drawing-conclusions-solutions.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": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "% matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load datasets\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": [
    {
     "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>model</th>\n",
       "      <th>displ</th>\n",
       "      <th>cyl</th>\n",
       "      <th>trans</th>\n",
       "      <th>drive</th>\n",
       "      <th>fuel</th>\n",
       "      <th>veh_class</th>\n",
       "      <th>air_pollution_score</th>\n",
       "      <th>city_mpg</th>\n",
       "      <th>hwy_mpg</th>\n",
       "      <th>cmb_mpg</th>\n",
       "      <th>greenhouse_gas_score</th>\n",
       "      <th>smartway</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ACURA MDX</td>\n",
       "      <td>3.7</td>\n",
       "      <td>6</td>\n",
       "      <td>Auto-S5</td>\n",
       "      <td>4WD</td>\n",
       "      <td>Gasoline</td>\n",
       "      <td>SUV</td>\n",
       "      <td>7.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>4</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       model  displ  cyl    trans drive      fuel veh_class  \\\n",
       "0  ACURA MDX    3.7    6  Auto-S5   4WD  Gasoline       SUV   \n",
       "\n",
       "   air_pollution_score  city_mpg  hwy_mpg  cmb_mpg  greenhouse_gas_score  \\\n",
       "0                  7.0      15.0     20.0     17.0                     4   \n",
       "\n",
       "  smartway  \n",
       "0       no  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_08.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Q1: Are more unique models using alternative sources of fuel? By how much?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's first look at what the sources of fuel are and which ones are alternative sources."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gasoline    984\n",
       "gas           1\n",
       "ethanol       1\n",
       "CNG           1\n",
       "Name: fuel, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_08.fuel.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gasoline       749\n",
       "Gas             26\n",
       "Ethanol         26\n",
       "Diesel          19\n",
       "Electricity     12\n",
       "Name: fuel, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_18.fuel.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks like the alternative sources of fuel available in 2008 are CNG and ethanol, and those in 2018 ethanol and electricity. (You can use Google if you weren't sure which ones are alternative sources of fuel!)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# how many unique models used alternative sources of fuel in 2008\n",
    "alt_08 = df_08.query('fuel in [\"CNG\", \"ethanol\"]').model.nunique()\n",
    "alt_08"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "26"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# how many unique models used alternative sources of fuel in 2018\n",
    "alt_18 = df_18.query('fuel in [\"Ethanol\", \"Electricity\"]').model.nunique()\n",
    "alt_18"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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+K3LU0LeB3YEnASLiHmDHAuMtAY6NiLcC2wJHSNoMmAjcEBFjgRvyezMz65JCl5iIiFlN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\nM7OacyIwM6s5JwIzs5pzIjAzq7n/Ax2YZmEZpTjPAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f73c4f7d1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar([\"2008\", \"2018\"], [alt_08, alt_18])\n",
    "plt.title(\"Number of Unique Models Using Alternative Fuels\")\n",
    "plt.xlabel(\"Year\")\n",
    "plt.ylabel(\"Number of Unique Models\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since 2008, the number of unique models using alternative sources of fuel increased by 24. We can also look at proportions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(377, 357)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# total unique models each year\n",
    "total_08 = df_08.model.nunique()\n",
    "total_18 = df_18.model.nunique()\n",
    "total_08, total_18"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.005305039787798408, 0.07282913165266107)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prop_08 = alt_08/total_08\n",
    "prop_18 = alt_18/total_18\n",
    "prop_08, prop_18"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Snk3VNdXyGIntc2zPtD1z0qRJNUKKiIjhqJMsLpE0W9IG5fVK4Ns1plsBTGkY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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f73c4f7df98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar([\"2008\", \"2018\"], [prop_08, prop_18])\n",
    "plt.title(\"Proportion of Unique Models Using Alternative Fuels\")\n",
    "plt.xlabel(\"Year\")\n",
    "plt.ylabel(\"Proportion of Unique Models\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Q2: How much have vehicle classes improved in fuel economy?  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at the average fuel economy for each vehicle class for both years."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "veh_class\n",
       "SUV              18.471429\n",
       "large car        18.509091\n",
       "midsize car      21.601449\n",
       "minivan          19.117647\n",
       "pickup           16.277108\n",
       "small car        21.105105\n",
       "station wagon    22.366667\n",
       "van              14.952381\n",
       "Name: cmb_mpg, dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "veh_08 = df_08.groupby('veh_class').cmb_mpg.mean()\n",
    "veh_08"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "veh_class\n",
       "large car          23.409091\n",
       "midsize car        27.884058\n",
       "minivan            20.800000\n",
       "pickup             18.589744\n",
       "small SUV          24.074074\n",
       "small car          25.421053\n",
       "special purpose    18.500000\n",
       "standard SUV       18.197674\n",
       "station wagon      27.529412\n",
       "Name: cmb_mpg, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "veh_18 = df_18.groupby('veh_class').cmb_mpg.mean()\n",
    "veh_18"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "veh_class\n",
       "SUV                     NaN\n",
       "large car          4.900000\n",
       "midsize car        6.282609\n",
       "minivan            1.682353\n",
       "pickup             2.312635\n",
       "small SUV               NaN\n",
       "small car          4.315948\n",
       "special purpose         NaN\n",
       "standard SUV            NaN\n",
       "station wagon      5.162745\n",
       "van                     NaN\n",
       "Name: cmb_mpg, dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# how much they've increased by for each vehicle class\n",
    "inc = veh_18 - veh_08\n",
    "inc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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e9X7dCMTM6qnn8PMGO4RKzTp6p8EOwaxXnT6R+cWIOEbST0g16UVExKcrjczM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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f73c2e5c0f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# only plot the classes that exist in both years\n",
    "inc.dropna(inplace=True)\n",
    "plt.subplots(figsize=(8, 5))\n",
    "plt.bar(inc.index, inc)\n",
    "plt.title('Improvements in Fuel Economy from 2008 to 2018 by Vehicle Class')\n",
    "plt.xlabel('Vehicle Class')\n",
    "plt.ylabel('Increase in Average Combined MPG');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Q3: What are the characteristics of SmartWay vehicles? Have they changed over time?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can analyze this by filtering each dataframe by SmartWay classification and exploring these datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['no', 'yes'], dtype=object)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# smartway labels for 2008\n",
    "df_08.smartway.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get all smartway vehicles in 2008\n",
    "smart_08 = df_08.query('smartway == \"yes\"')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>count</th>\n",
       "      <td>380.000000</td>\n",
       "      <td>380.000000</td>\n",
       "      <td>380.000000</td>\n",
       "      <td>380.000000</td>\n",
       "      <td>380.000000</td>\n",
       "      <td>380.000000</td>\n",
       "      <td>380.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.602895</td>\n",
       "      <td>4.826316</td>\n",
       "      <td>7.365789</td>\n",
       "      <td>20.984211</td>\n",
       "      <td>28.413158</td>\n",
       "      <td>23.736842</td>\n",
       "      <td>6.868421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.623436</td>\n",
       "      <td>1.002025</td>\n",
       "      <td>1.148195</td>\n",
       "      <td>3.442672</td>\n",
       "      <td>3.075194</td>\n",
       "      <td>3.060379</td>\n",
       "      <td>0.827338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.300000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.275000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.400000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>9.500000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            displ         cyl  air_pollution_score    city_mpg     hwy_mpg  \\\n",
       "count  380.000000  380.000000           380.000000  380.000000  380.000000   \n",
       "mean     2.602895    4.826316             7.365789   20.984211   28.413158   \n",
       "std      0.623436    1.002025             1.148195    3.442672    3.075194   \n",
       "min      1.300000    4.000000             6.000000   17.000000   22.000000   \n",
       "25%      2.275000    4.000000             7.000000   19.000000   26.000000   \n",
       "50%      2.400000    4.000000             7.000000   20.000000   28.000000   \n",
       "75%      3.000000    6.000000             7.000000   22.000000   30.000000   \n",
       "max      5.000000    8.000000             9.500000   48.000000   45.000000   \n",
       "\n",
       "          cmb_mpg  greenhouse_gas_score  \n",
       "count  380.000000            380.000000  \n",
       "mean    23.736842              6.868421  \n",
       "std      3.060379              0.827338  \n",
       "min     20.000000              6.000000  \n",
       "25%     22.000000              6.000000  \n",
       "50%     23.000000              7.000000  \n",
       "75%     25.000000              7.000000  \n",
       "max     46.000000             10.000000  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# explore smartway vehicles in 2008\n",
    "smart_08.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use what you've learned so for to further explore this dataset on 2008 smartway vehicles."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['No', 'Yes', 'Elite'], dtype=object)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# smartway labels for 2018\n",
    "df_18.smartway.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get all smartway vehicles in 2018\n",
    "smart_18 = df_18.query('smartway in [\"Yes\", \"Elite\"]')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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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",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>108.000000</td>\n",
       "      <td>108.000000</td>\n",
       "      <td>108.000000</td>\n",
       "      <td>108.000000</td>\n",
       "      <td>108.000000</td>\n",
       "      <td>108.000000</td>\n",
       "      <td>108.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.787963</td>\n",
       "      <td>3.935185</td>\n",
       "      <td>5.212963</td>\n",
       "      <td>34.907407</td>\n",
       "      <td>41.472222</td>\n",
       "      <td>37.361111</td>\n",
       "      <td>7.925926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.408031</td>\n",
       "      <td>0.416329</td>\n",
       "      <td>1.798498</td>\n",
       "      <td>16.431982</td>\n",
       "      <td>13.095236</td>\n",
       "      <td>14.848429</td>\n",
       "      <td>1.197378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.200000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.500000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.700000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.500000</td>\n",
       "      <td>28.500000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>31.250000</td>\n",
       "      <td>40.250000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>9.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.500000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>113.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>106.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            displ         cyl  air_pollution_score    city_mpg     hwy_mpg  \\\n",
       "count  108.000000  108.000000           108.000000  108.000000  108.000000   \n",
       "mean     1.787963    3.935185             5.212963   34.907407   41.472222   \n",
       "std      0.408031    0.416329             1.798498   16.431982   13.095236   \n",
       "min      1.200000    3.000000             3.000000   25.000000   27.000000   \n",
       "25%      1.500000    4.000000             3.000000   28.000000   36.000000   \n",
       "50%      1.700000    4.000000             5.500000   28.500000   37.000000   \n",
       "75%      2.000000    4.000000             7.000000   31.250000   40.250000   \n",
       "max      3.500000    6.000000             7.000000  113.000000   99.000000   \n",
       "\n",
       "          cmb_mpg  greenhouse_gas_score  \n",
       "count  108.000000            108.000000  \n",
       "mean    37.361111              7.925926  \n",
       "std     14.848429              1.197378  \n",
       "min     26.000000              7.000000  \n",
       "25%     31.000000              7.000000  \n",
       "50%     32.000000              7.000000  \n",
       "75%     35.000000              9.000000  \n",
       "max    106.000000             10.000000  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "smart_18.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use what you've learned so for to further explore this dataset on 2018 smartway vehicles."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Q4: What features are associated with better fuel economy?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can explore trends between cmb_mpg and the other features in this dataset, or filter this dataset like in the previous question and explore the properties of that dataset. For example, you can select all vehicles that have the top 50% fuel economy ratings like this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>519.000000</td>\n",
       "      <td>519.000000</td>\n",
       "      <td>519.000000</td>\n",
       "      <td>519.000000</td>\n",
       "      <td>519.000000</td>\n",
       "      <td>519.000000</td>\n",
       "      <td>519.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.667823</td>\n",
       "      <td>4.890173</td>\n",
       "      <td>6.998073</td>\n",
       "      <td>20.317919</td>\n",
       "      <td>27.603083</td>\n",
       "      <td>22.992293</td>\n",
       "      <td>6.639692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.665551</td>\n",
       "      <td>1.034856</td>\n",
       "      <td>1.159565</td>\n",
       "      <td>3.198257</td>\n",
       "      <td>3.051120</td>\n",
       "      <td>2.926371</td>\n",
       "      <td>0.804935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.300000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.300000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.500000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>9.500000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            displ         cyl  air_pollution_score    city_mpg     hwy_mpg  \\\n",
       "count  519.000000  519.000000           519.000000  519.000000  519.000000   \n",
       "mean     2.667823    4.890173             6.998073   20.317919   27.603083   \n",
       "std      0.665551    1.034856             1.159565    3.198257    3.051120   \n",
       "min      1.300000    4.000000             4.000000   17.000000   20.000000   \n",
       "25%      2.300000    4.000000             6.000000   18.000000   25.000000   \n",
       "50%      2.500000    4.000000             7.000000   20.000000   27.000000   \n",
       "75%      3.000000    6.000000             7.000000   21.000000   29.000000   \n",
       "max      6.000000    8.000000             9.500000   48.000000   45.000000   \n",
       "\n",
       "          cmb_mpg  greenhouse_gas_score  \n",
       "count  519.000000            519.000000  \n",
       "mean    22.992293              6.639692  \n",
       "std      2.926371              0.804935  \n",
       "min     20.000000              6.000000  \n",
       "25%     21.000000              6.000000  \n",
       "50%     22.000000              6.000000  \n",
       "75%     24.000000              7.000000  \n",
       "max     46.000000             10.000000  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_08 = df_08.query('cmb_mpg > cmb_mpg.mean()')\n",
    "top_08.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>count</th>\n",
       "      <td>328.000000</td>\n",
       "      <td>328.000000</td>\n",
       "      <td>328.000000</td>\n",
       "      <td>328.000000</td>\n",
       "      <td>328.000000</td>\n",
       "      <td>328.000000</td>\n",
       "      <td>328.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.964329</td>\n",
       "      <td>4.021341</td>\n",
       "      <td>4.856707</td>\n",
       "      <td>27.472561</td>\n",
       "      <td>35.304878</td>\n",
       "      <td>30.411585</td>\n",
       "      <td>6.329268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.398593</td>\n",
       "      <td>0.465477</td>\n",
       "      <td>1.860802</td>\n",
       "      <td>11.033692</td>\n",
       "      <td>9.024857</td>\n",
       "      <td>10.081539</td>\n",
       "      <td>1.410358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.200000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.600000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>7.000000</td>\n",
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       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.500000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>113.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>106.000000</td>\n",
       "      <td>10.000000</td>\n",
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       "  </tbody>\n",
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      ],
      "text/plain": [
       "            displ         cyl  air_pollution_score    city_mpg     hwy_mpg  \\\n",
       "count  328.000000  328.000000           328.000000  328.000000  328.000000   \n",
       "mean     1.964329    4.021341             4.856707   27.472561   35.304878   \n",
       "std      0.398593    0.465477             1.860802   11.033692    9.024857   \n",
       "min      1.200000    3.000000             1.000000   21.000000   27.000000   \n",
       "25%      1.600000    4.000000             3.000000   23.000000   31.000000   \n",
       "50%      2.000000    4.000000             5.000000   25.000000   33.000000   \n",
       "75%      2.000000    4.000000             7.000000   28.000000   36.000000   \n",
       "max      3.500000    6.000000             7.000000  113.000000   99.000000   \n",
       "\n",
       "          cmb_mpg  greenhouse_gas_score  \n",
       "count  328.000000            328.000000  \n",
       "mean    30.411585              6.329268  \n",
       "std     10.081539              1.410358  \n",
       "min     25.000000              4.000000  \n",
       "25%     26.000000              5.000000  \n",
       "50%     28.000000              6.000000  \n",
       "75%     31.000000              7.000000  \n",
       "max    106.000000             10.000000  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_18 = df_18.query('cmb_mpg > cmb_mpg.mean()')\n",
    "top_18.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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