
       "      <th>displ</th>\n",
       "      <th>cyl</th>\n",
       "      <th>trans</th>\n",
       "      <th>drive</th>\n",
       "      <th>fuel</th>\n",
       "      <th>veh_class</th>\n",
       "      <th>air_pollution_score</th>\n",
       "      <th>city_mpg</th>\n",
       "      <th>hwy_mpg</th>\n",
       "      <th>cmb_mpg</th>\n",
       "      <th>greenhouse_gas_score</th>\n",
       "      <th>smartway</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1550</th>\n",
       "      <td>MERCEDES-BENZ C300</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6</td>\n",
       "      <td>Auto-L7</td>\n",
       "      <td>2WD</td>\n",
       "      <td>ethanol/gas</td>\n",
       "      <td>small car</td>\n",
       "      <td>6/4</td>\n",
       "      <td>13/18</td>\n",
       "      <td>19/25</td>\n",
       "      <td>15/21</td>\n",
       "      <td>7/6</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   model  displ  cyl    trans drive         fuel  veh_class  \\\n",
       "1550  MERCEDES-BENZ C300    3.0    6  Auto-L7   2WD  ethanol/gas  small car   \n",
       "\n",
       "     air_pollution_score city_mpg hwy_mpg cmb_mpg greenhouse_gas_score  \\\n",
       "1550                 6/4    13/18   19/25   15/21                  7/6   \n",
       "\n",
       "     smartway  \n",
       "1550       no  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check error happening row value\n",
    "err_position = df_2008.query('air_pollution_score == \"6/4\"')\n",
    "err_position"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "According to [resource](http://www.fueleconomy.gov/feg/findacarhelp.shtml#airPollutionScore),\n",
    "\n",
    "- If a vehicle can operate on more than one type of fuel, an estimate is provided for each fuel type.\n",
    "\n",
    "### Findings\n",
    "so we need to check for those cars which can have more than one type of fuel. The above car is using `fuel = ethanol/gas`\n",
    "\n",
    "Columns with `/` for holding more than 2 values as string:\n",
    "- fuel\n",
    "- air_pollution_score\n",
    "- city_mpg\n",
    "- hwy_mpg\n",
    "- cmb_mpg\n",
    "- greenhouse_gas_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### get all hybirds in 2008"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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",
       "    .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>model</th>\n",
       "      <th>displ</th>\n",
       "      <th>cyl</th>\n",
       "      <th>trans</th>\n",
       "      <th>drive</th>\n",
       "      <th>fuel</th>\n",
       "      <th>veh_class</th>\n",
       "      <th>air_pollution_score</th>\n",
       "      <th>city_mpg</th>\n",
       "      <th>hwy_mpg</th>\n",
       "      <th>cmb_mpg</th>\n",
       "      <th>greenhouse_gas_score</th>\n",
       "      <th>smartway</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1550</th>\n",
       "      <td>MERCEDES-BENZ C300</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6</td>\n",
       "      <td>Auto-L7</td>\n",
       "      <td>2WD</td>\n",
       "      <td>ethanol/gas</td>\n",
       "      <td>small car</td>\n",
       "      <td>6/4</td>\n",
       "      <td>13/18</td>\n",
       "      <td>19/25</td>\n",
       "      <td>15/21</td>\n",
       "      <td>7/6</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   model  displ  cyl    trans drive         fuel  veh_class  \\\n",
       "1550  MERCEDES-BENZ C300    3.0    6  Auto-L7   2WD  ethanol/gas  small car   \n",
       "\n",
       "     air_pollution_score city_mpg hwy_mpg cmb_mpg greenhouse_gas_score  \\\n",
       "1550                 6/4    13/18   19/25   15/21                  7/6   \n",
       "\n",
       "     smartway  \n",
       "1550       no  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hb_2008 = df_2008[df_2008[\"fuel\"].str.contains(\"/\")]\n",
    "hb_2008.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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>model</th>\n",
       "      <th>displ</th>\n",
       "      <th>cyl</th>\n",
       "      <th>trans</th>\n",
       "      <th>drive</th>\n",
       "      <th>fuel</th>\n",
       "      <th>veh_class</th>\n",
       "      <th>air_pollution_score</th>\n",
       "      <th>city_mpg</th>\n",
       "      <th>hwy_mpg</th>\n",
       "      <th>cmb_mpg</th>\n",
       "      <th>greenhouse_gas_score</th>\n",
       "      <th>smartway</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>BMW 330e</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>SemiAuto-8</td>\n",
       "      <td>2WD</td>\n",
       "      <td>Gasoline/Electricity</td>\n",
       "      <td>small car</td>\n",
       "      <td>3</td>\n",
       "      <td>28/66</td>\n",
       "      <td>34/78</td>\n",
       "      <td>30/71</td>\n",
       "      <td>10</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>BMW 530e</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>SemiAuto-8</td>\n",
       "      <td>2WD</td>\n",
       "      <td>Gasoline/Electricity</td>\n",
       "      <td>small car</td>\n",
       "      <td>7</td>\n",
       "      <td>27/70</td>\n",
       "      <td>31/75</td>\n",
       "      <td>29/72</td>\n",
       "      <td>10</td>\n",
       "      <td>Elite</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>BMW 530e</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>SemiAuto-8</td>\n",
       "      <td>4WD</td>\n",
       "      <td>Gasoline/Electricity</td>\n",
       "      <td>small car</td>\n",
       "      <td>7</td>\n",
       "      <td>27/66</td>\n",
       "      <td>31/68</td>\n",
       "      <td>28/67</td>\n",
       "      <td>10</td>\n",
       "      <td>Elite</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>BMW 740e</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>SemiAuto-8</td>\n",
       "      <td>4WD</td>\n",
       "      <td>Gasoline/Electricity</td>\n",
       "      <td>large car</td>\n",
       "      <td>3</td>\n",
       "      <td>25/62</td>\n",
       "      <td>29/68</td>\n",
       "      <td>27/64</td>\n",
       "      <td>9</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>382</th>\n",
       "      <td>CHEVROLET Impala</td>\n",
       "      <td>3.6</td>\n",
       "      <td>6</td>\n",
       "      <td>SemiAuto-6</td>\n",
       "      <td>2WD</td>\n",
       "      <td>Ethanol/Gas</td>\n",
       "      <td>large car</td>\n",
       "      <td>5</td>\n",
       "      <td>14/18</td>\n",
       "      <td>20/28</td>\n",
       "      <td>16/22</td>\n",
       "      <td>4</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                model  displ  cyl       trans drive                  fuel  \\\n",
       "108          BMW 330e    2.0    4  SemiAuto-8   2WD  Gasoline/Electricity   \n",
       "160          BMW 530e    2.0    4  SemiAuto-8   2WD  Gasoline/Electricity   \n",
       "162          BMW 530e    2.0    4  SemiAuto-8   4WD  Gasoline/Electricity   \n",
       "188          BMW 740e    2.0    4  SemiAuto-8   4WD  Gasoline/Electricity   \n",
       "382  CHEVROLET Impala    3.6    6  SemiAuto-6   2WD           Ethanol/Gas   \n",
       "\n",
       "     veh_class  air_pollution_score city_mpg hwy_mpg cmb_mpg  \\\n",
       "108  small car                    3    28/66   34/78   30/71   \n",
       "160  small car                    7    27/70   31/75   29/72   \n",
       "162  small car                    7    27/66   31/68   28/67   \n",
       "188  large car                    3    25/62   29/68   27/64   \n",
       "382  large car                    5    14/18   20/28   16/22   \n",
       "\n",
       "     greenhouse_gas_score smartway  \n",
       "108                    10      Yes  \n",
       "160                    10    Elite  \n",
       "162                    10    Elite  \n",
       "188                     9      Yes  \n",
       "382                     4       No  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get all hybrids in 2018\n",
    "hb_2018 = df_2018[df_2018[\"fuel\"].str.contains(\"/\")]\n",
    "hb_2018.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As each row needs to be split into two rows; One for each specific fuel type (separated by `/`)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create two copies of 2008 hybrid df\n",
    "df1 = hb_2008.copy() # data on first fuel type of each hybrid vehicle\n",
    "df2 = hb_2008.copy() # data on second fuel type of each hybrid vehicle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "# affected columns process\n",
    "columns_to_be_splited = [\"fuel\", \"air_pollution_score\", \"city_mpg\", \"hwy_mpg\", \"cmb_mpg\", \"greenhouse_gas_score\"]\n",
    "\n",
    "# split each column\n",
    "for col in columns_to_be_splited:\n",
    "    df1[col] = df1[col].apply(lambda x: x.split(\"/\")[0]) # first fuel type value\n",
    "    df2[col] = df2[col].apply(lambda x: x.split(\"/\")[1]) # second fuel type value value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th>displ</th>\n",
       "      <th>cyl</th>\n",
       "      <th>trans</th>\n",
       "      <th>drive</th>\n",
       "      <th>fuel</th>\n",
       "      <th>veh_class</th>\n",
       "      <th>air_pollution_score</th>\n",
       "      <th>city_mpg</th>\n",
       "      <th>hwy_mpg</th>\n",
       "      <th>cmb_mpg</th>\n",
       "      <th>greenhouse_gas_score</th>\n",
       "      <th>smartway</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1550</th>\n",
       "      <td>MERCEDES-BENZ C300</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6</td>\n",
       "      <td>Auto-L7</td>\n",
       "      <td>2WD</td>\n",
       "      <td>ethanol</td>\n",
       "      <td>small car</td>\n",
       "      <td>6</td>\n",
       "      <td>13</td>\n",
       "      <td>19</td>\n",
       "      <td>15</td>\n",
       "      <td>7</td>\n",
       "      <td>no</td>\n",
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      ],
      "text/plain": [
       "                   model  displ  cyl    trans drive     fuel  veh_class  \\\n",
       "1550  MERCEDES-BENZ C300    3.0    6  Auto-L7   2WD  ethanol  small car   \n",
       "\n",
       "     air_pollution_score city_mpg hwy_mpg cmb_mpg greenhouse_gas_score  \\\n",
       "1550                   6       13      19      15                    7   \n",
       "\n",
       "     smartway  \n",
       "1550       no  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th>displ</th>\n",
       "      <th>cyl</th>\n",
       "      <th>trans</th>\n",
       "      <th>drive</th>\n",
       "      <th>fuel</th>\n",
       "      <th>veh_class</th>\n",
       "      <th>air_pollution_score</th>\n",
       "      <th>city_mpg</th>\n",
       "      <th>hwy_mpg</th>\n",
       "      <th>cmb_mpg</th>\n",
       "      <th>greenhouse_gas_score</th>\n",
       "      <th>smartway</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1550</th>\n",
       "      <td>MERCEDES-BENZ C300</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6</td>\n",
       "      <td>Auto-L7</td>\n",
       "      <td>2WD</td>\n",
       "      <td>gas</td>\n",
       "      <td>small car</td>\n",
       "      <td>4</td>\n",
       "      <td>18</td>\n",
       "      <td>25</td>\n",
       "      <td>21</td>\n",
       "      <td>6</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   model  displ  cyl    trans drive fuel  veh_class  \\\n",
       "1550  MERCEDES-BENZ C300    3.0    6  Auto-L7   2WD  gas  small car   \n",
       "\n",
       "     air_pollution_score city_mpg hwy_mpg cmb_mpg greenhouse_gas_score  \\\n",
       "1550                   4       18      25      21                    6   \n",
       "\n",
       "     smartway  \n",
       "1550       no  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th>displ</th>\n",
       "      <th>cyl</th>\n",
       "      <th>trans</th>\n",
       "      <th>drive</th>\n",
       "      <th>fuel</th>\n",
       "      <th>veh_class</th>\n",
       "      <th>air_pollution_score</th>\n",
       "      <th>city_mpg</th>\n",
       "      <th>hwy_mpg</th>\n",
       "      <th>cmb_mpg</th>\n",
       "      <th>greenhouse_gas_score</th>\n",
       "      <th>smartway</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1550</th>\n",
       "      <td>MERCEDES-BENZ C300</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6</td>\n",
       "      <td>Auto-L7</td>\n",
       "      <td>2WD</td>\n",
       "      <td>ethanol</td>\n",
       "      <td>small car</td>\n",
       "      <td>6</td>\n",
       "      <td>13</td>\n",
       "      <td>19</td>\n",
       "      <td>15</td>\n",
       "      <td>7</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1550</th>\n",
       "      <td>MERCEDES-BENZ C300</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6</td>\n",
       "      <td>Auto-L7</td>\n",
       "      <td>2WD</td>\n",
       "      <td>gas</td>\n",
       "      <td>small car</td>\n",
       "      <td>4</td>\n",
       "      <td>18</td>\n",
       "      <td>25</td>\n",
       "      <td>21</td>\n",
       "      <td>6</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   model  displ  cyl    trans drive     fuel  veh_class  \\\n",
       "1550  MERCEDES-BENZ C300    3.0    6  Auto-L7   2WD  ethanol  small car   \n",
       "1550  MERCEDES-BENZ C300    3.0    6  Auto-L7   2WD      gas  small car   \n",
       "\n",
       "     air_pollution_score city_mpg hwy_mpg cmb_mpg greenhouse_gas_score  \\\n",
       "1550                   6       13      19      15                    7   \n",
       "1550                   4       18      25      21                    6   \n",
       "\n",
       "     smartway  \n",
       "1550       no  \n",
       "1550       no  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# combine dataframes to add to the original dataframe\n",
    "new_rows = df1.append(df2)\n",
    "\n",
    "# now we have separate rows for each fuel type of each vehicle!\n",
    "new_rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop the original hybrid rows\n",
    "df_2008.drop(hb_2008.index, inplace = True)\n",
    "\n",
    "# add newly fixed rows\n",
    "df_2008 = df_2008.append(new_rows, ignore_index = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# recheck whether \"/\" values are gone\n",
    "df_2008[\"fuel\"].str.contains(\"/\").sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(987, 13)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2008.shape # 1 additional row for newly sperated one"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Repeat process for 2018: get all hybirds in 2018"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create two copies of 2018 hybrid df\n",
    "df1 = hb_2018.copy()\n",
    "df2 = hb_2018.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "# affected columns process\n",
    "columns_to_be_splited = [\"fuel\", \"city_mpg\", \"hwy_mpg\", \"cmb_mpg\"]\n",
    "\n",
    "# split each column\n",
    "for col in columns_to_be_splited:\n",
    "    df1[col] = df1[col].apply(lambda x: x.split(\"/\")[0]) # first fuel type value\n",
    "    df2[col] = df2[col].apply(lambda x: x.split(\"/\")[1]) # second fuel type value value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "# combine two df\n",
    "new_rows = df1.append(df2)\n",
    "\n",
    "# drop the original non sperated rows of hybrid datset from original 2018 dataset\n",
    "df_2018.drop(hb_2018.index, inplace = True)\n",
    "\n",
    "# append original 2018 dataset with newly created rows\n",
    "df_2018 = df_2018.append(new_rows, ignore_index = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check \"/\" still there or not\n",
    "df_2018[\"fuel\"].str.contains(\"/\").sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(835, 13)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2018.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### continue changes for `air_pollution_score`:\n",
    "- 2008: convert string to float.\n",
    "- 2018: convert int to float."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_2008[\"air_pollution_score\"] = df_2008[\"air_pollution_score\"].astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_2018[\"air_pollution_score\"] = df_2018[\"air_pollution_score\"].astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save the progress\n",
    "df_2008.to_csv(\"Data/data_08.csv\", index = False)\n",
    "df_2018.to_csv(\"Data/data_18.csv\", index = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.3. Fix `city_mpg`, `hwy_mpg`, `cmb_mpg` datatypes\n",
    "- 2008 and 2018: convert string to float."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ACURA MDX</td>\n",
       "      <td>3.7</td>\n",
       "      <td>6</td>\n",
       "      <td>Auto-S5</td>\n",
       "      <td>4WD</td>\n",
       "      <td>Gasoline</td>\n",
       "      <td>SUV</td>\n",
       "      <td>7.0</td>\n",
       "      <td>15</td>\n",
       "      <td>20</td>\n",
       "      <td>17</td>\n",
       "      <td>4</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ACURA RDX</td>\n",
       "      <td>2.3</td>\n",
       "      <td>4</td>\n",
       "      <td>Auto-S5</td>\n",
       "      <td>4WD</td>\n",
       "      <td>Gasoline</td>\n",
       "      <td>SUV</td>\n",
       "      <td>7.0</td>\n",
       "      <td>17</td>\n",
       "      <td>22</td>\n",
       "      <td>19</td>\n",
       "      <td>5</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       model  displ  cyl    trans drive      fuel veh_class  \\\n",
       "0  ACURA MDX    3.7    6  Auto-S5   4WD  Gasoline       SUV   \n",
       "1  ACURA RDX    2.3    4  Auto-S5   4WD  Gasoline       SUV   \n",
       "\n",
       "   air_pollution_score city_mpg hwy_mpg cmb_mpg greenhouse_gas_score smartway  \n",
       "0                  7.0       15      20      17                    4       no  \n",
       "1                  7.0       17      22      19                    5       no  "
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2008.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_2008[\"city_mpg\"] = df_2008[\"city_mpg\"].astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_2008[\"hwy_mpg\"] = df_2008[\"hwy_mpg\"].astype(float)\n",
    "df_2008[\"cmb_mpg\"] = df_2008[\"cmb_mpg\"].astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_2018[\"city_mpg\"] = df_2018[\"city_mpg\"].astype(float)\n",
    "df_2018[\"hwy_mpg\"] = df_2018[\"hwy_mpg\"].astype(float)\n",
    "df_2018[\"cmb_mpg\"] = df_2018[\"cmb_mpg\"].astype(float)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "--------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.4. Fix `greenhouse_gas_score` datatype\n",
    "- 2008: convert from float to int."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_2008[\"greenhouse_gas_score\"] = df_2008[\"greenhouse_gas_score\"].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "model                    object\n",
       "displ                   float64\n",
       "cyl                       int32\n",
       "trans                    object\n",
       "drive                    object\n",
       "fuel                     object\n",
       "veh_class                object\n",
       "air_pollution_score     float64\n",
       "city_mpg                float64\n",
       "hwy_mpg                 float64\n",
       "cmb_mpg                 float64\n",
       "greenhouse_gas_score      int32\n",
       "smartway                 object\n",
       "dtype: object"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check all data type again\n",
    "df_2008.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "model                    object\n",
       "displ                   float64\n",
       "cyl                       int32\n",
       "trans                    object\n",
       "drive                    object\n",
       "fuel                     object\n",
       "veh_class                object\n",
       "air_pollution_score     float64\n",
       "city_mpg                float64\n",
       "hwy_mpg                 float64\n",
       "cmb_mpg                 float64\n",
       "greenhouse_gas_score      int64\n",
       "smartway                 object\n",
       "dtype: object"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2018.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((987, 13), (835, 13))"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2008.shape, df_2018.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6. Exploring with Visuals"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.1. distributions of greenhouse gas score in 2008 and 2018"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "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>model</th>\n",
       "      <th>displ</th>\n",
       "      <th>cyl</th>\n",
       "      <th>trans</th>\n",
       "      <th>drive</th>\n",
       "      <th>fuel</th>\n",
       "      <th>veh_class</th>\n",
       "      <th>air_pollution_score</th>\n",
       "      <th>city_mpg</th>\n",
       "      <th>hwy_mpg</th>\n",
       "      <th>cmb_mpg</th>\n",
       "      <th>greenhouse_gas_score</th>\n",
       "      <th>smartway</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ACURA MDX</td>\n",
       "      <td>3.7</td>\n",
       "      <td>6</td>\n",
       "      <td>Auto-S5</td>\n",
       "      <td>4WD</td>\n",
       "      <td>Gasoline</td>\n",
       "      <td>SUV</td>\n",
       "      <td>7.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>4</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ACURA RDX</td>\n",
       "      <td>2.3</td>\n",
       "      <td>4</td>\n",
       "      <td>Auto-S5</td>\n",
       "      <td>4WD</td>\n",
       "      <td>Gasoline</td>\n",
       "      <td>SUV</td>\n",
       "      <td>7.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>5</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       model  displ  cyl    trans drive      fuel veh_class  \\\n",
       "0  ACURA MDX    3.7    6  Auto-S5   4WD  Gasoline       SUV   \n",
       "1  ACURA RDX    2.3    4  Auto-S5   4WD  Gasoline       SUV   \n",
       "\n",
       "   air_pollution_score  city_mpg  hwy_mpg  cmb_mpg  greenhouse_gas_score  \\\n",
       "0                  7.0      15.0     20.0     17.0                     4   \n",
       "1                  7.0      17.0     22.0     19.0                     5   \n",
       "\n",
       "  smartway  \n",
       "0       no  \n",
       "1       no  "
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2008.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "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>model</th>\n",
       "      <th>displ</th>\n",
       "      <th>cyl</th>\n",
       "      <th>trans</th>\n",
       "      <th>drive</th>\n",
       "      <th>fuel</th>\n",
       "      <th>veh_class</th>\n",
       "      <th>air_pollution_score</th>\n",
       "      <th>city_mpg</th>\n",
       "      <th>hwy_mpg</th>\n",
       "      <th>cmb_mpg</th>\n",
       "      <th>greenhouse_gas_score</th>\n",
       "      <th>smartway</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ACURA RDX</td>\n",
       "      <td>3.5</td>\n",
       "      <td>6</td>\n",
       "      <td>SemiAuto-6</td>\n",
       "      <td>2WD</td>\n",
       "      <td>Gasoline</td>\n",
       "      <td>small SUV</td>\n",
       "      <td>3.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>5</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ACURA RDX</td>\n",
       "      <td>3.5</td>\n",
       "      <td>6</td>\n",
       "      <td>SemiAuto-6</td>\n",
       "      <td>4WD</td>\n",
       "      <td>Gasoline</td>\n",
       "      <td>small SUV</td>\n",
       "      <td>3.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>4</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       model  displ  cyl       trans drive      fuel  veh_class  \\\n",
       "0  ACURA RDX    3.5    6  SemiAuto-6   2WD  Gasoline  small SUV   \n",
       "1  ACURA RDX    3.5    6  SemiAuto-6   4WD  Gasoline  small SUV   \n",
       "\n",
       "   air_pollution_score  city_mpg  hwy_mpg  cmb_mpg  greenhouse_gas_score  \\\n",
       "0                  3.0      20.0     28.0     23.0                     5   \n",
       "1                  3.0      19.0     27.0     22.0                     4   \n",
       "\n",
       "  smartway  \n",
       "0       No  \n",
       "1       No  "
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2018.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "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": [
    "df_2008[\"greenhouse_gas_score\"].hist();\n",
    "plt.title(\"Green House Gas Score 2008\")\n",
    "plt.xlabel(\"Green House Gas Score\")\n",
    "plt.ylabel(\"Frequency\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As per the histogram above, 2008 Greenhouse Gas Score is left schewed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "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": [
    "df_2018[\"greenhouse_gas_score\"].hist();\n",
    "plt.title(\"Green House Gas Score 2018\")\n",
    "plt.xlabel(\"Green House Gas Score\")\n",
    "plt.ylabel(\"Frequency\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As per the histogram above, 2018 Greenhouse Gas Score is right schewed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.2. distribution of combined mpg changed from 2008 to 2018"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "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": [
    "df_2008[\"cmb_mpg\"].hist(bins = 20);\n",
    "plt.title(\"Combined mpg 2008 Histogram\")\n",
    "plt.xlabel(\"combined mpg score\")\n",
    "plt.ylabel(\"frequency\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "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": [
    "df_2018[\"cmb_mpg\"].hist(bins = 20);\n",
    "plt.title(\"Combined mpg 2018 Histogram\")\n",
    "plt.xlabel(\"combined mpg score\")\n",
    "plt.ylabel(\"frequency\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As per two histograms above, both 2008 and 2018 Combined MPG are both right schewed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.3.correlation between displacement and combined mpg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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p2Dp0pTGoVzV7Wlrihu1paWFQr+oClcgYY4pbh640arp2Ztq5tVRVltGtcwVVlWVMO7eWmq6dU89sjDEdUF4C4SJSjtMd5DpVPVtE7sHpwvM/7iRfV9WF+SiL34QxAxk7vA9rN+9gUK9qqzCMMSaJfLWeugJYBnT3DPuhqs7M0/qTquna2SoLY4yJIOePp0RkEHAWTqfuxhhjSlg+Yhq3A1cBLb7hN4rIYhGZLiKBf+aLyGUiUi8i9U1NTTkvqDHGmORyWmmIyNnARlWd7xt1DXAY8BmgNzA1aH5VvVNV61S1rm/frPUhYowxJkO5vtMYC0wQkZXAQ8DJIvKAqjaqYxfwv8CxOS6HMcaYLMhppaGq16jqIFUdClwIvKCqF4nIAAAREWASsCSX5TDGGJMdhco99RcR6QsIsBD4ZoHKYYwxJg15qzRU9UXgRff3k/O1XmOMMdnTod8IN8YYkx6rNIwxxkRmlYYxxpjIrNIwxhgTmVUaxhhjIrNKwxhjTGRWaRhjjIms3Vcazdt3sWjNFuv32xhjsqBQb4TnxeyF65j6yGIqy8rY09LCtHNrmTBmYKGLZYwxJavd3mk0b9/F1EcWs3NPC9t27WXnnhauemSx3XEYY0wbtNtKY+3mHVSWxW9eZVkZazfvKFCJjDGm9LXbSmNQr2r2tMT3+7SnpYVBvaoLVCJjjCl97bbSqOnamWnn1lJVWUa3zhVUVZYx7dxa6wvcGGPaoF0HwieMGcjY4X1Yu3kHg3pVW4VhjDFt1K4rDXDuOKyyMMaY7Gi3j6eMMcZkn1UaxhhjIrNKwxhjTGR5qTREpFxE3hKRx93Pw0TkdRFZLiIPi0infJTDGGNM2+TrTuMKYJnn8y3AdFUdAWwGvpGnchhjjGmDnFcaIjIIOAu4y/0swMnATHeSe4FJuS6HMcaYtsvHncbtwFVA7PXsGmCLqu51P68FArMIishlIlIvIvVNTU25L6kxxpikclppiMjZwEZVne8dHDCpBs2vqneqap2q1vXt2zcnZTTGGBNdrl/uGwtMEJEzgSqgO86dR08RqXDvNgYBH+a4HMYYY7Igp3caqnqNqg5S1aHAhcALqvpfwBxgsjvZJcDsXJbDGGNMdhTqPY2pwJUisgInxnF3gcphjDEmDXnLPaWqLwIvur9/ABybr3UbY4zJDnsj3BhjTGRWaRhjjInMKg1jjDGRWaVhjDEmMqs0jDHGRGaVhjHGmMis0jDGGBOZVRrGGGMis0rDGGNMZFZpGGOMicwqDWOMMZFZpWGMMSYyqzSMMcZEZpWGMcaYyKzSMMYYE5lVGsYYYyKzSsMYY0xkVmkYY4yJLKeVhohUicgbIrJIRN4RkZ+5w+8RkQYRWej+jMllOYwxxmRHrvsI3wWcrKrbRaQSeElEnnLH/VBVZ+Z4/cYYY7Iop5WGqiqw3f1Y6f5oLtdpjDEmd3Ie0xCRchFZCGwEnlXV191RN4rIYhGZLiKdc10OY4wxbZfzSkNV96nqGGAQcKyIHAlcAxwGfAboDUwNmldELhORehGpb2pqynVRjTHGpJC31lOqugV4EThdVRvVsQv4X+DYkHnuVNU6Va3r27dvvopqjDEmRK5bT/UVkZ7u79XAF4B3RWSAO0yAScCSXJajVDVv38WiNVto3r4r6TBjjMmXXLeeGgDcKyLlOBXUDFV9XEReEJG+gAALgW/muBwlZ/bCdUx9ZDGVZWXsaWlh2rm1KCQMmzBmYKGLaozpQMRp4FT86urqtL6+vtDFyIvm7bsYe8sL7NzT0jqsc0UZoOzau/94VVWW8fLUk6npau0IjDHBRGS+qtZla3n2RngRWrt5B5Vl8YemvEwol/hhlWVlrN28I59FM8Z0cLl+PGUyMKhXNXtaWuKG7WtR/K+47GlpYVCv6jyWzBjT0dmdRhGq6dqZaefWUlVZRrfOFVRVlvHLybX8cvLouGHTzq21R1PGmLyyO40iNWHMQMYO78PazTsY1Ku6tXIIGmaMMflilUYRq+naOaFiCBpmjDH5Yo+njDHGRGaVhjHGmMis0jDGGBOZVRoUT2qO+oZmbnvmPeobmgtaDmOMCdPhA+FB6ToKkZrjorte46UVTmXxmxdWMG54Dfdfenzey2GMMcl06DuN5u27mPrIYnbuaWHbrr3s3NPCVY8szvsdR31Dc2uFETNvRbPdcRhjik6HrjSC0nUUIjXH3OUfpTXcGGMKJfLjKRH5B4ldtf4HqAfuUNWd2SxYPgSl6yhEao7xI/rwmxdWBA43xphiks6dxgc4/X3/yf3ZCmwAPuV+LjlB6ToKkZqjblgN44bXxA0bN7yGumE1IXMYY0xhpBMI/7Sqjvd8/oeIzFXV8SLyTrYLli8TxgzkoB5VzF3+EeNH9Il8oW7eviur6Tzuv/R46hua0y6HMcbkUzqVRl8RGaKqqwFEZAgQe36yO+sly5OfzHqb+15bDTitli4+YQjXTzwq6Ty5anFVN8zuLowxxS2dx1PfB14SkTki8iIwD/ihiHQB7s1F4XJtxYZtrRVGzH2vrmbFhm2h8xRLiytjjCmEyHcaqvqkiIwADsPppvVdT/D79lwULtcWrtkSOnx4v26B42ItrnayP4Aea3FliQSNMe1dOq2nqoBvAZ/DaUU1T0T+WIqtpmLGDO6Z1nAonhZXxhhTCOk8nroPOAL4LfA7YBRwf7IZRKRKRN4QkUUi8o6I/MwdPkxEXheR5SLysIh0ynQD2mJ4v25cfMKQuGEXnzAk9C4DiqfFlTHGFEI6gfCRqjra83mOiCxKMc8u4GRV3S4ilTgxkaeAK4HpqvqQiPwR+Abwh7RKniXHHNybh95YjVCG0kLdwb1TzhPWQZIxxrR36dxpvCUircmQROQ44OVkM6hju/ux0v1R4GRgpjv8XmBSGuXImlhQe/c+2LWvhd37iBzUrunamdGDe1qFYYzpUNKpNI4DXhGRlSKyEngV+LyIvC0ii8NmEpFyEVkIbASeBf4NbFHVve4ka4HA9qoicpmI1ItIfVNTUxpFjaZY0ogYY0ypSOfx1OmZrEBV9wFjRKQn8Hfg8KDJQua9E7gToK6uLnCatrCgtjHGpCfynYaqrsJJHdIDqIn9qOoqd1yq+bcALwLHAz1FJFZhDQI+TLPcWWFBbWOMSU86TW5/Dnwd5/FS7K/+WHwibJ6+wB5V3SIi1cAXgFuAOcBk4CHgEmB2JoXPhgljBjJqQHcWrtnCmME9Gd6vG88vXc8zSzdw2qh+nDKqf+B8d8xZzqzFjUyqHcDlJ43Ic6mNMaYwRDXaUx8ReQ84SlUjpwwRkVqcQHc5zl3NDFW9XkQOwakwegNvARepatLoc11dndbX10dddWT+lCA9qytYv3X/Jo7s14Wnp5wYN8/h1z7Jjr3791t1hbDshjOzXjZjjGkrEZmvqnXZWl46MY0lQE+cgHYkqroY+HTA8A+AY9NYd054U4LE3vBevye+Tnxvw8c8v3R96x3HHXOWx1UYADv2KnfMWW53HMaYdi+d1lO/wGl2+7SIPBb7yVXB8iGo9VSQZ5ZuaP191uLGwGnChhtjTHuSzp3GvTjxiLeBlhTTloSg1lNBThvVr/X3SbUDWNaYmNBwUu2ArJbNGGOKUTp3Gh+p6m9UdY6q/iv2k7OS5UFQ66kB3eMzmozs1yUuGH75SSOorpC4aaorJOHR1KwFa7j03jeZtWBN5PLUNzRz2zPvWd/gxpiilc6dxnwR+QXwGE56EABUdUHWS5VH/pQgv37u/bh06ccdkti/xTFDe/PSiv0X9rqh8alHjr/p2dZg+nPLNnLLP9/l1R+dmrQcF931Wusyf/PCCsYNr+H+S49POo8xxuRbOncan8Z5x+Im4Fb351e5KFS+xVKCbP54d8r+NeobmuMqDIB5K5pb7w5mLVgT1/oKoHHr7qR3HKmWaYwxxSKdl/tOCvhpfUdDRC7JTRHzJ1n/GjFzl38UOE1s+ONvrw8cHzY8yjKNMaZYpHOnkcoVWVxWQUTpX2P8iD6B08SGn31U8MuAYcOjLNMYY4pFNisNST1JcYvSv0bdsBrGDY+Pc4wbvr9v70lHD04Ipg/o3olJRw8OXW+qZRpjTLGI/EZ4ygWJLFDVo7OysAC5eiM8SH1DM3OXf8T4EX1CL9wPvNLA7MWNTKwdwEWfHZYw/tpHF/HUOxs444h+3PDl0QFLSFyGf70rNmyLS29ijDHpyvYb4dmsNN5S1YS3v7MlX5WGP63ItHNrmTAmPnO7t6UTkNDSydt6Cpw7DX/rqdHX/ZP/7NzX+rlHVTmLrtufSPgns96OC8pffMIQrp94VNs30BjToWS70sjm46mkHTKVAm9akW279rJzT0tCp0zZaD31wCsNcRUGwH927uOBVxoAWLFhW8pWXMYYUwiRKw0RqRGR34rIAhGZLyK/FpHWZzeq+u3cFDF/onTKlI3WU7NDUo7EhkdpxWWMMYWQzp3GQzjJCs/FSWveBDyci0IVSpROmbLRempiSMqR2PAorbiMMaYQ0qk0eqvqz1W1wf25ASfrbbsRSyvSuaKMAzqV07kisVOmbLSeuuizw+hRVR43TY+q8taAerJWXDf+Ywkn/OI5bvzHkrZvcBs0b9/FojVbIvWnboxpP9JJIzJHRC4EZrifJwNPZL9IhaWxf1UI6YWW+y89PmkLq27VlTR64hrdqyvTLscHTR/HfW5o+phDrn6iNVPkn15exd0vr+KDm89Ke9ltFaWxgDGmfUp5pyEi20RkK3A58Fdgt/vzEDAlt8XLr1ggfNde5ZM9+9i1VxMC4TF1w2q48rSRCRXG80vX8/6G+At+rE+OmFSB8LBguz8fbwvk/Y4jSmMBY0z7lbLSUNVuqtrd/b9MVSvcnzJV7Z6PQuZLlEB4Kt6+N8KGpwqEp5M+5PEl4elJciEb+8gYU7rSanIrIrUiMkFEvhz7yVXBCiFKIDwVb98bYcNTBcLTSR9y9pHh6UlyIRv7yBhTutJpcvtn4M84rafOcX/OTjHPYBGZIyLLROQdEbnCHX6diKwTkYXuT1F0sB3Uv4Y/EJ7KKaP6M7Jfl7hh/j45UgXCw4Lt/oNVBvz4nCMjly0bsrGPjDGlK/Ib4SKyVFVHpbVwkQHAAFVdICLdgPnAJOB8YLuqRk6tns80Is3bd7X2r5HpxfAHDy/g2WUbOfXwA/nVBcHZVaY/vYzZi9czsbY/U754eMJ6T5r2PA2bdjKsdxVzrjoFcGIYjy9Zz9lH9s9ahZEqJUqQKKlWjDGFV7A0IiJyN3Crqi7NeGUis4HfAWMp4kqjrQ6/9kl27N2/X6srhGU3xN9M+dOEfG54DfWrNre2SNq5J7Eb2pU5aCmVKp1JEEtxYkzpKGQakXuBV0XkPRFZLCJvi8jiqDOLyFCcjpxedwd9213On0WkVxrlKGp3zFkeV2EA7Nir3DFneevnoDQhL61ojmuRFOTs21/MallTteIKYilOjOnY0qk0/gx8DTid/fGMc6LMKCJdgUeA76nqVuAPwKHAGKARpxfAoPkuE5F6EalvampKo6iFMyukZZR3eKbpQN5Z/3HqidKQqhVXEEtxYkzHlk6lsVpVH3PfBl8V+0k1k4hU4lQYf1HVRwFUdYOq7lPVFuBPwLFB86rqnapap6p1ffv2TaOohTMppGWUd3im6UCO6N8l9URpSNWKK4ilODGmY0un0nhXRP4qIl+J2uRWRAS4G1imqrd5hnuvSl8CCpsTI4suP2kE1RXx/VFVVwiXnzSi9XNQmhBv66iwg/L4907kvN/P49BrnuC8389rc1lTteIKEqWjKmNM+5VOGpFqYBdwmmeYAo8mmWcsziOtt0VkoTvsR8BXRGSMO/9KnLfN242gmEYC36CXPW98B0c0YOjV+7O2vLl6K0OvfqLNwfGgmEYq1088iouPH2odRBnTAaVTaZQBV6jqFgA3eB0Yi4hR1ZcI7gb2yTTWW1LC7gDO+/08/vatcUBwMDmsooiyvthy03Xto4tCh4f1NhgzvF83qyyM6YDSeTxVG6swAFR1M05rKOOxYM3WlMOzGTQOW18UT70TnPIkbLgxxqRTaZR5m8aKSG/Su1PpEI4eHJyOyzs8m0HjsPVFccYRwSlPwoYbY0w6lYMOlCQAAB2nSURBVMatwCsi8nMRuR54BZiWm2KVrrBHRd7hQcHkTPvdzfTRFBD6CCrVoyljTMcV+Vqlqvfh5J3agNNr35dV9f5cFazYrdiwjZn1awJfalt581kM610FwLDeVYHB6usnHsVzU8bzq8m1PDdlPB/cfBbnHHkgXTqVcc6RB7Ly5rOI9cJR6S5z5c1n0d3NatK9c/Ab4snKFWTlzWcxvI+TbHB4n+qsvXVunTQZ0z5FTiNSaMWURiRVGo3jb3qW9Z5OmAZ078SrPzo16TL980Tlvchnkt7D2yIraJmZsE6ajCkehUwjYkidRmPWgjUJF//GrbuZtWBN6DKD5onqsntej1SuIMlaT2XKOmkypn2zSiNNqdJoPP52cKdIYcNTjUtlrtvDXybpPXLReso6aTKmfbNKI02p0micfVRwp0hhw1ONS2W82+9GJuk9ctF6yjppMqZ9s0ojTanSaEw6ejADuneKGz+geycmHT04dJlB80R159ePi1SuIOm0nvIHtsMC3bFOmryskyZj2g97zyITKdoObPDFJ/yfgxx6YDcatza3pVQ88078Y65n31mflX4u/IHt8+sGMaN+bWig+7sPLYyb/7sPLbRAuDHthN1ppClVwPnGfyxJSAnS4g4PU9/QzEsrMqswjv7ZU0BmAfiv3vFyyuFBge37Xl0dGug+adrzgcsMG26MKS1WaaQpZSB8SUggPGQ4wNzlH2Vcnk07nCoqkwD8G6uCt8U7PCiw7ecNdDds2hk4TdhwY0xpsUojTSkD4UeGBMJDhgOMH9En4/L0rnYOYSYB+GMPDt4W7/CgwLafN9Ade6nRL2y4Maa0WKWRplQB5x+fc2TCTi1zh4epG1bDOLcVVLoW/PQMILMA/F8vH5tyeCywXVVZRrfOFVRVlnHxCUPiPnsD3XOuOiVwmWHDjTGlxd4Iz9CKDduS9ifx1Tte5o1VWzj24J6hF+fm7btYu3kHg3pVU9O1M9c+uoin3tnAGUf044Yvj+bwHz/Bjn1QXQ7LbnTe0j7k6idowamIPgh4c/vrd73KKw2b+Oyw3txz6QmRtmXsTc+ybutuBnbvxMs/OjVw255fup5nlm7gtFH9OGVU/5Sfj7/hGdZv30P/rpW8dq3TBUt9QzNzl3/E+BF9qBuWWEmmGl9q/Me3mKU6n03pyvYb4VZp5MDo6/4Z15lRj6pyFl13etw0/hZJ+/a2sCeDQ+FN+RGrUGLCKhavEdc8kXS9F58whA+aPo4L1PeoKo/bvuoKietoyv953PAaFOKWMW54Dfdfenzr54vuei3p+FJTSqlUMkk/Y0qHpREpcg+80hDYG94DrzS0fg5qkZRJhQH704hk0mpr+tPLUq73vldXJ7Ts8m9fqp4K561IbB02b0Uz9Q3OsKDWY97xpaaUUqlkkn7GdGxWaWTZ7MWNKYdHaZEUVSyNSCattmYvzjx9STbEWo2FtR5rS6uyQiqlVCqZpJ8xHZtVGlk2sXZAyuFRWiRFFUsjkkmrrYm1macvyYZYq7Gw1mNtaVVWSKWUSiWT9DOmY8tppSEig0VkjogsE5F3ROQKd3hvEXlWRJa7//dKtaxScdFnh9GjqjxuWI+qci767LDWz0EtkiqDelKPIJZGJJNWW1O+eHjK9V58wpCEll3+7auukKSfxw1PbB02bngNPQ/oxMz6NfQ8oFPg+KBgeL766Ui1nlkL1nDpvW8GvjwZdHyLNZVKJulnTMeW6zQie4Hvq+oCEekGzBeRZ4GvA8+r6s0icjVwNTA1x2XJm4ljBsY9J5746cQA6IQxAxk7vE9r65orHnor7rn+gO6daPS84X3xCUP425trEgLOXt18Aepuvot7kFQxjWffWc+hB8ZfQPr1qOKT3R+DCKhyy+TRdOlU3tp66hv3zY+bfl7A2+7zVjTzhelzWz9Hyb2Vr+ByqvV4+z55btlGbvnnuwn9pfiPbzFWGK1Koy2MKRI5vdNQ1UZVXeD+vg1YBgwEJgL3upPdC0zKZTnyKZ3AYk3Xzowe3JOGpu0JgeBGX0qQ+15dHRhwvmPOciBaAN7vOw+8mXJ7GrfuTijb+xs+Zk8L7Nmn7GmBqx5ZzJghvbhl8mh+MuvtlMsMW4+XPxCer+ByqvWkk64ldnyLucKwQLhJV95iGiIyFPg08DrQT1UbwalYgAND5rlMROpFpL6pqSlfRW2TTAKLbQn4znID7FEC8H4vvJ+dQLM3yLsuw86kgnj3S76Cy6nWk0m6lmJmgXCTrrxUGiLSFXgE+J6qbo06n6reqap1qlrXt2/f3BUwizIJLLYl4DvJDbBHCcD7nfyp7ASavUHegRmmeA/i3S/5Ci6nWk8m6VqKmQXCTbpyXmmISCVOhfEXVX3UHbxBRAa44wcAG3O1/nwFTmMyCSwGpRHxP+O/+IQhgQHny08aAYQH4A8b0J3bnnkv8J2H3170mZTbM6B7YpB6ZL8udCoXKsuFTuXCtHNrWbh6M1NnLuL6SZm9FObf3lggfMWGbcysX8Pmj3cz7dxaOleUcUCncjpXRA8up3MOxILYncqhc3kZncrj+wPJJF1LOp5fup6pMxfx/NL83LlYINykK6eBcBER4G5gmare5hn1GHAJcLP7/+xcrL9Qb+VeP/EoLj5+aFppGYb16RIXMD71iP4Jy/C/mV03tHfcMj7ZlRjTmHzHawD85oUVgW9Zf6pfF97f8HHrZ//b3qce0T+hn44PNn4cF0CfOnNRa7zl4fq1Kbc1yJZP9sR9XrFxW8Kbyp8bXgMoqBA1epvJOVC/chO79wHu65L1qzbFzXPqqP5x5Tr1iOzcZZw2/cXWY/Fw/VpG9uvC01NOzMqyk8nkfDUdV67vNMYCXwNOFpGF7s+ZOJXFqSKyHDjV/ZxVhX4rd3i/bkyuGxzpCxgWjARal5Hqrekob3f7g8vPL10fV2FA4tve9726OiHw61+PP0CfCf8yGrfuTtgnL61oZtde5ZM9+9i1V1Mez0zOgVSB4VwFjoOOxXsbPs7rHUfU89V0bLluPfWSqoqq1qrqGPfnSVVtVtVTVHWE+/+mbK+7vb2Vm+qt6ahvd3uX88zSDVGLWJRSHc9MzoFUxyJXgeOwY1Hqx8i0P+32jfD29lZuqremo77d7V3OaaP6RS1iUUp1PDM5B1Idi1wFjsOORakfI9P+tNtKo729lRsULPe+NR3l7W7/W9anjOrPyH5d4qbxB9MvPmFIQuDXvx5/gD4T/mUM6N4pYZ+MG16T1vHM5BxIdSxyFTgOOhYj+3XhlFGl2SrLtF/tPjV6KfUTEKU/iVTT+PvkeOCVBmYvbmRi7YC4VCZe/mmC9tkdc5Yza3Ejk2oHcPlJIxLK4e9PY9S1T/DJXjigApbe4KRnH3Pdk2zZqfSsEhZedyY/eHgBzy7byKmHH8ivLjg6sC8Q/3qmP72M2YvXM7G2P1O+eHik/ePv18K/fUH9XmSynmzw78d8KZW+P0qlnMXE+tNIQyn1aZCNsvpbG/Xv3ikuiB3UeipKXwrp9rcw9Oon0ip3kDJg+oVjkvY5Ul0hLLvhzNbPUfrk8G/LuOE1vLlqc9x+/z+z3k7aH0opnVdRlMr2lEo5i431pxFRoVtPpSMbZQ1q1eNv9eRvPRWlJVC6rYVi/Xu0VQtw5YxFSfsc8aZRidInR9C2zFvRHLeO789YlDQdSymdV1GUyvaUSjk7gnZbaZRS66lslDVq6x1v66koLYHSbS00NyA5YaZaWlLfBcfSqETpkyPKPtoXcucdS8dSSudVFKWyPaVSzo6g3VYapdR6Khtljdp6x9t6KkpLoHRbC40fnr3n+2VlqQPssTQqUfrkiLKPyiV4nbF0LKV0XkVRKttTKuXsCNptpVFKraeyUdagVj1hqTmSzeNvCZRua6FY/x5tVQZMP3900j5HvGlUkrUue+CVBs774yu89u+PUrbIuvX80YHpWM6oPYhF7p2Kk85EOKCynM4VktXzKpY2JezxX7bT4sTOvUzSs+RTKX2f27tc96dRUKXUp0E2yhqUDiJVK58PmuLfQm7wfQY45uDePPzmWmLJO+oO7p0wjZc/FUmPqvKEPkZS6de9U8I+OeaG5+Km8b9FPrluMK83bHZLKZxXN5jR1/2ztSxvrtxMj6pynpsyPmnrqfqVm+LKetSgnoy95YXWAOz5dYMAwdkhbW9uHJOqwUGuAsEa+zeN9CyFUErf5/asXbeeMsnVNzS35qbymnn58XHNVcfe8gI79+x/NFBVWcbLU08O/NI+8EoD1z62NCvlu/382tZEgOf9fh5vrk5MkPyZId3527fGBZazskzYExAXuWHCqNDmxys2bIvrHCqKZPsjqrD1PjdlfGvFls5xiCpXyzXFw1pPmayJEjxONwCZrP+OdHn7qFiwJjijfmx4UDlTBbWDZJIOJBsB2VQNDnIVCLYAs0mXVRodWJTgcboByGT9d6TL20fF0YO7B04TGx5UzlRB7SCZpAPJRkA2VYODXAWCLcBs0mWVRgeWKjUJpB+ADOvXwx+ATsXfR8XfvjUucLrY8KByhgW1wx5NQXDg3x8sv/iEIVkPyKZqcJCrQLAFmE26LKZhIqWuSDdthj/tCCSmKzn9tjm8u/ETDjvwAP555UmRUp7UXf9PPvpkH30OKKf+J6cnjI+yXn/gOyg1xY3/WMLjS9Zz9pH9+fE5R0aaJ11By5i1YA2Pv72es4/qz6SjByekPPGPj7rcTMpiCi8baZAsjYjJqigtctJNIxK0zN/NWZ7QX4RXdYWgIknLEZSeZOXNZ7X+7m0pBYnpP4LKdv4xg5gxf23cer2dSsXK5k1Xkg1B+2hG/Zq4t9oHdO9Eo+etfn+rtKBOmizVRvuR7vcujAXCTdZESc2QbhqRoGVe+fDCpBUGOM1nk5Xj2kcXBc4XG/7AKw1J03+Ele2+11bHfZ7y8MKEprzedCXZEFSO789YmJAGpdGXBsa/ff5OmizVRvuRq86+ssEqjQ4sSsuZdNOIBC0zk1cZ/OV46p3gzohiw8NaRHmHB5XNLyxzyawstgoLLkdm73t4O2myllDtR646+8oGqzQ6sCgtZ9JNIxK0TMngCai/HGccEdwZUWx4WIso7/CgsvmFZS6ZlMVWYcHlyOwxsbeTJmsJ1X7kqrOvbMhppSEifxaRjSKyxDPsOhFZ5+sz3BTA/hQS4Skx0k0jEtQa57YLxiR0MORXXSFJW/Dc8OXRgfPFhoe12rros8N4ful6ps5cxMLVm5l2bi2VZVBZLlSWkdASavoFYxI6hKquEJ5a8iGHXP0Ek377L6Bt6TyC9vut549JaMnmTwPj376R/bowZkiv1nLEllvuFr9ciNwSyp++ZNaCNVx675vMWrAmcHy25Gq5pS5XnX1lQ67TiNwD/A64zzd8uqr+KsfrNhE4f98mT4kRlJ4kmaB0DxPGDIxrpXXljIVxz+g7VZTxwg9OStqCx98/iP+iuntvS8Ln06a/2BpPebh+LdUVgvPys7PlDU0f8/LUk+PWe9OTS9nhWc+OvcrCddsBWLhuO0OvfsLJhdWGYHPQfn/t3/ExjQ1bd9OpHIQylBYmjhnIX19fjYpz93bcsJq49CbTzq3luw8tbJ1/n8J3H1qYsmz+gGt1hbTGdZ5btpFrHn07Ls6TaUA21Xqztdz2It3vXb7k9E5DVecCm3K5DpO5WOB0194WPtm9j117wwOnw/t1Y3Ld4Mgnbk3Xzowe3DPu4n/KqP7cMnk0jVt2BAatn1r8YcI8MbMWrEnoH6Rx6+7Wv4TvmLM8MIDtD8D7p5m3opmGpu2t6w1aT5C2BJuD9vt3H1qY0F9IC7B7H+za18LufXDfa6vZq7CvBfYqCUH8KzwVhtcPHl4QWpaggGvQfvTKRkC2mAO9xSTd710+FCqm8W0RWew+vuoVNpGIXCYi9SJS39TUlM/ydQiFCpxGCVr7eVOKBA1vS6DamzYlbD3JpLvPogTkMxEWFXl22cbQeTINrLY1IFvMgV6TXCEqjT8AhwJjgEbg1rAJVfVOVa1T1bq+ffvmq3wdRqECp1GC1n7elCJBw9sSqPamTQlbTzLp7rMoAflMhLW/OvXwA0PnyTSw2taAbDEHek1yea80VHWDqu5T1RbgT8Cx+S6DcRQqhUSyoHWYSUcPTohheFONXH7SiMAAtj8A75/GnzYlaD1B2rLPgvb7by4ck9BfSJlvPf6gvf/zry8cE7i+X11wdGhZggKuQfvRKxsB2WIO9Jrkcv5GuIgMBR5X1SPdzwNUtdH9fQpwnKpemGo59kZ47hQqhUSUtCF+qdJofOeBN3nh/Y84+VN9+O1FnwES06T4lxG0/f6ynTTteRo27WRY7yrmXHVKpPQOQSlNvILWO+m3/2Lxuu3UDuzKrO98nulPL2P24vVMrO3PlC8enpDOxT8e4Kt3vMwbq7Zw7ME9+evlYzPar/7Pl93zOnNXNDN+eE3WOtqC7KTJMMmVVBoREXkQOBHoA2wAfup+HoPzCHYlcHmsEknGKg2TyvE3PZvQuurVH50aN42/xc644TW8uWpzXAskhbhUHL2qK+Lezh7QvRObd+xN2nrq8GufTDsVySFXP0Gyh1ZlEDfe/7m6Qhhcc0Bc8D8o1YjfRXe9Fvc2+rjhNdx/6fGtn1OlbzHFraTSiKjqV1R1gKpWquogVb1bVb+mqkepaq2qTohSYRiTSqrWVRDcYmfeiua4Fkg/nLmYq2YuihvmT+fRuHV30tZTYS25kqUiufEfS5JWGEDCeP/noNZi/lQjfvUNzQnpS+ataKa+wRkW1vIqWYss077ZG+GmXUjVugqitcwpLxPKJb2vhb/1VFhLrmQtvB5fkn6rrai8qUb8UnXEFdbyKlmLLNO+WaVh2oVUrasgWsucfS3KPk2vZZO/9VRYS65kLbzOPjL9VltReVON+KXqiCus5VWyFlmmfbNKw7QLqVpXQbQOln45uZZfTh4dNyxouclaT4W15AoKhsf8+JwjU34Z/eP9n4Nai43s1yWhjxRv6o5UHXGFtbxK1iLLtG+5TiNiTN68+qNTU7auCkrNENSKyZ8Gxd8CK1WLs2OG9o6LFdQN7Z2y/B/cfFZc50/LNmyPW0Y3X38aY4fX8LlDaxJaaCXrVCsodceGbTvjptno+/zrC8dw5cML2adOPqvpFwQ37TUdg3XCZEyW1Tc0M/mO1xKGz7z8+Ei9HiZbRluWuWLDNr4wfW6kae+++JjWynHsLS+wc8/+R3ZVlWW8PPVk6+GvRJRU6yljOqJUweW2LKMty0wnRUcseG59dBg/qzSMybJUweW2LKMty0wnRUcseG59dBg/qzSMybJUweVMl+FPvZLuMsNSdyQLnhcq1YwpXhbTMCZHUgXlo0iVAiVIqiB9UPoWfzoS/zIKlWrGtF1JpRHJJqs0TCnJRgdDsxeui0tnUndwr7jWVEHL9M/jT3Hi7ZQKnLsKhbhh/bt3YkuKNCmmdFgg3Jgil40OhmIdNXnTlfjTffiXGTSPN8XJ80vXB6YZ8Q9bnyJNiunYrNIwJsuy0cFQ1I6avMtM1dIpWTqRZKy1lPGySsOYLMtGB0NRO2ryLjNVS6dk6USSsdZSxssqDWOyLBsdDAW1WvK3pvIvMzZP54oyDuhUTueK+JZOp4zqH9hSyj8sVZqUqOobmrntmfdaM+ZmOo0pLhYINyZHstHBkL/VUqplzl64jqtmLqJcytinLfxy8uiEIHZQmpF006SkkqqPjqjTmLaz1lPGmEDFkvIjShqVbKRaMdFY6yljTKBiSfkRJY1KNlKtmMKwSsOYdqJYUn5ESaOSjVQrpjCs0jCmnSiWlB9R0qhkI9WKKYycxjRE5M/A2cBGVT3SHdYbeBgYCqwEzlfVzamWZTENY6IplpQfUVKe1Dc0M3f5R4wf0afdVRjFchxKKhAuIuOB7cB9nkpjGrBJVW8WkauBXqo6NdWyrNIwpnRkI41KKUuVziWfSioQrqpzgU2+wROBe93f7wUm5bIMxpj8ykYalVKWKp1LqStETKOfqjYCuP+H9lAvIpeJSL2I1Dc1NeWtgMaYzGUjjUopK5ZWbLlS1IFwVb1TVetUta5v376FLo4xJoJspFEpZcXSii1XClFpbBCRAQDu/xsLUAZjTI5kI41KKSuWVmy5UlGAdT4GXALc7P4/uwBlMMbk0PUTj+Li44e2OY1KqZowZiBjh/cpitZT2ZbTSkNEHgROBPqIyFrgpziVxQwR+QawGjgvl2UwxhTG8H7dOlxl4VXTtXO7qixiclppqOpXQkadksv1GmOMyY2iDoQbY4wpLlZpGGOMicwqDWOMMZFZpWGMMSaykumESUSagFVAH6BUku5bWXPDypobpVLWUiknFEdZD1bVrL0dXTKVRoyI1Gcz+VYuWVlzw8qaG6VS1lIpJ5RWWaOyx1PGGGMis0rDGGNMZKVYadxZ6AKkwcqaG1bW3CiVspZKOaG0yhpJycU0jDHGFE4p3mkYY4wpEKs0jDHGRFYylYaI/FlENorIkkKXJRURGSwic0RkmYi8IyJXFLpMYUSkSkTeEJFFbll/VugyJSMi5SLylog8XuiyJCMiK0XkbRFZKCJF3bm9iPQUkZki8q57zp5Q6DIFEZGR7v6M/WwVke8VulxhRGSK+51aIiIPikhVocuUDSUT0xCR8cB24D5VPbLQ5UnG7VxqgKouEJFuwHxgkqouLXDREoiIAF1UdbuIVAIvAVeo6msFLlogEbkSqAO6q+rZhS5PGBFZCdSpaqFf7EpJRO4F5qnqXSLSCThAVYu6b1YRKQfWAcep6qpCl8dPRAbifJdGqeoOEZkBPKmq9xS2ZG1XMncaqjoX2FTockShqo2qusD9fRuwDBhY2FIFU8d292Ol+1OUf0mIyCDgLOCuQpelvRCR7sB44G4AVd1d7BWG6xTg38VYYXhUANUiUgEcAHxY4PJkRclUGqVKRIYCnwZeL2xJwrmPfBbidL37rKoWa1lvB64CWlJNWAQUeEZE5ovIZYUuTBKHAE3A/7qP/e4SkS6FLlQEFwIPFroQYVR1HfArnI7mGoH/qOozhS1VdlilkUMi0hV4BPieqm4tdHnCqOo+VR0DDAKOFZGie/wnImcDG1V1fqHLEtFYVT0aOAP4/9zHq8WoAjga+IOqfhr4GLi6sEVKzn2ENgH4W6HLEkZEegETgWHAQUAXEbmosKXKDqs0csSNDzwC/EVVHy10eaJwH0u8CJxe4KIEGQtMcGMFDwEni8gDhS1SOFX90P1/I/B34NjClijUWmCt5+5yJk4lUszOABao6oZCFySJLwANqtqkqnuAR4HPFrhMWWGVRg64weW7gWWqeluhy5OMiPQVkZ7u79U4J/u7hS1VIlW9RlUHqepQnEcTL6hqUf7lJiJd3AYQuI96TgOKstWfqq4H1ojISHfQKUDRNdjw+QpF/GjKtRo4XkQOcK8Hp+DENkteyVQaIvIg8CowUkTWisg3Cl2mJMYCX8P5azjWPPDMQhcqxABgjogsBt7EiWkUdXPWEtAPeElEFgFvAE+o6j8LXKZkvgP8xT0HxgA3Fbg8oUTkAOBUnL/ci5Z75zYTWAC8jXOtbRcpRUqmya0xxpjCK5k7DWOMMYVnlYYxxpjIrNIwxhgTmVUaxhhjIrNKwxhjTGQVhS6AMaVARK7DSZjZHZirqs+lOf+JwA+KOcmiMVFYpWFMGlT1J4UugzGFZI+njAkhIj8WkfdE5DlgpDvsHhGZ7P5+s4gsFZHFIvIrz/g/isg8EXnfzZllTLthdxrGBBCRY3DSlXwa53uyAKdflNj43sCXgMNUVWOpWFxDgc8Dh+K8bT88X+U2JtfsTsOYYOOAv6vqJ26G4sd847cCO4G7ROTLwCeecTNUtUVVlwMfAIflpcTG5IFVGsaEC82xo6p7cTLXPgJMAry5pfzzWa4e025YpWFMsLnAl0Sk2s1Ye453pNtXSg9VfRL4Hk6iv5jzRKRMRA7F6eTovXwV2phcs5iGMQHc/t0fBhYCq4B5vkm6AbNFpAoQYIpn3HvAv3Cy3X5TVXc62bGNKX2W5daYLBKRe4DHVXVmoctiTC7Y4yljjDGR2Z2GMcaYyOxOwxhjTGRWaRhjjInMKg1jjDGRWaVhjDEmMqs0jDHGRPZ/AQFoO6g/7rceAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_2008.plot(x=\"displ\", y=\"cmb_mpg\", kind=\"scatter\");\n",
    "plt.title(\"Correlation between displacement and combined mpg 2008\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "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": [
    "df_2018.plot(x=\"displ\", y=\"cmb_mpg\", kind=\"scatter\");\n",
    "plt.title(\"Correlation between displacement and combined mpg 2018\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As per two scatter plots above, there is negative correlation between displacement and combined mpg."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.4. correlation between greenhouse gas score and combined mpg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "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": [
    "df_2008.plot(x=\"greenhouse_gas_score\", y=\"cmb_mpg\", kind=\"scatter\");\n",
    "plt.title(\"Correlation between green house gas score and combined mpg 2008\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "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": [
    "df_2018.plot(x=\"greenhouse_gas_score\", y=\"cmb_mpg\", kind=\"scatter\");\n",
    "plt.title(\"Correlation between green house gas score and combined mpg 2018\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As per two scatter plots above, there is positive correlation between displacement and combined mpg."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 7. Conclusions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7.1) Q: Are more unique models using alternative fuels in 2018 compared to 2008? By how much?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "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>model</th>\n",
       "      <th>displ</th>\n",
       "      <th>cyl</th>\n",
       "      <th>trans</th>\n",
       "      <th>drive</th>\n",
       "      <th>fuel</th>\n",
       "      <th>veh_class</th>\n",
       "      <th>air_pollution_score</th>\n",
       "      <th>city_mpg</th>\n",
       "      <th>hwy_mpg</th>\n",
       "      <th>cmb_mpg</th>\n",
       "      <th>greenhouse_gas_score</th>\n",
       "      <th>smartway</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ACURA MDX</td>\n",
       "      <td>3.7</td>\n",
       "      <td>6</td>\n",
       "      <td>Auto-S5</td>\n",
       "      <td>4WD</td>\n",
       "      <td>Gasoline</td>\n",
       "      <td>SUV</td>\n",
       "      <td>7.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>4</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ACURA RDX</td>\n",
       "      <td>2.3</td>\n",
       "      <td>4</td>\n",
       "      <td>Auto-S5</td>\n",
       "      <td>4WD</td>\n",
       "      <td>Gasoline</td>\n",
       "      <td>SUV</td>\n",
       "      <td>7.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>5</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       model  displ  cyl    trans drive      fuel veh_class  \\\n",
       "0  ACURA MDX    3.7    6  Auto-S5   4WD  Gasoline       SUV   \n",
       "1  ACURA RDX    2.3    4  Auto-S5   4WD  Gasoline       SUV   \n",
       "\n",
       "   air_pollution_score  city_mpg  hwy_mpg  cmb_mpg  greenhouse_gas_score  \\\n",
       "0                  7.0      15.0     20.0     17.0                     4   \n",
       "1                  7.0      17.0     22.0     19.0                     5   \n",
       "\n",
       "  smartway  \n",
       "0       no  \n",
       "1       no  "
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2008.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "check the fuel usage across the years."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gasoline    984\n",
       "ethanol       1\n",
       "CNG           1\n",
       "gas           1\n",
       "Name: fuel, dtype: int64"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2008[\"fuel\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gasoline       752\n",
       "Ethanol         26\n",
       "Gas             26\n",
       "Diesel          19\n",
       "Electricity     12\n",
       "Name: fuel, dtype: int64"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2018[\"fuel\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Alternative Fuel classification: reference from [wikipeida](https://en.wikipedia.org/wiki/Alternative_fuel#:~:text=Alternative%20fuels%2C%20known%20as%20non,as%20artificial%20radioisotope%20fuels%20that)\n",
    "\n",
    "- 2008 alternative fuels: Ethanol, CNG\n",
    "- 2018 alternative fuels: Ethanol, Electricity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "check the models per each fuel type across the years."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "fuel\n",
       "CNG           1\n",
       "Gasoline    984\n",
       "ethanol       1\n",
       "gas           1\n",
       "Name: model, dtype: int64"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2008.groupby(\"fuel\").count().model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# how many unique models used alternative sources of fuel in 2008\n",
    "alternative_2008 = df_2008.query('fuel in [\"CNG\", \"ethanol\"]').model.nunique()\n",
    "alternative_2008"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "fuel\n",
       "Diesel          19\n",
       "Electricity     12\n",
       "Ethanol         26\n",
       "Gas             26\n",
       "Gasoline       752\n",
       "Name: model, dtype: int64"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2018.groupby(\"fuel\").count().model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "26"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# how many unique models used alternative sources of fuel in 2018\n",
    "alternative_2018 = df_2018.query('fuel in [\"Ethanol\", \"Electricity\"]').model.nunique()\n",
    "alternative_2018"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "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": [
    "# plot the numbers\n",
    "plt.bar([\"2008\", \"2018\"], [alternative_2008, alternative_2018]);\n",
    "plt.xlabel(\"Year\")\n",
    "plt.ylabel(\"Number of Models\")\n",
    "plt.title(\"Number of Unique Models using Alternative Fuel\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "24 more unique models have been using Alternative Fuel in 2018 since 2008. We can find the additional information such as proportion."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total unique model in 2008:  377\n",
      "Total unique model in 2018:  357\n"
     ]
    }
   ],
   "source": [
    "# get the total unique models for each year\n",
    "total_2008 = df_2008.model.nunique()\n",
    "total_2018 = df_2018.model.nunique()\n",
    "\n",
    "print(\"Total unique model in 2008: \", total_2008)\n",
    "print(\"Total unique model in 2018: \", total_2018)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Proportion of Altrnative Fuel used Models in 2008:  0.005305039787798408\n",
      "Proportion of Altrnative Fuel used Models in 2018:  0.07282913165266107\n"
     ]
    }
   ],
   "source": [
    "# calculate the proportion of alternative fuels model , in each year\n",
    "proportion_2008 = alternative_2008 / total_2008\n",
    "proportion_2018 = alternative_2018 / total_2018\n",
    "\n",
    "print(\"Proportion of Altrnative Fuel used Models in 2008: \", proportion_2008)\n",
    "print(\"Proportion of Altrnative Fuel used Models in 2018: \", proportion_2018)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "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": [
    "# plot the proportion\n",
    "plt.bar([\"2008\", \"2018\"], [proportion_2008, proportion_2018]);\n",
    "plt.title(\"Proportion of Unique Models Using Alternative Fuels\")\n",
    "plt.xlabel(\"Year\")\n",
    "plt.ylabel(\"Proportion of Unique Models\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7.2) Q: How much have vehicle classes improved in fuel economy (increased in mpg)?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "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>model</th>\n",
       "      <th>displ</th>\n",
       "      <th>cyl</th>\n",
       "      <th>trans</th>\n",
       "      <th>drive</th>\n",
       "      <th>fuel</th>\n",
       "      <th>veh_class</th>\n",
       "      <th>air_pollution_score</th>\n",
       "      <th>city_mpg</th>\n",
       "      <th>hwy_mpg</th>\n",
       "      <th>cmb_mpg</th>\n",
       "      <th>greenhouse_gas_score</th>\n",
       "      <th>smartway</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ACURA MDX</td>\n",
       "      <td>3.7</td>\n",
       "      <td>6</td>\n",
       "      <td>Auto-S5</td>\n",
       "      <td>4WD</td>\n",
       "      <td>Gasoline</td>\n",
       "      <td>SUV</td>\n",
       "      <td>7.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>4</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ACURA RDX</td>\n",
       "      <td>2.3</td>\n",
       "      <td>4</td>\n",
       "      <td>Auto-S5</td>\n",
       "      <td>4WD</td>\n",
       "      <td>Gasoline</td>\n",
       "      <td>SUV</td>\n",
       "      <td>7.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>5</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       model  displ  cyl    trans drive      fuel veh_class  \\\n",
       "0  ACURA MDX    3.7    6  Auto-S5   4WD  Gasoline       SUV   \n",
       "1  ACURA RDX    2.3    4  Auto-S5   4WD  Gasoline       SUV   \n",
       "\n",
       "   air_pollution_score  city_mpg  hwy_mpg  cmb_mpg  greenhouse_gas_score  \\\n",
       "0                  7.0      15.0     20.0     17.0                     4   \n",
       "1                  7.0      17.0     22.0     19.0                     5   \n",
       "\n",
       "  smartway  \n",
       "0       no  \n",
       "1       no  "
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2008.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "we can find the mean for each mpg per each vehicle classes, and compare it for both year."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [],
   "source": [
    "vehicle_2008 = df_2008.groupby(\"veh_class\").cmb_mpg.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [],
   "source": [
    "vehicle_2018 = df_2018.groupby(\"veh_class\").cmb_mpg.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "veh_class\n",
       "SUV                     NaN\n",
       "large car          7.572727\n",
       "midsize car        9.001251\n",
       "minivan            3.917647\n",
       "pickup             4.011430\n",
       "small SUV               NaN\n",
       "small car          7.048680\n",
       "special purpose         NaN\n",
       "standard SUV            NaN\n",
       "station wagon      7.995238\n",
       "van                     NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check the improvement\n",
    "improvement = vehicle_2018 - vehicle_2008\n",
    "improvement"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [],
   "source": [
    "# only interested in vehicle classes existed in both year\n",
    "improvement.dropna(inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "veh_class\n",
       "large car        7.572727\n",
       "midsize car      9.001251\n",
       "minivan          3.917647\n",
       "pickup           4.011430\n",
       "small car        7.048680\n",
       "station wagon    7.995238\n",
       "dtype: float64"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "improvement"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the improvement\n",
    "fig, ax = plt.subplots(figsize = (10, 6))\n",
    "ax.bar(improvement.index, improvement);\n",
    "plt.title(\"Improvements in Fuel Economy from 2008 to 2018 by Vehicle Class\")\n",
    "plt.xlabel(\"Vehicle Class\")\n",
    "plt.ylabel(\"Increase in Average Combined MPG\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7.3) Q: What are the characteristics of SmartWay vehicles? Have they changed over time? (mpg, greenhouse gas)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "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>model</th>\n",
       "      <th>displ</th>\n",
       "      <th>cyl</th>\n",
       "      <th>trans</th>\n",
       "      <th>drive</th>\n",
       "      <th>fuel</th>\n",
       "      <th>veh_class</th>\n",
       "      <th>air_pollution_score</th>\n",
       "      <th>city_mpg</th>\n",
       "      <th>hwy_mpg</th>\n",
       "      <th>cmb_mpg</th>\n",
       "      <th>greenhouse_gas_score</th>\n",
       "      <th>smartway</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ACURA MDX</td>\n",
       "      <td>3.7</td>\n",
       "      <td>6</td>\n",
       "      <td>Auto-S5</td>\n",
       "      <td>4WD</td>\n",
       "      <td>Gasoline</td>\n",
       "      <td>SUV</td>\n",
       "      <td>7.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>4</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ACURA RDX</td>\n",
       "      <td>2.3</td>\n",
       "      <td>4</td>\n",
       "      <td>Auto-S5</td>\n",
       "      <td>4WD</td>\n",
       "      <td>Gasoline</td>\n",
       "      <td>SUV</td>\n",
       "      <td>7.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>5</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       model  displ  cyl    trans drive      fuel veh_class  \\\n",
       "0  ACURA MDX    3.7    6  Auto-S5   4WD  Gasoline       SUV   \n",
       "1  ACURA RDX    2.3    4  Auto-S5   4WD  Gasoline       SUV   \n",
       "\n",
       "   air_pollution_score  city_mpg  hwy_mpg  cmb_mpg  greenhouse_gas_score  \\\n",
       "0                  7.0      15.0     20.0     17.0                     4   \n",
       "1                  7.0      17.0     22.0     19.0                     5   \n",
       "\n",
       "  smartway  \n",
       "0       no  \n",
       "1       no  "
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2008.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "we can filter out the smartway vehicles and analyze them for each characteristics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "no     607\n",
       "yes    380\n",
       "Name: smartway, dtype: int64"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2008.smartway.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Number of Smarway Vehicles in 2008:  380\n"
     ]
    },
    {
     "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>model</th>\n",
       "      <th>displ</th>\n",
       "      <th>cyl</th>\n",
       "      <th>trans</th>\n",
       "      <th>drive</th>\n",
       "      <th>fuel</th>\n",
       "      <th>veh_class</th>\n",
       "      <th>air_pollution_score</th>\n",
       "      <th>city_mpg</th>\n",
       "      <th>hwy_mpg</th>\n",
       "      <th>cmb_mpg</th>\n",
       "      <th>greenhouse_gas_score</th>\n",
       "      <th>smartway</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ACURA TL</td>\n",
       "      <td>3.2</td>\n",
       "      <td>6</td>\n",
       "      <td>Auto-S5</td>\n",
       "      <td>2WD</td>\n",
       "      <td>Gasoline</td>\n",
       "      <td>midsize car</td>\n",
       "      <td>7.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>6</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ACURA TL</td>\n",
       "      <td>3.5</td>\n",
       "      <td>6</td>\n",
       "      <td>Auto-S5</td>\n",
       "      <td>2WD</td>\n",
       "      <td>Gasoline</td>\n",
       "      <td>midsize car</td>\n",
       "      <td>7.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>6</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      model  displ  cyl    trans drive      fuel    veh_class  \\\n",
       "3  ACURA TL    3.2    6  Auto-S5   2WD  Gasoline  midsize car   \n",
       "4  ACURA TL    3.5    6  Auto-S5   2WD  Gasoline  midsize car   \n",
       "\n",
       "   air_pollution_score  city_mpg  hwy_mpg  cmb_mpg  greenhouse_gas_score  \\\n",
       "3                  7.0      18.0     26.0     21.0                     6   \n",
       "4                  7.0      17.0     26.0     20.0                     6   \n",
       "\n",
       "  smartway  \n",
       "3      yes  \n",
       "4      yes  "
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "smart_2008 = df_2008[df_2008[\"smartway\"] == 'yes']\n",
    "print(\"Total Number of Smarway Vehicles in 2008: \", smart_2008.shape[0])\n",
    "\n",
    "smart_2008.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "No       726\n",
       "Yes       92\n",
       "Elite     17\n",
       "Name: smartway, dtype: int64"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2018.smartway.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Number of Smarway Vehicles in 2018:  109\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        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>model</th>\n",
       "      <th>displ</th>\n",
       "      <th>cyl</th>\n",
       "      <th>trans</th>\n",
       "      <th>drive</th>\n",
       "      <th>fuel</th>\n",
       "      <th>veh_class</th>\n",
       "      <th>air_pollution_score</th>\n",
       "      <th>city_mpg</th>\n",
       "      <th>hwy_mpg</th>\n",
       "      <th>cmb_mpg</th>\n",
       "      <th>greenhouse_gas_score</th>\n",
       "      <th>smartway</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>AUDI A4 Ultra</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>AMS-7</td>\n",
       "      <td>2WD</td>\n",
       "      <td>Gasoline</td>\n",
       "      <td>small car</td>\n",
       "      <td>3.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>7</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>BUICK Encore</td>\n",
       "      <td>1.4</td>\n",
       "      <td>4</td>\n",
       "      <td>SemiAuto-6</td>\n",
       "      <td>2WD</td>\n",
       "      <td>Gasoline</td>\n",
       "      <td>small SUV</td>\n",
       "      <td>3.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>7</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             model  displ  cyl       trans drive      fuel  veh_class  \\\n",
       "16   AUDI A4 Ultra    2.0    4       AMS-7   2WD  Gasoline  small car   \n",
       "121   BUICK Encore    1.4    4  SemiAuto-6   2WD  Gasoline  small SUV   \n",
       "\n",
       "     air_pollution_score  city_mpg  hwy_mpg  cmb_mpg  greenhouse_gas_score  \\\n",
       "16                   3.0      27.0     37.0     31.0                     7   \n",
       "121                  3.0      27.0     33.0     30.0                     7   \n",
       "\n",
       "    smartway  \n",
       "16       Yes  \n",
       "121      Yes  "
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "smart_2018 = df_2018.query('smartway in [\"Yes\", \"Elite\"]')\n",
    "print(\"Total Number of Smarway Vehicles in 2018: \", smart_2018.shape[0])\n",
    "\n",
    "smart_2018.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "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>displ</th>\n",
       "      <th>cyl</th>\n",
       "      <th>air_pollution_score</th>\n",
       "      <th>city_mpg</th>\n",
       "      <th>hwy_mpg</th>\n",
       "      <th>cmb_mpg</th>\n",
       "      <th>greenhouse_gas_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>380.000000</td>\n",
       "      <td>380.000000</td>\n",
       "      <td>380.000000</td>\n",
       "      <td>380.000000</td>\n",
       "      <td>380.000000</td>\n",
       "      <td>380.000000</td>\n",
       "      <td>380.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.602895</td>\n",
       "      <td>4.826316</td>\n",
       "      <td>7.365789</td>\n",
       "      <td>20.984211</td>\n",
       "      <td>28.413158</td>\n",
       "      <td>23.736842</td>\n",
       "      <td>6.868421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.623436</td>\n",
       "      <td>1.002025</td>\n",
       "      <td>1.148195</td>\n",
       "      <td>3.442672</td>\n",
       "      <td>3.075194</td>\n",
       "      <td>3.060379</td>\n",
       "      <td>0.827338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.300000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.275000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.400000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>9.500000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            displ         cyl  air_pollution_score    city_mpg     hwy_mpg  \\\n",
       "count  380.000000  380.000000           380.000000  380.000000  380.000000   \n",
       "mean     2.602895    4.826316             7.365789   20.984211   28.413158   \n",
       "std      0.623436    1.002025             1.148195    3.442672    3.075194   \n",
       "min      1.300000    4.000000             6.000000   17.000000   22.000000   \n",
       "25%      2.275000    4.000000             7.000000   19.000000   26.000000   \n",
       "50%      2.400000    4.000000             7.000000   20.000000   28.000000   \n",
       "75%      3.000000    6.000000             7.000000   22.000000   30.000000   \n",
       "max      5.000000    8.000000             9.500000   48.000000   45.000000   \n",
       "\n",
       "          cmb_mpg  greenhouse_gas_score  \n",
       "count  380.000000            380.000000  \n",
       "mean    23.736842              6.868421  \n",
       "std      3.060379              0.827338  \n",
       "min     20.000000              6.000000  \n",
       "25%     22.000000              6.000000  \n",
       "50%     23.000000              7.000000  \n",
       "75%     25.000000              7.000000  \n",
       "max     46.000000             10.000000  "
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check the characterctics for 2008\n",
    "smart_2008.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\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>displ</th>\n",
       "      <th>cyl</th>\n",
       "      <th>air_pollution_score</th>\n",
       "      <th>city_mpg</th>\n",
       "      <th>hwy_mpg</th>\n",
       "      <th>cmb_mpg</th>\n",
       "      <th>greenhouse_gas_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>109.000000</td>\n",
       "      <td>109.000000</td>\n",
       "      <td>109.000000</td>\n",
       "      <td>109.000000</td>\n",
       "      <td>109.000000</td>\n",
       "      <td>109.000000</td>\n",
       "      <td>109.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.785321</td>\n",
       "      <td>3.935780</td>\n",
       "      <td>5.192661</td>\n",
       "      <td>34.853211</td>\n",
       "      <td>41.422018</td>\n",
       "      <td>37.302752</td>\n",
       "      <td>7.917431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.407073</td>\n",
       "      <td>0.414444</td>\n",
       "      <td>1.802658</td>\n",
       "      <td>16.365516</td>\n",
       "      <td>13.045003</td>\n",
       "      <td>14.792080</td>\n",
       "      <td>1.195117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.200000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.500000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.600000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>9.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.500000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>113.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>106.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            displ         cyl  air_pollution_score    city_mpg     hwy_mpg  \\\n",
       "count  109.000000  109.000000           109.000000  109.000000  109.000000   \n",
       "mean     1.785321    3.935780             5.192661   34.853211   41.422018   \n",
       "std      0.407073    0.414444             1.802658   16.365516   13.045003   \n",
       "min      1.200000    3.000000             3.000000   25.000000   27.000000   \n",
       "25%      1.500000    4.000000             3.000000   28.000000   36.000000   \n",
       "50%      1.600000    4.000000             5.000000   29.000000   37.000000   \n",
       "75%      2.000000    4.000000             7.000000   31.000000   40.000000   \n",
       "max      3.500000    6.000000             7.000000  113.000000   99.000000   \n",
       "\n",
       "          cmb_mpg  greenhouse_gas_score  \n",
       "count  109.000000            109.000000  \n",
       "mean    37.302752              7.917431  \n",
       "std     14.792080              1.195117  \n",
       "min     26.000000              7.000000  \n",
       "25%     31.000000              7.000000  \n",
       "50%     32.000000              7.000000  \n",
       "75%     35.000000              9.000000  \n",
       "max    106.000000             10.000000  "
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check the characterctics for 2018\n",
    "smart_2018.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Summary Findings\n",
    "\n",
    "After comparing the mean value for each characteristics between 2008 and 2018, we can briefly say that the following\n",
    "- Engine displacement, Number of Engine cylinders are getting smaller.\n",
    "- Air pollution score (smog rating) is also getting decreased.\n",
    "- city fuel economy, – highway fuel economy , combined city/highway fuel economy are getting more efficient.\n",
    "- greenhouse gas score is getting improved."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7.4) Q: What features are associated with better fuel economy (mpg)?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "we will explore the top features asscoiated with better mpg, which are better than average mpg in each year."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "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>displ</th>\n",
       "      <th>cyl</th>\n",
       "      <th>air_pollution_score</th>\n",
       "      <th>city_mpg</th>\n",
       "      <th>hwy_mpg</th>\n",
       "      <th>cmb_mpg</th>\n",
       "      <th>greenhouse_gas_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>519.000000</td>\n",
       "      <td>519.000000</td>\n",
       "      <td>519.000000</td>\n",
       "      <td>519.000000</td>\n",
       "      <td>519.000000</td>\n",
       "      <td>519.000000</td>\n",
       "      <td>519.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.667823</td>\n",
       "      <td>4.890173</td>\n",
       "      <td>6.998073</td>\n",
       "      <td>20.317919</td>\n",
       "      <td>27.603083</td>\n",
       "      <td>22.992293</td>\n",
       "      <td>6.639692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.665551</td>\n",
       "      <td>1.034856</td>\n",
       "      <td>1.159565</td>\n",
       "      <td>3.198257</td>\n",
       "      <td>3.051120</td>\n",
       "      <td>2.926371</td>\n",
       "      <td>0.804935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.300000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.300000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.500000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>9.500000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            displ         cyl  air_pollution_score    city_mpg     hwy_mpg  \\\n",
       "count  519.000000  519.000000           519.000000  519.000000  519.000000   \n",
       "mean     2.667823    4.890173             6.998073   20.317919   27.603083   \n",
       "std      0.665551    1.034856             1.159565    3.198257    3.051120   \n",
       "min      1.300000    4.000000             4.000000   17.000000   20.000000   \n",
       "25%      2.300000    4.000000             6.000000   18.000000   25.000000   \n",
       "50%      2.500000    4.000000             7.000000   20.000000   27.000000   \n",
       "75%      3.000000    6.000000             7.000000   21.000000   29.000000   \n",
       "max      6.000000    8.000000             9.500000   48.000000   45.000000   \n",
       "\n",
       "          cmb_mpg  greenhouse_gas_score  \n",
       "count  519.000000            519.000000  \n",
       "mean    22.992293              6.639692  \n",
       "std      2.926371              0.804935  \n",
       "min     20.000000              6.000000  \n",
       "25%     21.000000              6.000000  \n",
       "50%     22.000000              6.000000  \n",
       "75%     24.000000              7.000000  \n",
       "max     46.000000             10.000000  "
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_2008 = df_2008.query('cmb_mpg > cmb_mpg.mean()')\n",
    "top_2008.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\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>displ</th>\n",
       "      <th>cyl</th>\n",
       "      <th>air_pollution_score</th>\n",
       "      <th>city_mpg</th>\n",
       "      <th>hwy_mpg</th>\n",
       "      <th>cmb_mpg</th>\n",
       "      <th>greenhouse_gas_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>329.000000</td>\n",
       "      <td>329.000000</td>\n",
       "      <td>329.000000</td>\n",
       "      <td>329.000000</td>\n",
       "      <td>329.000000</td>\n",
       "      <td>329.000000</td>\n",
       "      <td>329.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.962918</td>\n",
       "      <td>4.021277</td>\n",
       "      <td>4.851064</td>\n",
       "      <td>27.477204</td>\n",
       "      <td>35.306991</td>\n",
       "      <td>30.413374</td>\n",
       "      <td>6.331307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.398808</td>\n",
       "      <td>0.464768</td>\n",
       "      <td>1.860781</td>\n",
       "      <td>11.017182</td>\n",
       "      <td>9.011171</td>\n",
       "      <td>10.066211</td>\n",
       "      <td>1.408692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.200000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.600000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.500000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>113.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>106.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            displ         cyl  air_pollution_score    city_mpg     hwy_mpg  \\\n",
       "count  329.000000  329.000000           329.000000  329.000000  329.000000   \n",
       "mean     1.962918    4.021277             4.851064   27.477204   35.306991   \n",
       "std      0.398808    0.464768             1.860781   11.017182    9.011171   \n",
       "min      1.200000    3.000000             1.000000   21.000000   27.000000   \n",
       "25%      1.600000    4.000000             3.000000   23.000000   31.000000   \n",
       "50%      2.000000    4.000000             5.000000   25.000000   33.000000   \n",
       "75%      2.000000    4.000000             7.000000   28.000000   36.000000   \n",
       "max      3.500000    6.000000             7.000000  113.000000   99.000000   \n",
       "\n",
       "          cmb_mpg  greenhouse_gas_score  \n",
       "count  329.000000            329.000000  \n",
       "mean    30.413374              6.331307  \n",
       "std     10.066211              1.408692  \n",
       "min     25.000000              4.000000  \n",
       "25%     26.000000              5.000000  \n",
       "50%     28.000000              6.000000  \n",
       "75%     31.000000              7.000000  \n",
       "max    106.000000             10.000000  "
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_2018 = df_2018.query('cmb_mpg > cmb_mpg.mean()')\n",
    "top_2018.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7.5) Q: For all of the models that were produced in 2008 that are still being produced in 2018, how much has the mpg improved and which vehicle improved the most?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "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>model</th>\n",
       "      <th>displ</th>\n",
       "      <th>cyl</th>\n",
       "      <th>trans</th>\n",
       "      <th>drive</th>\n",
       "      <th>fuel</th>\n",
       "      <th>veh_class</th>\n",
       "      <th>air_pollution_score</th>\n",
       "      <th>city_mpg</th>\n",
       "      <th>hwy_mpg</th>\n",
       "      <th>cmb_mpg</th>\n",
       "      <th>greenhouse_gas_score</th>\n",
       "      <th>smartway</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ACURA MDX</td>\n",
       "      <td>3.7</td>\n",
       "      <td>6</td>\n",
       "      <td>Auto-S5</td>\n",
       "      <td>4WD</td>\n",
       "      <td>Gasoline</td>\n",
       "      <td>SUV</td>\n",
       "      <td>7.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>4</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ACURA RDX</td>\n",
       "      <td>2.3</td>\n",
       "      <td>4</td>\n",
       "      <td>Auto-S5</td>\n",
       "      <td>4WD</td>\n",
       "      <td>Gasoline</td>\n",
       "      <td>SUV</td>\n",
       "      <td>7.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>5</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       model  displ  cyl    trans drive      fuel veh_class  \\\n",
       "0  ACURA MDX    3.7    6  Auto-S5   4WD  Gasoline       SUV   \n",
       "1  ACURA RDX    2.3    4  Auto-S5   4WD  Gasoline       SUV   \n",
       "\n",
       "   air_pollution_score  city_mpg  hwy_mpg  cmb_mpg  greenhouse_gas_score  \\\n",
       "0                  7.0      15.0     20.0     17.0                     4   \n",
       "1                  7.0      17.0     22.0     19.0                     5   \n",
       "\n",
       "  smartway  \n",
       "0       no  \n",
       "1       no  "
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2008.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NISSAN Altima                12\n",
       "HONDA Accord                 11\n",
       "FORD Ranger                  10\n",
       "DODGE RAM 1500                9\n",
       "SUBARU Legacy                 8\n",
       "                             ..\n",
       "BENTLEY Azure                 1\n",
       "MERCEDES-BENZ E550 4Matic     1\n",
       "MERCEDES-BENZ Maybach 62S     1\n",
       "LEXUS ES 350                  1\n",
       "ACURA RDX                     1\n",
       "Name: model, Length: 377, dtype: int64"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2008.model.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "FORD F150                        22\n",
       "CHEVROLET Silverado 15           16\n",
       "GMC Sierra 15                    16\n",
       "GMC Yukon 1500                    8\n",
       "JEEP Cherokee                     8\n",
       "                                 ..\n",
       "FERRARI GTC4Lusso                 1\n",
       "MERCEDES-BENZ GLA 250             1\n",
       "PORSCHE Macan GTS                 1\n",
       "VOLKSWAGEN Beetle Convertible     1\n",
       "AUDI A5 Cabriolet                 1\n",
       "Name: model, Length: 357, dtype: int64"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2018.model.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "--------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 8. Merging Datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8.1. Rename 2008 columns to distinguish from 2018 columns after the merge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "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",
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       "    }\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>model_2008</th>\n",
       "      <th>displ_2008</th>\n",
       "      <th>cyl_2008</th>\n",
       "      <th>trans_2008</th>\n",
       "      <th>drive_2008</th>\n",
       "      <th>fuel_2008</th>\n",
       "      <th>veh_class_2008</th>\n",
       "      <th>air_pollut_2008</th>\n",
       "      <th>city_mpg_2008</th>\n",
       "      <th>hwy_mpg_2008</th>\n",
       "      <th>cmb_mpg_2008</th>\n",
       "      <th>greenhouse_2008</th>\n",
       "      <th>smartway_2008</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ACURA MDX</td>\n",
       "      <td>3.7</td>\n",
       "      <td>6</td>\n",
       "      <td>Auto-S5</td>\n",
       "      <td>4WD</td>\n",
       "      <td>Gasoline</td>\n",
       "      <td>SUV</td>\n",
       "      <td>7.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>4</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ACURA RDX</td>\n",
       "      <td>2.3</td>\n",
       "      <td>4</td>\n",
       "      <td>Auto-S5</td>\n",
       "      <td>4WD</td>\n",
       "      <td>Gasoline</td>\n",
       "      <td>SUV</td>\n",
       "      <td>7.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>5</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  model_2008  displ_2008  cyl_2008 trans_2008 drive_2008 fuel_2008  \\\n",
       "0  ACURA MDX         3.7         6    Auto-S5        4WD  Gasoline   \n",
       "1  ACURA RDX         2.3         4    Auto-S5        4WD  Gasoline   \n",
       "\n",
       "  veh_class_2008  air_pollut_2008  city_mpg_2008  hwy_mpg_2008  cmb_mpg_2008  \\\n",
       "0            SUV              7.0           15.0          20.0          17.0   \n",
       "1            SUV              7.0           17.0          22.0          19.0   \n",
       "\n",
       "   greenhouse_2008 smartway_2008  \n",
       "0                4            no  \n",
       "1                5            no  "
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# postfix with \"_2008\" for each columns\n",
    "df_2008 = df_2008.rename(columns = lambda x : x[:10] + \"_2008\")\n",
    "df_2008.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8.2. Perform inner merge"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "we are only interested in how the same model of car has been updated and how the new model's mpg compares to the old model's mpg."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_combined = df_2008.merge(df_2018, left_on = \"model_2008\", right_on = \"model\", how = \"inner\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model_2008</th>\n",
       "      <th>displ_2008</th>\n",
       "      <th>cyl_2008</th>\n",
       "      <th>trans_2008</th>\n",
       "      <th>drive_2008</th>\n",
       "      <th>fuel_2008</th>\n",
       "      <th>veh_class_2008</th>\n",
       "      <th>air_pollut_2008</th>\n",
       "      <th>city_mpg_2008</th>\n",
       "      <th>hwy_mpg_2008</th>\n",
       "      <th>...</th>\n",
       "      <th>trans</th>\n",
       "      <th>drive</th>\n",
       "      <th>fuel</th>\n",
       "      <th>veh_class</th>\n",
       "      <th>air_pollution_score</th>\n",
       "      <th>city_mpg</th>\n",
       "      <th>hwy_mpg</th>\n",
       "      <th>cmb_mpg</th>\n",
       "      <th>greenhouse_gas_score</th>\n",
       "      <th>smartway</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ACURA RDX</td>\n",
       "      <td>2.3</td>\n",
       "      <td>4</td>\n",
       "      <td>Auto-S5</td>\n",
       "      <td>4WD</td>\n",
       "      <td>Gasoline</td>\n",
       "      <td>SUV</td>\n",
       "      <td>7.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>...</td>\n",
       "      <td>SemiAuto-6</td>\n",
       "      <td>2WD</td>\n",
       "      <td>Gasoline</td>\n",
       "      <td>small SUV</td>\n",
       "      <td>3.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>5</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ACURA RDX</td>\n",
       "      <td>2.3</td>\n",
       "      <td>4</td>\n",
       "      <td>Auto-S5</td>\n",
       "      <td>4WD</td>\n",
       "      <td>Gasoline</td>\n",
       "      <td>SUV</td>\n",
       "      <td>7.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>...</td>\n",
       "      <td>SemiAuto-6</td>\n",
       "      <td>4WD</td>\n",
       "      <td>Gasoline</td>\n",
       "      <td>small SUV</td>\n",
       "      <td>3.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>4</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AUDI A3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>Man-6</td>\n",
       "      <td>2WD</td>\n",
       "      <td>Gasoline</td>\n",
       "      <td>station wagon</td>\n",
       "      <td>7.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>...</td>\n",
       "      <td>AMS-6</td>\n",
       "      <td>4WD</td>\n",
       "      <td>Gasoline</td>\n",
       "      <td>small car</td>\n",
       "      <td>7.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>6</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AUDI A3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>Man-6</td>\n",
       "      <td>2WD</td>\n",
       "      <td>Gasoline</td>\n",
       "      <td>station wagon</td>\n",
       "      <td>7.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>...</td>\n",
       "      <td>AMS-7</td>\n",
       "      <td>2WD</td>\n",
       "      <td>Gasoline</td>\n",
       "      <td>small car</td>\n",
       "      <td>7.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>6</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AUDI A3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>Auto-S6</td>\n",
       "      <td>2WD</td>\n",
       "      <td>Gasoline</td>\n",
       "      <td>station wagon</td>\n",
       "      <td>7.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>...</td>\n",
       "      <td>AMS-6</td>\n",
       "      <td>4WD</td>\n",
       "      <td>Gasoline</td>\n",
       "      <td>small car</td>\n",
       "      <td>7.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>6</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  model_2008  displ_2008  cyl_2008 trans_2008 drive_2008 fuel_2008  \\\n",
       "0  ACURA RDX         2.3         4    Auto-S5        4WD  Gasoline   \n",
       "1  ACURA RDX         2.3         4    Auto-S5        4WD  Gasoline   \n",
       "2    AUDI A3         2.0         4      Man-6        2WD  Gasoline   \n",
       "3    AUDI A3         2.0         4      Man-6        2WD  Gasoline   \n",
       "4    AUDI A3         2.0         4    Auto-S6        2WD  Gasoline   \n",
       "\n",
       "  veh_class_2008  air_pollut_2008  city_mpg_2008  hwy_mpg_2008  ...  \\\n",
       "0            SUV              7.0           17.0          22.0  ...   \n",
       "1            SUV              7.0           17.0          22.0  ...   \n",
       "2  station wagon              7.0           21.0          29.0  ...   \n",
       "3  station wagon              7.0           21.0          29.0  ...   \n",
       "4  station wagon              7.0           22.0          29.0  ...   \n",
       "\n",
       "        trans  drive      fuel  veh_class  air_pollution_score  city_mpg  \\\n",
       "0  SemiAuto-6    2WD  Gasoline  small SUV                  3.0      20.0   \n",
       "1  SemiAuto-6    4WD  Gasoline  small SUV                  3.0      19.0   \n",
       "2       AMS-6    4WD  Gasoline  small car                  7.0      24.0   \n",
       "3       AMS-7    2WD  Gasoline  small car                  7.0      26.0   \n",
       "4       AMS-6    4WD  Gasoline  small car                  7.0      24.0   \n",
       "\n",
       "  hwy_mpg cmb_mpg greenhouse_gas_score smartway  \n",
       "0    28.0    23.0                    5       No  \n",
       "1    27.0    22.0                    4       No  \n",
       "2    31.0    27.0                    6       No  \n",
       "3    35.0    29.0                    6       No  \n",
       "4    31.0    27.0                    6       No  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check the result\n",
    "df_combined.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save the combined dataset , just for incase\n",
    "df_combined.to_csv(\"Data/models_still_produced_2018.csv\", index = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "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",
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       "    }\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>cmb_mpg_2008</th>\n",
       "      <th>cmb_mpg</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ACURA RDX</th>\n",
       "      <td>19.000000</td>\n",
       "      <td>22.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AUDI A3</th>\n",
       "      <td>23.333333</td>\n",
       "      <td>28.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AUDI A4</th>\n",
       "      <td>21.000000</td>\n",
       "      <td>27.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AUDI A6</th>\n",
       "      <td>19.666667</td>\n",
       "      <td>25.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AUDI A8 L</th>\n",
       "      <td>16.500000</td>\n",
       "      <td>22.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           cmb_mpg_2008    cmb_mpg\n",
       "model                             \n",
       "ACURA RDX     19.000000  22.500000\n",
       "AUDI A3       23.333333  28.000000\n",
       "AUDI A4       21.000000  27.000000\n",
       "AUDI A6       19.666667  25.666667\n",
       "AUDI A8 L     16.500000  22.000000"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1. Create a new dataframe, model_mpg, that contain the mean combined mpg values in 2008 and 2018 for each unique model\n",
    "combined_model_mpg  = df_combined.groupby([\"model\"])[[\"cmb_mpg_2008\", \"cmb_mpg\"]].mean()\n",
    "combined_model_mpg.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "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>cmb_mpg_2008</th>\n",
       "      <th>cmb_mpg</th>\n",
       "      <th>mpg_change</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>ACURA RDX</th>\n",
       "      <td>19.000000</td>\n",
       "      <td>22.500000</td>\n",
       "      <td>3.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AUDI A3</th>\n",
       "      <td>23.333333</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>4.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AUDI A4</th>\n",
       "      <td>21.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AUDI A6</th>\n",
       "      <td>19.666667</td>\n",
       "      <td>25.666667</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AUDI A8 L</th>\n",
       "      <td>16.500000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>5.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           cmb_mpg_2008    cmb_mpg  mpg_change\n",
       "model                                         \n",
       "ACURA RDX     19.000000  22.500000    3.500000\n",
       "AUDI A3       23.333333  28.000000    4.666667\n",
       "AUDI A4       21.000000  27.000000    6.000000\n",
       "AUDI A6       19.666667  25.666667    6.000000\n",
       "AUDI A8 L     16.500000  22.000000    5.500000"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2. Create a new column, mpg_change, with the change in mpg\n",
    "combined_model_mpg[\"mpg_change\"] = combined_model_mpg[\"cmb_mpg\"] - combined_model_mpg[\"cmb_mpg_2008\"]\n",
    "combined_model_mpg.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "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>cmb_mpg_2008</th>\n",
       "      <th>cmb_mpg</th>\n",
       "      <th>mpg_change</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>VOLVO XC 90</th>\n",
       "      <td>15.666667</td>\n",
       "      <td>32.2</td>\n",
       "      <td>16.533333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             cmb_mpg_2008  cmb_mpg  mpg_change\n",
       "model                                         \n",
       "VOLVO XC 90     15.666667     32.2   16.533333"
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 3. Find the vehicle that improved the most\n",
    "combined_model_mpg.sort_values(by = \"mpg_change\", ascending = False)[0:1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Summary Findings:\n",
    "VOLVO XC 90 is the best model that made the biggest improvement in combined mpg from 2008 to 2018. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----------"
   ]
  }
 ],
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