drawing_conclusions_Fuel.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Drawing Conclusions\n",
    "Use the space below to address questions on datasets `clean_08.csv` and `clean_18.csv`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "% matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# load datasets\n",
    "\n",
    "df_08 = pd.read_csv('clean_08.csv')\n",
    "df_18 = pd.read_csv('clean_18.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 987 entries, 0 to 986\n",
      "Data columns (total 13 columns):\n",
      "model                   987 non-null object\n",
      "displ                   987 non-null float64\n",
      "cyl                     987 non-null int64\n",
      "trans                   987 non-null object\n",
      "drive                   987 non-null object\n",
      "fuel                    987 non-null object\n",
      "veh_class               987 non-null object\n",
      "air_pollution_score     987 non-null float64\n",
      "city_mpg                987 non-null float64\n",
      "hwy_mpg                 987 non-null float64\n",
      "cmb_mpg                 987 non-null float64\n",
      "greenhouse_gas_score    987 non-null int64\n",
      "smartway                987 non-null object\n",
      "dtypes: float64(5), int64(2), object(6)\n",
      "memory usage: 100.3+ KB\n"
     ]
    }
   ],
   "source": [
    "df_08.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Q1: Are more unique models using alternative sources of fuel? By how much?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
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       "      <th>AUDI Q7</th>\n",
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       "      <td>2</td>\n",
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       "      <th>AUDI RS4</th>\n",
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       "    <tr>\n",
       "      <th>AUDI RS4 Cabriolet</th>\n",
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       "      <th>AUDI S4 Avant</th>\n",
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       "      <td>1</td>\n",
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       "      <th>TOYOTA Scion XD</th>\n",
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       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>TOYOTA Sequoia</th>\n",
       "      <td>4</td>\n",
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       "      <td>4</td>\n",
       "      <td>4</td>\n",
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       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>TOYOTA Sienna</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>TOYOTA Solara</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TOYOTA Solara Convertible</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TOYOTA Tacoma</th>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TOYOTA Tundra</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TOYOTA Yaris</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VOLKSWAGEN Eos</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VOLKSWAGEN GTI</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VOLKSWAGEN Jetta</th>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VOLKSWAGEN Jetta Wagon</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VOLKSWAGEN New Beetle</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VOLKSWAGEN New Beetle Convertible</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VOLKSWAGEN Passat</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VOLKSWAGEN Passat Wagon</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VOLKSWAGEN R32</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VOLKSWAGEN Rabbit</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VOLKSWAGEN Touareg</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VOLVO C30</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VOLVO C70 Convertible</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VOLVO S40</th>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VOLVO S60</th>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VOLVO S80</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VOLVO V50</th>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VOLVO V70</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VOLVO XC 70</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VOLVO XC 90</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ethanol</th>\n",
       "      <th>MERCEDES-BENZ C300</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gas</th>\n",
       "      <th>MERCEDES-BENZ C300</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>379 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            displ  cyl  trans  drive  \\\n",
       "fuel     model                                                         \n",
       "CNG      HONDA Civic                            1    1      1      1   \n",
       "Gasoline ACURA MDX                              1    1      1      1   \n",
       "         ACURA RDX                              1    1      1      1   \n",
       "         ACURA RL                               1    1      1      1   \n",
       "         ACURA TL                               3    3      3      3   \n",
       "         ACURA TSX                              2    2      2      2   \n",
       "         ASTON MARTIN DB9                       2    2      2      2   \n",
       "         ASTON MARTIN DB9 Volante               2    2      2      2   \n",
       "         ASTON MARTIN V8 Vantage                2    2      2      2   \n",
       "         AUDI A3                                3    3      3      3   \n",
       "         AUDI A4                                4    4      4      4   \n",
       "         AUDI A4 Avant                          2    2      2      2   \n",
       "         AUDI A4 Cabriolet                      1    1      1      1   \n",
       "         AUDI A6                                3    3      3      3   \n",
       "         AUDI A6 Avant                          1    1      1      1   \n",
       "         AUDI A8                                1    1      1      1   \n",
       "         AUDI A8 L                              2    2      2      2   \n",
       "         AUDI Q7                                2    2      2      2   \n",
       "         AUDI R8                                2    2      2      2   \n",
       "         AUDI RS4                               1    1      1      1   \n",
       "         AUDI RS4 Cabriolet                     1    1      1      1   \n",
       "         AUDI S4                                2    2      2      2   \n",
       "         AUDI S4 Avant                          2    2      2      2   \n",
       "         AUDI S4 Cabriolet                      2    2      2      2   \n",
       "         AUDI S5                                1    1      1      1   \n",
       "         AUDI S6                                1    1      1      1   \n",
       "         AUDI S8                                1    1      1      1   \n",
       "         AUDI TT Coupe                          3    3      3      3   \n",
       "         AUDI TT Roadster                       3    3      3      3   \n",
       "         BENTLEY Arnage                         2    2      2      2   \n",
       "...                                           ...  ...    ...    ...   \n",
       "         TOYOTA Scion XD                        2    2      2      2   \n",
       "         TOYOTA Sequoia                         4    4      4      4   \n",
       "         TOYOTA Sienna                          2    2      2      2   \n",
       "         TOYOTA Solara                          3    3      3      3   \n",
       "         TOYOTA Solara Convertible              1    1      1      1   \n",
       "         TOYOTA Tacoma                          7    7      7      7   \n",
       "         TOYOTA Tundra                          5    5      5      5   \n",
       "         TOYOTA Yaris                           2    2      2      2   \n",
       "         VOLKSWAGEN Eos                         3    3      3      3   \n",
       "         VOLKSWAGEN GTI                         2    2      2      2   \n",
       "         VOLKSWAGEN Jetta                       6    6      6      6   \n",
       "         VOLKSWAGEN Jetta Wagon                 4    4      4      4   \n",
       "         VOLKSWAGEN New Beetle                  4    4      4      4   \n",
       "         VOLKSWAGEN New Beetle Convertible      4    4      4      4   \n",
       "         VOLKSWAGEN Passat                      4    4      4      4   \n",
       "         VOLKSWAGEN Passat Wagon                3    3      3      3   \n",
       "         VOLKSWAGEN R32                         1    1      1      1   \n",
       "         VOLKSWAGEN Rabbit                      4    4      4      4   \n",
       "         VOLKSWAGEN Touareg                     2    2      2      2   \n",
       "         VOLVO C30                              4    4      4      4   \n",
       "         VOLVO C70 Convertible                  2    2      2      2   \n",
       "         VOLVO S40                              7    7      7      7   \n",
       "         VOLVO S60                              6    6      6      6   \n",
       "         VOLVO S80                              4    4      4      4   \n",
       "         VOLVO V50                              7    7      7      7   \n",
       "         VOLVO V70                              1    1      1      1   \n",
       "         VOLVO XC 70                            1    1      1      1   \n",
       "         VOLVO XC 90                            3    3      3      3   \n",
       "ethanol  MERCEDES-BENZ C300                     1    1      1      1   \n",
       "gas      MERCEDES-BENZ C300                     1    1      1      1   \n",
       "\n",
       "                                            veh_class  air_pollution_score  \\\n",
       "fuel     model                                                               \n",
       "CNG      HONDA Civic                                1                    1   \n",
       "Gasoline ACURA MDX                                  1                    1   \n",
       "         ACURA RDX                                  1                    1   \n",
       "         ACURA RL                                   1                    1   \n",
       "         ACURA TL                                   3                    3   \n",
       "         ACURA TSX                                  2                    2   \n",
       "         ASTON MARTIN DB9                           2                    2   \n",
       "         ASTON MARTIN DB9 Volante                   2                    2   \n",
       "         ASTON MARTIN V8 Vantage                    2                    2   \n",
       "         AUDI A3                                    3                    3   \n",
       "         AUDI A4                                    4                    4   \n",
       "         AUDI A4 Avant                              2                    2   \n",
       "         AUDI A4 Cabriolet                          1                    1   \n",
       "         AUDI A6                                    3                    3   \n",
       "         AUDI A6 Avant                              1                    1   \n",
       "         AUDI A8                                    1                    1   \n",
       "         AUDI A8 L                                  2                    2   \n",
       "         AUDI Q7                                    2                    2   \n",
       "         AUDI R8                                    2                    2   \n",
       "         AUDI RS4                                   1                    1   \n",
       "         AUDI RS4 Cabriolet                         1                    1   \n",
       "         AUDI S4                                    2                    2   \n",
       "         AUDI S4 Avant                              2                    2   \n",
       "         AUDI S4 Cabriolet                          2                    2   \n",
       "         AUDI S5                                    1                    1   \n",
       "         AUDI S6                                    1                    1   \n",
       "         AUDI S8                                    1                    1   \n",
       "         AUDI TT Coupe                              3                    3   \n",
       "         AUDI TT Roadster                           3                    3   \n",
       "         BENTLEY Arnage                             2                    2   \n",
       "...                                               ...                  ...   \n",
       "         TOYOTA Scion XD                            2                    2   \n",
       "         TOYOTA Sequoia                             4                    4   \n",
       "         TOYOTA Sienna                              2                    2   \n",
       "         TOYOTA Solara                              3                    3   \n",
       "         TOYOTA Solara Convertible                  1                    1   \n",
       "         TOYOTA Tacoma                              7                    7   \n",
       "         TOYOTA Tundra                              5                    5   \n",
       "         TOYOTA Yaris                               2                    2   \n",
       "         VOLKSWAGEN Eos                             3                    3   \n",
       "         VOLKSWAGEN GTI                             2                    2   \n",
       "         VOLKSWAGEN Jetta                           6                    6   \n",
       "         VOLKSWAGEN Jetta Wagon                     4                    4   \n",
       "         VOLKSWAGEN New Beetle                      4                    4   \n",
       "         VOLKSWAGEN New Beetle Convertible          4                    4   \n",
       "         VOLKSWAGEN Passat                          4                    4   \n",
       "         VOLKSWAGEN Passat Wagon                    3                    3   \n",
       "         VOLKSWAGEN R32                             1                    1   \n",
       "         VOLKSWAGEN Rabbit                          4                    4   \n",
       "         VOLKSWAGEN Touareg                         2                    2   \n",
       "         VOLVO C30                                  4                    4   \n",
       "         VOLVO C70 Convertible                      2                    2   \n",
       "         VOLVO S40                                  7                    7   \n",
       "         VOLVO S60                                  6                    6   \n",
       "         VOLVO S80                                  4                    4   \n",
       "         VOLVO V50                                  7                    7   \n",
       "         VOLVO V70                                  1                    1   \n",
       "         VOLVO XC 70                                1                    1   \n",
       "         VOLVO XC 90                                3                    3   \n",
       "ethanol  MERCEDES-BENZ C300                         1                    1   \n",
       "gas      MERCEDES-BENZ C300                         1                    1   \n",
       "\n",
       "                                            city_mpg  hwy_mpg  cmb_mpg  \\\n",
       "fuel     model                                                           \n",
       "CNG      HONDA Civic                               1        1        1   \n",
       "Gasoline ACURA MDX                                 1        1        1   \n",
       "         ACURA RDX                                 1        1        1   \n",
       "         ACURA RL                                  1        1        1   \n",
       "         ACURA TL                                  3        3        3   \n",
       "         ACURA TSX                                 2        2        2   \n",
       "         ASTON MARTIN DB9                          2        2        2   \n",
       "         ASTON MARTIN DB9 Volante                  2        2        2   \n",
       "         ASTON MARTIN V8 Vantage                   2        2        2   \n",
       "         AUDI A3                                   3        3        3   \n",
       "         AUDI A4                                   4        4        4   \n",
       "         AUDI A4 Avant                             2        2        2   \n",
       "         AUDI A4 Cabriolet                         1        1        1   \n",
       "         AUDI A6                                   3        3        3   \n",
       "         AUDI A6 Avant                             1        1        1   \n",
       "         AUDI A8                                   1        1        1   \n",
       "         AUDI A8 L                                 2        2        2   \n",
       "         AUDI Q7                                   2        2        2   \n",
       "         AUDI R8                                   2        2        2   \n",
       "         AUDI RS4                                  1        1        1   \n",
       "         AUDI RS4 Cabriolet                        1        1        1   \n",
       "         AUDI S4                                   2        2        2   \n",
       "         AUDI S4 Avant                             2        2        2   \n",
       "         AUDI S4 Cabriolet                         2        2        2   \n",
       "         AUDI S5                                   1        1        1   \n",
       "         AUDI S6                                   1        1        1   \n",
       "         AUDI S8                                   1        1        1   \n",
       "         AUDI TT Coupe                             3        3        3   \n",
       "         AUDI TT Roadster                          3        3        3   \n",
       "         BENTLEY Arnage                            2        2        2   \n",
       "...                                              ...      ...      ...   \n",
       "         TOYOTA Scion XD                           2        2        2   \n",
       "         TOYOTA Sequoia                            4        4        4   \n",
       "         TOYOTA Sienna                             2        2        2   \n",
       "         TOYOTA Solara                             3        3        3   \n",
       "         TOYOTA Solara Convertible                 1        1        1   \n",
       "         TOYOTA Tacoma                             7        7        7   \n",
       "         TOYOTA Tundra                             5        5        5   \n",
       "         TOYOTA Yaris                              2        2        2   \n",
       "         VOLKSWAGEN Eos                            3        3        3   \n",
       "         VOLKSWAGEN GTI                            2        2        2   \n",
       "         VOLKSWAGEN Jetta                          6        6        6   \n",
       "         VOLKSWAGEN Jetta Wagon                    4        4        4   \n",
       "         VOLKSWAGEN New Beetle                     4        4        4   \n",
       "         VOLKSWAGEN New Beetle Convertible         4        4        4   \n",
       "         VOLKSWAGEN Passat                         4        4        4   \n",
       "         VOLKSWAGEN Passat Wagon                   3        3        3   \n",
       "         VOLKSWAGEN R32                            1        1        1   \n",
       "         VOLKSWAGEN Rabbit                         4        4        4   \n",
       "         VOLKSWAGEN Touareg                        2        2        2   \n",
       "         VOLVO C30                                 4        4        4   \n",
       "         VOLVO C70 Convertible                     2        2        2   \n",
       "         VOLVO S40                                 7        7        7   \n",
       "         VOLVO S60                                 6        6        6   \n",
       "         VOLVO S80                                 4        4        4   \n",
       "         VOLVO V50                                 7        7        7   \n",
       "         VOLVO V70                                 1        1        1   \n",
       "         VOLVO XC 70                               1        1        1   \n",
       "         VOLVO XC 90                               3        3        3   \n",
       "ethanol  MERCEDES-BENZ C300                        1        1        1   \n",
       "gas      MERCEDES-BENZ C300                        1        1        1   \n",
       "\n",
       "                                            greenhouse_gas_score  smartway  \n",
       "fuel     model                                                              \n",
       "CNG      HONDA Civic                                           1         1  \n",
       "Gasoline ACURA MDX                                             1         1  \n",
       "         ACURA RDX                                             1         1  \n",
       "         ACURA RL                                              1         1  \n",
       "         ACURA TL                                              3         3  \n",
       "         ACURA TSX                                             2         2  \n",
       "         ASTON MARTIN DB9                                      2         2  \n",
       "         ASTON MARTIN DB9 Volante                              2         2  \n",
       "         ASTON MARTIN V8 Vantage                               2         2  \n",
       "         AUDI A3                                               3         3  \n",
       "         AUDI A4                                               4         4  \n",
       "         AUDI A4 Avant                                         2         2  \n",
       "         AUDI A4 Cabriolet                                     1         1  \n",
       "         AUDI A6                                               3         3  \n",
       "         AUDI A6 Avant                                         1         1  \n",
       "         AUDI A8                                               1         1  \n",
       "         AUDI A8 L                                             2         2  \n",
       "         AUDI Q7                                               2         2  \n",
       "         AUDI R8                                               2         2  \n",
       "         AUDI RS4                                              1         1  \n",
       "         AUDI RS4 Cabriolet                                    1         1  \n",
       "         AUDI S4                                               2         2  \n",
       "         AUDI S4 Avant                                         2         2  \n",
       "         AUDI S4 Cabriolet                                     2         2  \n",
       "         AUDI S5                                               1         1  \n",
       "         AUDI S6                                               1         1  \n",
       "         AUDI S8                                               1         1  \n",
       "         AUDI TT Coupe                                         3         3  \n",
       "         AUDI TT Roadster                                      3         3  \n",
       "         BENTLEY Arnage                                        2         2  \n",
       "...                                                          ...       ...  \n",
       "         TOYOTA Scion XD                                       2         2  \n",
       "         TOYOTA Sequoia                                        4         4  \n",
       "         TOYOTA Sienna                                         2         2  \n",
       "         TOYOTA Solara                                         3         3  \n",
       "         TOYOTA Solara Convertible                             1         1  \n",
       "         TOYOTA Tacoma                                         7         7  \n",
       "         TOYOTA Tundra                                         5         5  \n",
       "         TOYOTA Yaris                                          2         2  \n",
       "         VOLKSWAGEN Eos                                        3         3  \n",
       "         VOLKSWAGEN GTI                                        2         2  \n",
       "         VOLKSWAGEN Jetta                                      6         6  \n",
       "         VOLKSWAGEN Jetta Wagon                                4         4  \n",
       "         VOLKSWAGEN New Beetle                                 4         4  \n",
       "         VOLKSWAGEN New Beetle Convertible                     4         4  \n",
       "         VOLKSWAGEN Passat                                     4         4  \n",
       "         VOLKSWAGEN Passat Wagon                               3         3  \n",
       "         VOLKSWAGEN R32                                        1         1  \n",
       "         VOLKSWAGEN Rabbit                                     4         4  \n",
       "         VOLKSWAGEN Touareg                                    2         2  \n",
       "         VOLVO C30                                             4         4  \n",
       "         VOLVO C70 Convertible                                 2         2  \n",
       "         VOLVO S40                                             7         7  \n",
       "         VOLVO S60                                             6         6  \n",
       "         VOLVO S80                                             4         4  \n",
       "         VOLVO V50                                             7         7  \n",
       "         VOLVO V70                                             1         1  \n",
       "         VOLVO XC 70                                           1         1  \n",
       "         VOLVO XC 90                                           3         3  \n",
       "ethanol  MERCEDES-BENZ C300                                    1         1  \n",
       "gas      MERCEDES-BENZ C300                                    1         1  \n",
       "\n",
       "[379 rows x 11 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_08.groupby(['fuel','model']).count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>displ</th>\n",
       "      <th>cyl</th>\n",
       "      <th>trans</th>\n",
       "      <th>drive</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",
       "    <tr>\n",
       "      <th>fuel</th>\n",
       "      <th>model</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"11\" valign=\"top\">Diesel</th>\n",
       "      <th>CHEVROLET Colorado</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CHEVROLET Colorado ZR2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CHEVROLET Cruze</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CHEVROLET Cruze Hatchback</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CHEVROLET Equinox</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GMC Canyon</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GMC Terrain</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JAGUAR F-Pace</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JAGUAR XE</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JAGUAR XF</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LAND ROVER Range Rover Velar</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"10\" valign=\"top\">Electricity</th>\n",
       "      <th>BMW 330e</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BMW 530e</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BMW 740e</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CHEVROLET Volt</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FORD Fusion Energi Plug-in Hybrid</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KARMA Revero</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MINI Cooper SE Countryman All4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VOLVO S90</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VOLVO XC 60</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VOLVO XC 90</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"9\" valign=\"top\">Ethanol</th>\n",
       "      <th>CHEVROLET Impala</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CHEVROLET Silverado 15</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CHEVROLET Suburban 1500</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CHEVROLET Tahoe 1500</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CHRYSLER 300</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DODGE Charger</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GMC Sierra 15</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GMC Yukon 1500</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GMC Yukon 1500 XL</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"30\" valign=\"top\">Gasoline</th>\n",
       "      <th>TOYOTA RAV4</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TOYOTA RAV4 Hybrid</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TOYOTA RAV4 LE/XLE</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TOYOTA RAV4 Limited</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TOYOTA Sequoia</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TOYOTA Tundra</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TOYOTA Yaris</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VOLKSWAGEN Beetle</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VOLKSWAGEN Beetle Convertible</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VOLKSWAGEN Beetle Dune</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VOLKSWAGEN Beetle Dune Convertible</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VOLKSWAGEN Golf R</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VOLKSWAGEN Jetta</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VOLKSWAGEN Passat</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VOLKSWAGEN Tiguan</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VOLKSWAGEN Tiguan 4Motion</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VOLVO Atlas</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VOLVO Atlas 4Motion</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VOLVO S60</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VOLVO S60 CC</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VOLVO S60 Inscription</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VOLVO S60 Polestar</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VOLVO S90</th>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VOLVO V60</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VOLVO V60 CC</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VOLVO V60 Polestar</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VOLVO V90</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VOLVO V90 CC</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VOLVO XC 60</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VOLVO XC 90</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>408 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                displ  cyl  trans  drive  \\\n",
       "fuel        model                                                          \n",
       "Diesel      CHEVROLET Colorado                      2    2      2      2   \n",
       "            CHEVROLET Colorado ZR2                  1    1      1      1   \n",
       "            CHEVROLET Cruze                         2    2      2      2   \n",
       "            CHEVROLET Cruze Hatchback               2    2      2      2   \n",
       "            CHEVROLET Equinox                       2    2      2      2   \n",
       "            GMC Canyon                              2    2      2      2   \n",
       "            GMC Terrain                             2    2      2      2   \n",
       "            JAGUAR F-Pace                           1    1      1      1   \n",
       "            JAGUAR XE                               2    2      2      2   \n",
       "            JAGUAR XF                               2    2      2      2   \n",
       "            LAND ROVER Range Rover Velar            1    1      1      1   \n",
       "Electricity BMW 330e                                1    1      1      1   \n",
       "            BMW 530e                                2    2      2      2   \n",
       "            BMW 740e                                1    1      1      1   \n",
       "            CHEVROLET Volt                          2    2      2      2   \n",
       "            FORD Fusion Energi Plug-in Hybrid       1    1      1      1   \n",
       "            KARMA Revero                            1    1      1      1   \n",
       "            MINI Cooper SE Countryman All4          1    1      1      1   \n",
       "            VOLVO S90                               1    1      1      1   \n",
       "            VOLVO XC 60                             1    1      1      1   \n",
       "            VOLVO XC 90                             1    1      1      1   \n",
       "Ethanol     CHEVROLET Impala                        1    1      1      1   \n",
       "            CHEVROLET Silverado 15                  3    3      3      3   \n",
       "            CHEVROLET Suburban 1500                 2    2      2      2   \n",
       "            CHEVROLET Tahoe 1500                    2    2      2      2   \n",
       "            CHRYSLER 300                            2    2      2      2   \n",
       "            DODGE Charger                           2    2      2      2   \n",
       "            GMC Sierra 15                           3    3      3      3   \n",
       "            GMC Yukon 1500                          2    2      2      2   \n",
       "            GMC Yukon 1500 XL                       1    1      1      1   \n",
       "...                                               ...  ...    ...    ...   \n",
       "Gasoline    TOYOTA RAV4                             2    2      2      2   \n",
       "            TOYOTA RAV4 Hybrid                      1    1      1      1   \n",
       "            TOYOTA RAV4 LE/XLE                      1    1      1      1   \n",
       "            TOYOTA RAV4 Limited                     1    1      1      1   \n",
       "            TOYOTA Sequoia                          2    2      2      2   \n",
       "            TOYOTA Tundra                           4    4      4      4   \n",
       "            TOYOTA Yaris                            2    2      2      2   \n",
       "            VOLKSWAGEN Beetle                       1    1      1      1   \n",
       "            VOLKSWAGEN Beetle Convertible           1    1      1      1   \n",
       "            VOLKSWAGEN Beetle Dune                  1    1      1      1   \n",
       "            VOLKSWAGEN Beetle Dune Convertible      1    1      1      1   \n",
       "            VOLKSWAGEN Golf R                       2    2      2      2   \n",
       "            VOLKSWAGEN Jetta                        4    4      4      4   \n",
       "            VOLKSWAGEN Passat                       1    1      1      1   \n",
       "            VOLKSWAGEN Tiguan                       1    1      1      1   \n",
       "            VOLKSWAGEN Tiguan 4Motion               1    1      1      1   \n",
       "            VOLVO Atlas                             1    1      1      1   \n",
       "            VOLVO Atlas 4Motion                     1    1      1      1   \n",
       "            VOLVO S60                               3    3      3      3   \n",
       "            VOLVO S60 CC                            1    1      1      1   \n",
       "            VOLVO S60 Inscription                   2    2      2      2   \n",
       "            VOLVO S60 Polestar                      1    1      1      1   \n",
       "            VOLVO S90                               6    6      6      6   \n",
       "            VOLVO V60                               3    3      3      3   \n",
       "            VOLVO V60 CC                            1    1      1      1   \n",
       "            VOLVO V60 Polestar                      1    1      1      1   \n",
       "            VOLVO V90                               2    2      2      2   \n",
       "            VOLVO V90 CC                            2    2      2      2   \n",
       "            VOLVO XC 60                             3    3      3      3   \n",
       "            VOLVO XC 90                             4    4      4      4   \n",
       "\n",
       "                                                veh_class  \\\n",
       "fuel        model                                           \n",
       "Diesel      CHEVROLET Colorado                          2   \n",
       "            CHEVROLET Colorado ZR2                      1   \n",
       "            CHEVROLET Cruze                             2   \n",
       "            CHEVROLET Cruze Hatchback                   2   \n",
       "            CHEVROLET Equinox                           2   \n",
       "            GMC Canyon                                  2   \n",
       "            GMC Terrain                                 2   \n",
       "            JAGUAR F-Pace                               1   \n",
       "            JAGUAR XE                                   2   \n",
       "            JAGUAR XF                                   2   \n",
       "            LAND ROVER Range Rover Velar                1   \n",
       "Electricity BMW 330e                                    1   \n",
       "            BMW 530e                                    2   \n",
       "            BMW 740e                                    1   \n",
       "            CHEVROLET Volt                              2   \n",
       "            FORD Fusion Energi Plug-in Hybrid           1   \n",
       "            KARMA Revero                                1   \n",
       "            MINI Cooper SE Countryman All4              1   \n",
       "            VOLVO S90                                   1   \n",
       "            VOLVO XC 60                                 1   \n",
       "            VOLVO XC 90                                 1   \n",
       "Ethanol     CHEVROLET Impala                            1   \n",
       "            CHEVROLET Silverado 15                      3   \n",
       "            CHEVROLET Suburban 1500                     2   \n",
       "            CHEVROLET Tahoe 1500                        2   \n",
       "            CHRYSLER 300                                2   \n",
       "            DODGE Charger                               2   \n",
       "            GMC Sierra 15                               3   \n",
       "            GMC Yukon 1500                              2   \n",
       "            GMC Yukon 1500 XL                           1   \n",
       "...                                                   ...   \n",
       "Gasoline    TOYOTA RAV4                                 2   \n",
       "            TOYOTA RAV4 Hybrid                          1   \n",
       "            TOYOTA RAV4 LE/XLE                          1   \n",
       "            TOYOTA RAV4 Limited                         1   \n",
       "            TOYOTA Sequoia                              2   \n",
       "            TOYOTA Tundra                               4   \n",
       "            TOYOTA Yaris                                2   \n",
       "            VOLKSWAGEN Beetle                           1   \n",
       "            VOLKSWAGEN Beetle Convertible               1   \n",
       "            VOLKSWAGEN Beetle Dune                      1   \n",
       "            VOLKSWAGEN Beetle Dune Convertible          1   \n",
       "            VOLKSWAGEN Golf R                           2   \n",
       "            VOLKSWAGEN Jetta                            4   \n",
       "            VOLKSWAGEN Passat                           1   \n",
       "            VOLKSWAGEN Tiguan                           1   \n",
       "            VOLKSWAGEN Tiguan 4Motion                   1   \n",
       "            VOLVO Atlas                                 1   \n",
       "            VOLVO Atlas 4Motion                         1   \n",
       "            VOLVO S60                                   3   \n",
       "            VOLVO S60 CC                                1   \n",
       "            VOLVO S60 Inscription                       2   \n",
       "            VOLVO S60 Polestar                          1   \n",
       "            VOLVO S90                                   6   \n",
       "            VOLVO V60                                   3   \n",
       "            VOLVO V60 CC                                1   \n",
       "            VOLVO V60 Polestar                          1   \n",
       "            VOLVO V90                                   2   \n",
       "            VOLVO V90 CC                                2   \n",
       "            VOLVO XC 60                                 3   \n",
       "            VOLVO XC 90                                 4   \n",
       "\n",
       "                                                air_pollution_score  city_mpg  \\\n",
       "fuel        model                                                               \n",
       "Diesel      CHEVROLET Colorado                                    2         2   \n",
       "            CHEVROLET Colorado ZR2                                1         1   \n",
       "            CHEVROLET Cruze                                       2         2   \n",
       "            CHEVROLET Cruze Hatchback                             2         2   \n",
       "            CHEVROLET Equinox                                     2         2   \n",
       "            GMC Canyon                                            2         2   \n",
       "            GMC Terrain                                           2         2   \n",
       "            JAGUAR F-Pace                                         1         1   \n",
       "            JAGUAR XE                                             2         2   \n",
       "            JAGUAR XF                                             2         2   \n",
       "            LAND ROVER Range Rover Velar                          1         1   \n",
       "Electricity BMW 330e                                              1         1   \n",
       "            BMW 530e                                              2         2   \n",
       "            BMW 740e                                              1         1   \n",
       "            CHEVROLET Volt                                        2         2   \n",
       "            FORD Fusion Energi Plug-in Hybrid                     1         1   \n",
       "            KARMA Revero                                          1         1   \n",
       "            MINI Cooper SE Countryman All4                        1         1   \n",
       "            VOLVO S90                                             1         1   \n",
       "            VOLVO XC 60                                           1         1   \n",
       "            VOLVO XC 90                                           1         1   \n",
       "Ethanol     CHEVROLET Impala                                      1         1   \n",
       "            CHEVROLET Silverado 15                                3         3   \n",
       "            CHEVROLET Suburban 1500                               2         2   \n",
       "            CHEVROLET Tahoe 1500                                  2         2   \n",
       "            CHRYSLER 300                                          2         2   \n",
       "            DODGE Charger                                         2         2   \n",
       "            GMC Sierra 15                                         3         3   \n",
       "            GMC Yukon 1500                                        2         2   \n",
       "            GMC Yukon 1500 XL                                     1         1   \n",
       "...                                                             ...       ...   \n",
       "Gasoline    TOYOTA RAV4                                           2         2   \n",
       "            TOYOTA RAV4 Hybrid                                    1         1   \n",
       "            TOYOTA RAV4 LE/XLE                                    1         1   \n",
       "            TOYOTA RAV4 Limited                                   1         1   \n",
       "            TOYOTA Sequoia                                        2         2   \n",
       "            TOYOTA Tundra                                         4         4   \n",
       "            TOYOTA Yaris                                          2         2   \n",
       "            VOLKSWAGEN Beetle                                     1         1   \n",
       "            VOLKSWAGEN Beetle Convertible                         1         1   \n",
       "            VOLKSWAGEN Beetle Dune                                1         1   \n",
       "            VOLKSWAGEN Beetle Dune Convertible                    1         1   \n",
       "            VOLKSWAGEN Golf R                                     2         2   \n",
       "            VOLKSWAGEN Jetta                                      4         4   \n",
       "            VOLKSWAGEN Passat                                     1         1   \n",
       "            VOLKSWAGEN Tiguan                                     1         1   \n",
       "            VOLKSWAGEN Tiguan 4Motion                             1         1   \n",
       "            VOLVO Atlas                                           1         1   \n",
       "            VOLVO Atlas 4Motion                                   1         1   \n",
       "            VOLVO S60                                             3         3   \n",
       "            VOLVO S60 CC                                          1         1   \n",
       "            VOLVO S60 Inscription                                 2         2   \n",
       "            VOLVO S60 Polestar                                    1         1   \n",
       "            VOLVO S90                                             6         6   \n",
       "            VOLVO V60                                             3         3   \n",
       "            VOLVO V60 CC                                          1         1   \n",
       "            VOLVO V60 Polestar                                    1         1   \n",
       "            VOLVO V90                                             2         2   \n",
       "            VOLVO V90 CC                                          2         2   \n",
       "            VOLVO XC 60                                           3         3   \n",
       "            VOLVO XC 90                                           4         4   \n",
       "\n",
       "                                                hwy_mpg  cmb_mpg  \\\n",
       "fuel        model                                                  \n",
       "Diesel      CHEVROLET Colorado                        2        2   \n",
       "            CHEVROLET Colorado ZR2                    1        1   \n",
       "            CHEVROLET Cruze                           2        2   \n",
       "            CHEVROLET Cruze Hatchback                 2        2   \n",
       "            CHEVROLET Equinox                         2        2   \n",
       "            GMC Canyon                                2        2   \n",
       "            GMC Terrain                               2        2   \n",
       "            JAGUAR F-Pace                             1        1   \n",
       "            JAGUAR XE                                 2        2   \n",
       "            JAGUAR XF                                 2        2   \n",
       "            LAND ROVER Range Rover Velar              1        1   \n",
       "Electricity BMW 330e                                  1        1   \n",
       "            BMW 530e                                  2        2   \n",
       "            BMW 740e                                  1        1   \n",
       "            CHEVROLET Volt                            2        2   \n",
       "            FORD Fusion Energi Plug-in Hybrid         1        1   \n",
       "            KARMA Revero                              1        1   \n",
       "            MINI Cooper SE Countryman All4            1        1   \n",
       "            VOLVO S90                                 1        1   \n",
       "            VOLVO XC 60                               1        1   \n",
       "            VOLVO XC 90                               1        1   \n",
       "Ethanol     CHEVROLET Impala                          1        1   \n",
       "            CHEVROLET Silverado 15                    3        3   \n",
       "            CHEVROLET Suburban 1500                   2        2   \n",
       "            CHEVROLET Tahoe 1500                      2        2   \n",
       "            CHRYSLER 300                              2        2   \n",
       "            DODGE Charger                             2        2   \n",
       "            GMC Sierra 15                             3        3   \n",
       "            GMC Yukon 1500                            2        2   \n",
       "            GMC Yukon 1500 XL                         1        1   \n",
       "...                                                 ...      ...   \n",
       "Gasoline    TOYOTA RAV4                               2        2   \n",
       "            TOYOTA RAV4 Hybrid                        1        1   \n",
       "            TOYOTA RAV4 LE/XLE                        1        1   \n",
       "            TOYOTA RAV4 Limited                       1        1   \n",
       "            TOYOTA Sequoia                            2        2   \n",
       "            TOYOTA Tundra                             4        4   \n",
       "            TOYOTA Yaris                              2        2   \n",
       "            VOLKSWAGEN Beetle                         1        1   \n",
       "            VOLKSWAGEN Beetle Convertible             1        1   \n",
       "            VOLKSWAGEN Beetle Dune                    1        1   \n",
       "            VOLKSWAGEN Beetle Dune Convertible        1        1   \n",
       "            VOLKSWAGEN Golf R                         2        2   \n",
       "            VOLKSWAGEN Jetta                          4        4   \n",
       "            VOLKSWAGEN Passat                         1        1   \n",
       "            VOLKSWAGEN Tiguan                         1        1   \n",
       "            VOLKSWAGEN Tiguan 4Motion                 1        1   \n",
       "            VOLVO Atlas                               1        1   \n",
       "            VOLVO Atlas 4Motion                       1        1   \n",
       "            VOLVO S60                                 3        3   \n",
       "            VOLVO S60 CC                              1        1   \n",
       "            VOLVO S60 Inscription                     2        2   \n",
       "            VOLVO S60 Polestar                        1        1   \n",
       "            VOLVO S90                                 6        6   \n",
       "            VOLVO V60                                 3        3   \n",
       "            VOLVO V60 CC                              1        1   \n",
       "            VOLVO V60 Polestar                        1        1   \n",
       "            VOLVO V90                                 2        2   \n",
       "            VOLVO V90 CC                              2        2   \n",
       "            VOLVO XC 60                               3        3   \n",
       "            VOLVO XC 90                               4        4   \n",
       "\n",
       "                                                greenhouse_gas_score  smartway  \n",
       "fuel        model                                                               \n",
       "Diesel      CHEVROLET Colorado                                     2         2  \n",
       "            CHEVROLET Colorado ZR2                                 1         1  \n",
       "            CHEVROLET Cruze                                        2         2  \n",
       "            CHEVROLET Cruze Hatchback                              2         2  \n",
       "            CHEVROLET Equinox                                      2         2  \n",
       "            GMC Canyon                                             2         2  \n",
       "            GMC Terrain                                            2         2  \n",
       "            JAGUAR F-Pace                                          1         1  \n",
       "            JAGUAR XE                                              2         2  \n",
       "            JAGUAR XF                                              2         2  \n",
       "            LAND ROVER Range Rover Velar                           1         1  \n",
       "Electricity BMW 330e                                               1         1  \n",
       "            BMW 530e                                               2         2  \n",
       "            BMW 740e                                               1         1  \n",
       "            CHEVROLET Volt                                         2         2  \n",
       "            FORD Fusion Energi Plug-in Hybrid                      1         1  \n",
       "            KARMA Revero                                           1         1  \n",
       "            MINI Cooper SE Countryman All4                         1         1  \n",
       "            VOLVO S90                                              1         1  \n",
       "            VOLVO XC 60                                            1         1  \n",
       "            VOLVO XC 90                                            1         1  \n",
       "Ethanol     CHEVROLET Impala                                       1         1  \n",
       "            CHEVROLET Silverado 15                                 3         3  \n",
       "            CHEVROLET Suburban 1500                                2         2  \n",
       "            CHEVROLET Tahoe 1500                                   2         2  \n",
       "            CHRYSLER 300                                           2         2  \n",
       "            DODGE Charger                                          2         2  \n",
       "            GMC Sierra 15                                          3         3  \n",
       "            GMC Yukon 1500                                         2         2  \n",
       "            GMC Yukon 1500 XL                                      1         1  \n",
       "...                                                              ...       ...  \n",
       "Gasoline    TOYOTA RAV4                                            2         2  \n",
       "            TOYOTA RAV4 Hybrid                                     1         1  \n",
       "            TOYOTA RAV4 LE/XLE                                     1         1  \n",
       "            TOYOTA RAV4 Limited                                    1         1  \n",
       "            TOYOTA Sequoia                                         2         2  \n",
       "            TOYOTA Tundra                                          4         4  \n",
       "            TOYOTA Yaris                                           2         2  \n",
       "            VOLKSWAGEN Beetle                                      1         1  \n",
       "            VOLKSWAGEN Beetle Convertible                          1         1  \n",
       "            VOLKSWAGEN Beetle Dune                                 1         1  \n",
       "            VOLKSWAGEN Beetle Dune Convertible                     1         1  \n",
       "            VOLKSWAGEN Golf R                                      2         2  \n",
       "            VOLKSWAGEN Jetta                                       4         4  \n",
       "            VOLKSWAGEN Passat                                      1         1  \n",
       "            VOLKSWAGEN Tiguan                                      1         1  \n",
       "            VOLKSWAGEN Tiguan 4Motion                              1         1  \n",
       "            VOLVO Atlas                                            1         1  \n",
       "            VOLVO Atlas 4Motion                                    1         1  \n",
       "            VOLVO S60                                              3         3  \n",
       "            VOLVO S60 CC                                           1         1  \n",
       "            VOLVO S60 Inscription                                  2         2  \n",
       "            VOLVO S60 Polestar                                     1         1  \n",
       "            VOLVO S90                                              6         6  \n",
       "            VOLVO V60                                              3         3  \n",
       "            VOLVO V60 CC                                           1         1  \n",
       "            VOLVO V60 Polestar                                     1         1  \n",
       "            VOLVO V90                                              2         2  \n",
       "            VOLVO V90 CC                                           2         2  \n",
       "            VOLVO XC 60                                            3         3  \n",
       "            VOLVO XC 90                                            4         4  \n",
       "\n",
       "[408 rows x 11 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_18.groupby(['fuel','model']).count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\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>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",
       "    <tr>\n",
       "      <th>fuel</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CNG</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gasoline</th>\n",
       "      <td>984</td>\n",
       "      <td>984</td>\n",
       "      <td>984</td>\n",
       "      <td>984</td>\n",
       "      <td>984</td>\n",
       "      <td>984</td>\n",
       "      <td>984</td>\n",
       "      <td>984</td>\n",
       "      <td>984</td>\n",
       "      <td>984</td>\n",
       "      <td>984</td>\n",
       "      <td>984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ethanol</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gas</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          model  displ  cyl  trans  drive  veh_class  air_pollution_score  \\\n",
       "fuel                                                                        \n",
       "CNG           1      1    1      1      1          1                    1   \n",
       "Gasoline    984    984  984    984    984        984                  984   \n",
       "ethanol       1      1    1      1      1          1                    1   \n",
       "gas           1      1    1      1      1          1                    1   \n",
       "\n",
       "          city_mpg  hwy_mpg  cmb_mpg  greenhouse_gas_score  smartway  \n",
       "fuel                                                                  \n",
       "CNG              1        1        1                     1         1  \n",
       "Gasoline       984      984      984                   984       984  \n",
       "ethanol          1        1        1                     1         1  \n",
       "gas              1        1        1                     1         1  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_08.groupby('fuel').count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\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>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",
       "    <tr>\n",
       "      <th>fuel</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Diesel</th>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
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       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Electricity</th>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ethanol</th>\n",
       "      <td>26</td>\n",
       "      <td>26</td>\n",
       "      <td>26</td>\n",
       "      <td>26</td>\n",
       "      <td>26</td>\n",
       "      <td>26</td>\n",
       "      <td>26</td>\n",
       "      <td>26</td>\n",
       "      <td>26</td>\n",
       "      <td>26</td>\n",
       "      <td>26</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gas</th>\n",
       "      <td>26</td>\n",
       "      <td>26</td>\n",
       "      <td>26</td>\n",
       "      <td>26</td>\n",
       "      <td>26</td>\n",
       "      <td>26</td>\n",
       "      <td>26</td>\n",
       "      <td>26</td>\n",
       "      <td>26</td>\n",
       "      <td>26</td>\n",
       "      <td>26</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gasoline</th>\n",
       "      <td>749</td>\n",
       "      <td>749</td>\n",
       "      <td>749</td>\n",
       "      <td>749</td>\n",
       "      <td>749</td>\n",
       "      <td>749</td>\n",
       "      <td>749</td>\n",
       "      <td>749</td>\n",
       "      <td>749</td>\n",
       "      <td>749</td>\n",
       "      <td>749</td>\n",
       "      <td>749</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             model  displ  cyl  trans  drive  veh_class  air_pollution_score  \\\n",
       "fuel                                                                           \n",
       "Diesel          19     19   19     19     19         19                   19   \n",
       "Electricity     12     12   12     12     12         12                   12   \n",
       "Ethanol         26     26   26     26     26         26                   26   \n",
       "Gas             26     26   26     26     26         26                   26   \n",
       "Gasoline       749    749  749    749    749        749                  749   \n",
       "\n",
       "             city_mpg  hwy_mpg  cmb_mpg  greenhouse_gas_score  smartway  \n",
       "fuel                                                                     \n",
       "Diesel             19       19       19                    19        19  \n",
       "Electricity        12       12       12                    12        12  \n",
       "Ethanol            26       26       26                    26        26  \n",
       "Gas                26       26       26                    26        26  \n",
       "Gasoline          749      749      749                   749       749  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_18.groupby('fuel').count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa44cb01dd8>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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xMCIORMRl1fHTI+LuiHi4+vP51fGIiM9U78MDEXF2fytYmohYFRH/GRF3VvtnVreyeLi6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\nkgpkuEtSgQx3SSqQ4S5JBfo/1zWjegw4SZ8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa44cb985c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_08.groupby('fuel').count()['model'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa44cf13ac8>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa44d065358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_18.groupby('fuel').count()['model'].hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Thus, more unique models using alternative sources of fuel are available as : \n",
    "Ethanol models increased by 25, Gas by 25, Diesel and Electricity types added with 19 and 12 types each."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Q2: How much have vehicle classes improved in fuel economy?  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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",
       "    <tr>\n",
       "      <th>fuel</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CNG</th>\n",
       "      <td>1.800000</td>\n",
       "      <td>4.00000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>24.00000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>9.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gasoline</th>\n",
       "      <td>3.499797</td>\n",
       "      <td>6.02439</td>\n",
       "      <td>6.666667</td>\n",
       "      <td>17.38313</td>\n",
       "      <td>24.030488</td>\n",
       "      <td>19.783537</td>\n",
       "      <td>5.190041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ethanol</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>6.00000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>13.00000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gas</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>6.00000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>18.00000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             displ      cyl  air_pollution_score  city_mpg    hwy_mpg  \\\n",
       "fuel                                                                    \n",
       "CNG       1.800000  4.00000             9.000000  24.00000  36.000000   \n",
       "Gasoline  3.499797  6.02439             6.666667  17.38313  24.030488   \n",
       "ethanol   3.000000  6.00000             6.000000  13.00000  19.000000   \n",
       "gas       3.000000  6.00000             6.000000  18.00000  25.000000   \n",
       "\n",
       "            cmb_mpg  greenhouse_gas_score  \n",
       "fuel                                       \n",
       "CNG       28.000000              9.000000  \n",
       "Gasoline  19.783537              5.190041  \n",
       "ethanol   15.000000              7.000000  \n",
       "gas       21.000000              6.000000  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_08.groupby('fuel').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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",
       "    <tr>\n",
       "      <th>fuel</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Diesel</th>\n",
       "      <td>2.042105</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.368421</td>\n",
       "      <td>26.842105</td>\n",
       "      <td>37.421053</td>\n",
       "      <td>30.736842</td>\n",
       "      <td>5.736842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Electricity</th>\n",
       "      <td>1.875000</td>\n",
       "      <td>3.916667</td>\n",
       "      <td>5.166667</td>\n",
       "      <td>75.583333</td>\n",
       "      <td>74.666667</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>9.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ethanol</th>\n",
       "      <td>4.303846</td>\n",
       "      <td>6.615385</td>\n",
       "      <td>3.692308</td>\n",
       "      <td>12.538462</td>\n",
       "      <td>17.769231</td>\n",
       "      <td>14.538462</td>\n",
       "      <td>3.576923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gas</th>\n",
       "      <td>4.303846</td>\n",
       "      <td>6.615385</td>\n",
       "      <td>3.692308</td>\n",
       "      <td>17.500000</td>\n",
       "      <td>24.500000</td>\n",
       "      <td>20.115385</td>\n",
       "      <td>3.576923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gasoline</th>\n",
       "      <td>3.038318</td>\n",
       "      <td>5.473965</td>\n",
       "      <td>4.024032</td>\n",
       "      <td>20.779706</td>\n",
       "      <td>28.184246</td>\n",
       "      <td>23.508678</td>\n",
       "      <td>4.724967</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                displ       cyl  air_pollution_score   city_mpg    hwy_mpg  \\\n",
       "fuel                                                                         \n",
       "Diesel       2.042105  4.000000             2.368421  26.842105  37.421053   \n",
       "Electricity  1.875000  3.916667             5.166667  75.583333  74.666667   \n",
       "Ethanol      4.303846  6.615385             3.692308  12.538462  17.769231   \n",
       "Gas          4.303846  6.615385             3.692308  17.500000  24.500000   \n",
       "Gasoline     3.038318  5.473965             4.024032  20.779706  28.184246   \n",
       "\n",
       "               cmb_mpg  greenhouse_gas_score  \n",
       "fuel                                          \n",
       "Diesel       30.736842              5.736842  \n",
       "Electricity  75.000000              9.833333  \n",
       "Ethanol      14.538462              3.576923  \n",
       "Gas          20.115385              3.576923  \n",
       "Gasoline     23.508678              4.724967  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_18.groupby('fuel').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "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>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",
       "    <tr>\n",
       "      <th>veh_class</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>large car</th>\n",
       "      <td>3.193182</td>\n",
       "      <td>5.750000</td>\n",
       "      <td>3.954545</td>\n",
       "      <td>20.420455</td>\n",
       "      <td>28.795455</td>\n",
       "      <td>23.409091</td>\n",
       "      <td>4.727273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>midsize car</th>\n",
       "      <td>2.578986</td>\n",
       "      <td>4.739130</td>\n",
       "      <td>4.579710</td>\n",
       "      <td>24.862319</td>\n",
       "      <td>33.057971</td>\n",
       "      <td>27.884058</td>\n",
       "      <td>5.717391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>minivan</th>\n",
       "      <td>3.420000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>25.400000</td>\n",
       "      <td>20.800000</td>\n",
       "      <td>3.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pickup</th>\n",
       "      <td>4.243590</td>\n",
       "      <td>6.615385</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>16.551282</td>\n",
       "      <td>22.012821</td>\n",
       "      <td>18.589744</td>\n",
       "      <td>3.256410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>small SUV</th>\n",
       "      <td>2.391667</td>\n",
       "      <td>4.462963</td>\n",
       "      <td>4.296296</td>\n",
       "      <td>21.666667</td>\n",
       "      <td>27.879630</td>\n",
       "      <td>24.074074</td>\n",
       "      <td>4.870370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>small car</th>\n",
       "      <td>2.873333</td>\n",
       "      <td>5.498246</td>\n",
       "      <td>3.761404</td>\n",
       "      <td>22.561404</td>\n",
       "      <td>30.435088</td>\n",
       "      <td>25.421053</td>\n",
       "      <td>5.066667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>special purpose</th>\n",
       "      <td>3.950000</td>\n",
       "      <td>6.200000</td>\n",
       "      <td>3.400000</td>\n",
       "      <td>16.600000</td>\n",
       "      <td>21.300000</td>\n",
       "      <td>18.500000</td>\n",
       "      <td>3.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>standard SUV</th>\n",
       "      <td>4.588372</td>\n",
       "      <td>7.093023</td>\n",
       "      <td>3.348837</td>\n",
       "      <td>16.011628</td>\n",
       "      <td>21.953488</td>\n",
       "      <td>18.197674</td>\n",
       "      <td>3.162791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>station wagon</th>\n",
       "      <td>1.897059</td>\n",
       "      <td>4.117647</td>\n",
       "      <td>4.352941</td>\n",
       "      <td>24.705882</td>\n",
       "      <td>32.058824</td>\n",
       "      <td>27.529412</td>\n",
       "      <td>5.852941</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    displ       cyl  air_pollution_score   city_mpg  \\\n",
       "veh_class                                                             \n",
       "large car        3.193182  5.750000             3.954545  20.420455   \n",
       "midsize car      2.578986  4.739130             4.579710  24.862319   \n",
       "minivan          3.420000  6.000000             3.000000  18.000000   \n",
       "pickup           4.243590  6.615385             4.000000  16.551282   \n",
       "small SUV        2.391667  4.462963             4.296296  21.666667   \n",
       "small car        2.873333  5.498246             3.761404  22.561404   \n",
       "special purpose  3.950000  6.200000             3.400000  16.600000   \n",
       "standard SUV     4.588372  7.093023             3.348837  16.011628   \n",
       "station wagon    1.897059  4.117647             4.352941  24.705882   \n",
       "\n",
       "                   hwy_mpg    cmb_mpg  greenhouse_gas_score  \n",
       "veh_class                                                    \n",
       "large car        28.795455  23.409091              4.727273  \n",
       "midsize car      33.057971  27.884058              5.717391  \n",
       "minivan          25.400000  20.800000              3.800000  \n",
       "pickup           22.012821  18.589744              3.256410  \n",
       "small SUV        27.879630  24.074074              4.870370  \n",
       "small car        30.435088  25.421053              5.066667  \n",
       "special purpose  21.300000  18.500000              3.300000  \n",
       "standard SUV     21.953488  18.197674              3.162791  \n",
       "station wagon    32.058824  27.529412              5.852941  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_18.groupby('veh_class').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>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",
       "    <tr>\n",
       "      <th>veh_class</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SUV</th>\n",
       "      <td>3.680714</td>\n",
       "      <td>6.028571</td>\n",
       "      <td>6.621429</td>\n",
       "      <td>16.507143</td>\n",
       "      <td>21.935714</td>\n",
       "      <td>18.471429</td>\n",
       "      <td>4.675000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>large car</th>\n",
       "      <td>4.227273</td>\n",
       "      <td>7.272727</td>\n",
       "      <td>6.836364</td>\n",
       "      <td>15.836364</td>\n",
       "      <td>23.345455</td>\n",
       "      <td>18.509091</td>\n",
       "      <td>4.618182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>midsize car</th>\n",
       "      <td>3.323188</td>\n",
       "      <td>5.855072</td>\n",
       "      <td>6.826087</td>\n",
       "      <td>18.826087</td>\n",
       "      <td>26.565217</td>\n",
       "      <td>21.601449</td>\n",
       "      <td>5.978261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>minivan</th>\n",
       "      <td>3.511765</td>\n",
       "      <td>5.764706</td>\n",
       "      <td>6.764706</td>\n",
       "      <td>16.882353</td>\n",
       "      <td>23.705882</td>\n",
       "      <td>19.117647</td>\n",
       "      <td>5.294118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pickup</th>\n",
       "      <td>4.166265</td>\n",
       "      <td>6.506024</td>\n",
       "      <td>6.506024</td>\n",
       "      <td>14.578313</td>\n",
       "      <td>19.204819</td>\n",
       "      <td>16.277108</td>\n",
       "      <td>3.650602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>small car</th>\n",
       "      <td>3.195796</td>\n",
       "      <td>5.882883</td>\n",
       "      <td>6.711712</td>\n",
       "      <td>18.372372</td>\n",
       "      <td>25.963964</td>\n",
       "      <td>21.105105</td>\n",
       "      <td>5.738739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>station wagon</th>\n",
       "      <td>2.548333</td>\n",
       "      <td>4.850000</td>\n",
       "      <td>6.550000</td>\n",
       "      <td>19.633333</td>\n",
       "      <td>27.116667</td>\n",
       "      <td>22.366667</td>\n",
       "      <td>6.316667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>van</th>\n",
       "      <td>5.109524</td>\n",
       "      <td>7.619048</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>13.142857</td>\n",
       "      <td>17.333333</td>\n",
       "      <td>14.952381</td>\n",
       "      <td>2.761905</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  displ       cyl  air_pollution_score   city_mpg    hwy_mpg  \\\n",
       "veh_class                                                                      \n",
       "SUV            3.680714  6.028571             6.621429  16.507143  21.935714   \n",
       "large car      4.227273  7.272727             6.836364  15.836364  23.345455   \n",
       "midsize car    3.323188  5.855072             6.826087  18.826087  26.565217   \n",
       "minivan        3.511765  5.764706             6.764706  16.882353  23.705882   \n",
       "pickup         4.166265  6.506024             6.506024  14.578313  19.204819   \n",
       "small car      3.195796  5.882883             6.711712  18.372372  25.963964   \n",
       "station wagon  2.548333  4.850000             6.550000  19.633333  27.116667   \n",
       "van            5.109524  7.619048             6.000000  13.142857  17.333333   \n",
       "\n",
       "                 cmb_mpg  greenhouse_gas_score  \n",
       "veh_class                                       \n",
       "SUV            18.471429              4.675000  \n",
       "large car      18.509091              4.618182  \n",
       "midsize car    21.601449              5.978261  \n",
       "minivan        19.117647              5.294118  \n",
       "pickup         16.277108              3.650602  \n",
       "small car      21.105105              5.738739  \n",
       "station wagon  22.366667              6.316667  \n",
       "van            14.952381              2.761905  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_08.groupby('veh_class').mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vehicle classes improved in fuel economy (increased in mpg) as under:\n",
    "- Large Car 18.509091 to 23.409091\n",
    "- Mid size car 21.601449 to 27.884058\n",
    "- Minivan 19.117647 to 20.800000\n",
    "- Pickup 16.277108 to 18.589744\n",
    "- Small car 21.105105 to 25.421053\n",
    "- SUV 18.471429 to 18.197674 for standard SUV and 24.074074 for small SUV. Overall, improved.\n",
    "- Station wagon 22.366667 to 27.529412\n",
    "This is also seen in teh histograms below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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MrBAnDDMzK8QJw8zMCnHCMDOzQpwwzMyskFIThqSJkh6StFDS9CrLvyjpfkn3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\nhGFmZoX8fzl1e4ZSDDbQAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa44c368d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_08.groupby('veh_class').mean()['cmb_mpg'].hist()\n",
    "plt.xlabel('Vehicle class')\n",
    "plt.ylabel('Combined mpg')\n",
    "plt.title('Vehicle Class vs Combined MPG');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AcFka/h+yHlvbjqRVyL4gF0TEYHsfl7Remr8e0Ha70lC17Ug6ANgd2DfSgdZ2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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa44c390550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_18.groupby('veh_class').mean()['cmb_mpg'].hist()\n",
    "plt.xlabel('Vehicle class')\n",
    "plt.ylabel('Combined mpg')\n",
    "plt.title('Vehicle Class vs Combined MPG');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Q3: What are the characteristics of SmartWay vehicles? Have they changed over time?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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",
       "    <tr>\n",
       "      <th>smartway</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>no</th>\n",
       "      <td>4.056837</td>\n",
       "      <td>6.771005</td>\n",
       "      <td>6.291598</td>\n",
       "      <td>15.133443</td>\n",
       "      <td>21.299835</td>\n",
       "      <td>17.316310</td>\n",
       "      <td>4.149918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yes</th>\n",
       "      <td>2.602895</td>\n",
       "      <td>4.826316</td>\n",
       "      <td>7.268421</td>\n",
       "      <td>20.984211</td>\n",
       "      <td>28.413158</td>\n",
       "      <td>23.736842</td>\n",
       "      <td>6.868421</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             displ       cyl  air_pollution_score   city_mpg    hwy_mpg  \\\n",
       "smartway                                                                  \n",
       "no        4.056837  6.771005             6.291598  15.133443  21.299835   \n",
       "yes       2.602895  4.826316             7.268421  20.984211  28.413158   \n",
       "\n",
       "            cmb_mpg  greenhouse_gas_score  \n",
       "smartway                                   \n",
       "no        17.316310              4.149918  \n",
       "yes       23.736842              6.868421  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_08.groupby('smartway').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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",
       "    <tr>\n",
       "      <th>smartway</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Elite</th>\n",
       "      <td>1.970588</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>53.235294</td>\n",
       "      <td>53.117647</td>\n",
       "      <td>53.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No</th>\n",
       "      <td>3.270304</td>\n",
       "      <td>5.720994</td>\n",
       "      <td>3.798343</td>\n",
       "      <td>19.325967</td>\n",
       "      <td>26.708564</td>\n",
       "      <td>22.041436</td>\n",
       "      <td>4.276243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>1.753846</td>\n",
       "      <td>3.923077</td>\n",
       "      <td>4.879121</td>\n",
       "      <td>31.483516</td>\n",
       "      <td>39.296703</td>\n",
       "      <td>34.439560</td>\n",
       "      <td>7.538462</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             displ       cyl  air_pollution_score   city_mpg    hwy_mpg  \\\n",
       "smartway                                                                  \n",
       "Elite     1.970588  4.000000             7.000000  53.235294  53.117647   \n",
       "No        3.270304  5.720994             3.798343  19.325967  26.708564   \n",
       "Yes       1.753846  3.923077             4.879121  31.483516  39.296703   \n",
       "\n",
       "            cmb_mpg  greenhouse_gas_score  \n",
       "smartway                                   \n",
       "Elite     53.000000             10.000000  \n",
       "No        22.041436              4.276243  \n",
       "Yes       34.439560              7.538462  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_18.groupby('smartway').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa44c0dd908>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ReHlVPZxkO/Nr8Sf03Ib7AEmeAvwEsBOg98W72BfwFcBfAS9aseaWUce5fwH4WFXd26t5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\n/UmnAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa44c0fa518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_08.groupby('smartway').mean()['air_pollution_score'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa44beaf1d0>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa44bf52400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_18.groupby('smartway').mean()['air_pollution_score'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa44c0750f0>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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YjfVsqfkBvCjJ55P8U5KfWc3mVugnga8Af91bZnxPkvMHaib5/A0zP5jc89fvAPDBefYv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\nGPaS1ADDXpIaYNhLUgP+DzOoFk4X1QKAAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa44c088550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_08.groupby('smartway').mean()['greenhouse_gas_score'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa44a158828>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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m/vBhVf9RQ5LTgHcwc4f8K4A3Jtk23K6W5Dpg55x9e4FPV9U24NOd7YEbSrgn2QL8ArB/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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa44a0a5da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_18.groupby('smartway').mean()['greenhouse_gas_score'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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RzkL2lTeAJ61jiEgiLUWF3HS3WwcQkUS6F19J5UnlaS7k31kHEJFESu1gLc2FfD/whnUI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\nCllEJCH+Hy5tq/nPTO7RAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa44c3560f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggs_08= df_08.groupby('smartway').mean()['greenhouse_gas_score']\n",
    "ggs_08_label= ['No', 'Yes']\n",
    "\n",
    "plt.pie(ggs_08, labels=ggs_08_label);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWQAAADuCAYAAAAOR30qAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa44c34d5c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggs_18= df_18.groupby('smartway').mean()['greenhouse_gas_score']\n",
    "ggs_18_label= ['Elite','No', 'Yes']\n",
    "\n",
    "plt.pie(ggs_18, labels=ggs_18_label);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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fXfwCeFlwFBEZvbXAMe6+JDpIq5R2hQw7Rxfnkj2FVkSK7bwylzGUvJBh5x18H43OISKj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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa44a15cb70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "aps_08= df_08.groupby('smartway').mean()['air_pollution_score']\n",
    "aps_08_label= ['No', 'Yes']\n",
    "\n",
    "plt.pie(aps_08, labels=aps_08_label, colors='gk');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa44beb8da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "aps_18= df_18.groupby('smartway').mean()['air_pollution_score']\n",
    "aps_18_label= ['Elite','No', 'Yes']\n",
    "\n",
    "plt.pie(aps_18, labels=aps_18_label,colors='rgk');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Q3: What are the characteristics of SmartWay vehicles? Have they changed over time? (mpg, greenhouse gas)**\n",
    "- Characteristics are Air pollution score and Greenhouse Gas Score\n",
    "- These have improved from 2008 to 2018.\n",
    "- Air pollution scores reduced and Greenhouse scores increased.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Q4: What features are associated with better fuel economy?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>displ</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.899304</td>\n",
       "      <td>-0.296241</td>\n",
       "      <td>-0.790440</td>\n",
       "      <td>-0.801678</td>\n",
       "      <td>-0.818799</td>\n",
       "      <td>-0.847613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cyl</th>\n",
       "      <td>0.899304</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.274657</td>\n",
       "      <td>-0.756304</td>\n",
       "      <td>-0.733064</td>\n",
       "      <td>-0.770538</td>\n",
       "      <td>-0.820940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>air_pollution_score</th>\n",
       "      <td>-0.296241</td>\n",
       "      <td>-0.274657</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.434884</td>\n",
       "      <td>0.402069</td>\n",
       "      <td>0.428317</td>\n",
       "      <td>0.394814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>city_mpg</th>\n",
       "      <td>-0.790440</td>\n",
       "      <td>-0.756304</td>\n",
       "      <td>0.434884</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.902942</td>\n",
       "      <td>0.980891</td>\n",
       "      <td>0.910693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hwy_mpg</th>\n",
       "      <td>-0.801678</td>\n",
       "      <td>-0.733064</td>\n",
       "      <td>0.402069</td>\n",
       "      <td>0.902942</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.960533</td>\n",
       "      <td>0.931744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cmb_mpg</th>\n",
       "      <td>-0.818799</td>\n",
       "      <td>-0.770538</td>\n",
       "      <td>0.428317</td>\n",
       "      <td>0.980891</td>\n",
       "      <td>0.960533</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.947607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>greenhouse_gas_score</th>\n",
       "      <td>-0.847613</td>\n",
       "      <td>-0.820940</td>\n",
       "      <td>0.394814</td>\n",
       "      <td>0.910693</td>\n",
       "      <td>0.931744</td>\n",
       "      <td>0.947607</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         displ       cyl  air_pollution_score  city_mpg  \\\n",
       "displ                 1.000000  0.899304            -0.296241 -0.790440   \n",
       "cyl                   0.899304  1.000000            -0.274657 -0.756304   \n",
       "air_pollution_score  -0.296241 -0.274657             1.000000  0.434884   \n",
       "city_mpg             -0.790440 -0.756304             0.434884  1.000000   \n",
       "hwy_mpg              -0.801678 -0.733064             0.402069  0.902942   \n",
       "cmb_mpg              -0.818799 -0.770538             0.428317  0.980891   \n",
       "greenhouse_gas_score -0.847613 -0.820940             0.394814  0.910693   \n",
       "\n",
       "                       hwy_mpg   cmb_mpg  greenhouse_gas_score  \n",
       "displ                -0.801678 -0.818799             -0.847613  \n",
       "cyl                  -0.733064 -0.770538             -0.820940  \n",
       "air_pollution_score   0.402069  0.428317              0.394814  \n",
       "city_mpg              0.902942  0.980891              0.910693  \n",
       "hwy_mpg               1.000000  0.960533              0.931744  \n",
       "cmb_mpg               0.960533  1.000000              0.947607  \n",
       "greenhouse_gas_score  0.931744  0.947607              1.000000  "
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_08.corr('pearson')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "According to the 2008 dataset, we see that better fuel economy is associated with the following factors as [using correlation]:\n",
    "- Negatively with displ : -0.818799\n",
    "- Negatively with cyl : -0.770538\n",
    "- Positively with Greenhouse gas scores : 0.947607"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>displ</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.928189</td>\n",
       "      <td>-0.384986</td>\n",
       "      <td>-0.536748</td>\n",
       "      <td>-0.605202</td>\n",
       "      <td>-0.574880</td>\n",
       "      <td>-0.754193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cyl</th>\n",
       "      <td>0.928189</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.404966</td>\n",
       "      <td>-0.524090</td>\n",
       "      <td>-0.589986</td>\n",
       "      <td>-0.561166</td>\n",
       "      <td>-0.743946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>air_pollution_score</th>\n",
       "      <td>-0.384986</td>\n",
       "      <td>-0.404966</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.288700</td>\n",
       "      <td>0.309338</td>\n",
       "      <td>0.301150</td>\n",
       "      <td>0.390771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>city_mpg</th>\n",
       "      <td>-0.536748</td>\n",
       "      <td>-0.524090</td>\n",
       "      <td>0.288700</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.950309</td>\n",
       "      <td>0.992635</td>\n",
       "      <td>0.785513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hwy_mpg</th>\n",
       "      <td>-0.605202</td>\n",
       "      <td>-0.589986</td>\n",
       "      <td>0.309338</td>\n",
       "      <td>0.950309</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.978206</td>\n",
       "      <td>0.826981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cmb_mpg</th>\n",
       "      <td>-0.574880</td>\n",
       "      <td>-0.561166</td>\n",
       "      <td>0.301150</td>\n",
       "      <td>0.992635</td>\n",
       "      <td>0.978206</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.814982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>greenhouse_gas_score</th>\n",
       "      <td>-0.754193</td>\n",
       "      <td>-0.743946</td>\n",
       "      <td>0.390771</td>\n",
       "      <td>0.785513</td>\n",
       "      <td>0.826981</td>\n",
       "      <td>0.814982</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         displ       cyl  air_pollution_score  city_mpg  \\\n",
       "displ                 1.000000  0.928189            -0.384986 -0.536748   \n",
       "cyl                   0.928189  1.000000            -0.404966 -0.524090   \n",
       "air_pollution_score  -0.384986 -0.404966             1.000000  0.288700   \n",
       "city_mpg             -0.536748 -0.524090             0.288700  1.000000   \n",
       "hwy_mpg              -0.605202 -0.589986             0.309338  0.950309   \n",
       "cmb_mpg              -0.574880 -0.561166             0.301150  0.992635   \n",
       "greenhouse_gas_score -0.754193 -0.743946             0.390771  0.785513   \n",
       "\n",
       "                       hwy_mpg   cmb_mpg  greenhouse_gas_score  \n",
       "displ                -0.605202 -0.574880             -0.754193  \n",
       "cyl                  -0.589986 -0.561166             -0.743946  \n",
       "air_pollution_score   0.309338  0.301150              0.390771  \n",
       "city_mpg              0.950309  0.992635              0.785513  \n",
       "hwy_mpg               1.000000  0.978206              0.826981  \n",
       "cmb_mpg               0.978206  1.000000              0.814982  \n",
       "greenhouse_gas_score  0.826981  0.814982              1.000000  "
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_18.corr('pearson')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "According to the 2018 dataset, we see that better fuel economy is associated with the following factors as [using correlation]:\n",
    "- Positively with Greenhouse gas scores : 0.814982"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on both the datasets, we can associate Greenhouse Gas Scores as being positively associated with Fuel Economy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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