{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# load le dataset\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "filename = '../data/cars93.csv'\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv(filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(93, 27)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Manufacturer', 'Model', 'Type', 'Min.Price', 'Price', 'Max.Price',\n",
       "       'MPG.city', 'MPG.highway', 'AirBags', 'DriveTrain', 'Cylinders',\n",
       "       'EngineSize', 'Horsepower', 'RPM', 'Rev.per.mile', 'Man.trans.avail',\n",
       "       'Fuel.tank.capacity', 'Passengers', 'Length', 'Wheelbase', 'Width',\n",
       "       'Turn.circle', 'Rear.seat.room', 'Luggage.room', 'Weight', 'Origin',\n",
       "       'Make'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Manufacturer</th>\n",
       "      <th>Model</th>\n",
       "      <th>Type</th>\n",
       "      <th>Min.Price</th>\n",
       "      <th>Price</th>\n",
       "      <th>Max.Price</th>\n",
       "      <th>MPG.city</th>\n",
       "      <th>MPG.highway</th>\n",
       "      <th>AirBags</th>\n",
       "      <th>DriveTrain</th>\n",
       "      <th>...</th>\n",
       "      <th>Passengers</th>\n",
       "      <th>Length</th>\n",
       "      <th>Wheelbase</th>\n",
       "      <th>Width</th>\n",
       "      <th>Turn.circle</th>\n",
       "      <th>Rear.seat.room</th>\n",
       "      <th>Luggage.room</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Origin</th>\n",
       "      <th>Make</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Acura</td>\n",
       "      <td>Integra</td>\n",
       "      <td>Small</td>\n",
       "      <td>12.9</td>\n",
       "      <td>15.9</td>\n",
       "      <td>18.8</td>\n",
       "      <td>25</td>\n",
       "      <td>31</td>\n",
       "      <td>None</td>\n",
       "      <td>Front</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>177</td>\n",
       "      <td>102</td>\n",
       "      <td>68</td>\n",
       "      <td>37</td>\n",
       "      <td>26.5</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2705</td>\n",
       "      <td>non-USA</td>\n",
       "      <td>Acura Integra</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Acura</td>\n",
       "      <td>Legend</td>\n",
       "      <td>Midsize</td>\n",
       "      <td>29.2</td>\n",
       "      <td>33.9</td>\n",
       "      <td>38.7</td>\n",
       "      <td>18</td>\n",
       "      <td>25</td>\n",
       "      <td>Driver &amp; Passenger</td>\n",
       "      <td>Front</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>195</td>\n",
       "      <td>115</td>\n",
       "      <td>71</td>\n",
       "      <td>38</td>\n",
       "      <td>30.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>3560</td>\n",
       "      <td>non-USA</td>\n",
       "      <td>Acura Legend</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Audi</td>\n",
       "      <td>90</td>\n",
       "      <td>Compact</td>\n",
       "      <td>25.9</td>\n",
       "      <td>29.1</td>\n",
       "      <td>32.3</td>\n",
       "      <td>20</td>\n",
       "      <td>26</td>\n",
       "      <td>Driver only</td>\n",
       "      <td>Front</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>180</td>\n",
       "      <td>102</td>\n",
       "      <td>67</td>\n",
       "      <td>37</td>\n",
       "      <td>28.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>3375</td>\n",
       "      <td>non-USA</td>\n",
       "      <td>Audi 90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Audi</td>\n",
       "      <td>100</td>\n",
       "      <td>Midsize</td>\n",
       "      <td>30.8</td>\n",
       "      <td>37.7</td>\n",
       "      <td>44.6</td>\n",
       "      <td>19</td>\n",
       "      <td>26</td>\n",
       "      <td>Driver &amp; Passenger</td>\n",
       "      <td>Front</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>193</td>\n",
       "      <td>106</td>\n",
       "      <td>70</td>\n",
       "      <td>37</td>\n",
       "      <td>31.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>3405</td>\n",
       "      <td>non-USA</td>\n",
       "      <td>Audi 100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BMW</td>\n",
       "      <td>535i</td>\n",
       "      <td>Midsize</td>\n",
       "      <td>23.7</td>\n",
       "      <td>30.0</td>\n",
       "      <td>36.2</td>\n",
       "      <td>22</td>\n",
       "      <td>30</td>\n",
       "      <td>Driver only</td>\n",
       "      <td>Rear</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>186</td>\n",
       "      <td>109</td>\n",
       "      <td>69</td>\n",
       "      <td>39</td>\n",
       "      <td>27.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>3640</td>\n",
       "      <td>non-USA</td>\n",
       "      <td>BMW 535i</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Buick</td>\n",
       "      <td>Century</td>\n",
       "      <td>Midsize</td>\n",
       "      <td>14.2</td>\n",
       "      <td>15.7</td>\n",
       "      <td>17.3</td>\n",
       "      <td>22</td>\n",
       "      <td>31</td>\n",
       "      <td>Driver only</td>\n",
       "      <td>Front</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>189</td>\n",
       "      <td>105</td>\n",
       "      <td>69</td>\n",
       "      <td>41</td>\n",
       "      <td>28.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>2880</td>\n",
       "      <td>USA</td>\n",
       "      <td>Buick Century</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Buick</td>\n",
       "      <td>LeSabre</td>\n",
       "      <td>Large</td>\n",
       "      <td>19.9</td>\n",
       "      <td>20.8</td>\n",
       "      <td>21.7</td>\n",
       "      <td>19</td>\n",
       "      <td>28</td>\n",
       "      <td>Driver only</td>\n",
       "      <td>Front</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>200</td>\n",
       "      <td>111</td>\n",
       "      <td>74</td>\n",
       "      <td>42</td>\n",
       "      <td>30.5</td>\n",
       "      <td>17.0</td>\n",
       "      <td>3470</td>\n",
       "      <td>USA</td>\n",
       "      <td>Buick LeSabre</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Buick</td>\n",
       "      <td>Roadmaster</td>\n",
       "      <td>Large</td>\n",
       "      <td>22.6</td>\n",
       "      <td>23.7</td>\n",
       "      <td>24.9</td>\n",
       "      <td>16</td>\n",
       "      <td>25</td>\n",
       "      <td>Driver only</td>\n",
       "      <td>Rear</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>216</td>\n",
       "      <td>116</td>\n",
       "      <td>78</td>\n",
       "      <td>45</td>\n",
       "      <td>30.5</td>\n",
       "      <td>21.0</td>\n",
       "      <td>4105</td>\n",
       "      <td>USA</td>\n",
       "      <td>Buick Roadmaster</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Manufacturer       Model     Type  Min.Price  Price  Max.Price  MPG.city  \\\n",
       "0        Acura     Integra    Small       12.9   15.9       18.8        25   \n",
       "1        Acura      Legend  Midsize       29.2   33.9       38.7        18   \n",
       "2         Audi          90  Compact       25.9   29.1       32.3        20   \n",
       "3         Audi         100  Midsize       30.8   37.7       44.6        19   \n",
       "4          BMW        535i  Midsize       23.7   30.0       36.2        22   \n",
       "5        Buick     Century  Midsize       14.2   15.7       17.3        22   \n",
       "6        Buick     LeSabre    Large       19.9   20.8       21.7        19   \n",
       "7        Buick  Roadmaster    Large       22.6   23.7       24.9        16   \n",
       "\n",
       "   MPG.highway             AirBags DriveTrain        ...        Passengers  \\\n",
       "0           31                None      Front        ...                 5   \n",
       "1           25  Driver & Passenger      Front        ...                 5   \n",
       "2           26         Driver only      Front        ...                 5   \n",
       "3           26  Driver & Passenger      Front        ...                 6   \n",
       "4           30         Driver only       Rear        ...                 4   \n",
       "5           31         Driver only      Front        ...                 6   \n",
       "6           28         Driver only      Front        ...                 6   \n",
       "7           25         Driver only       Rear        ...                 6   \n",
       "\n",
       "   Length  Wheelbase  Width  Turn.circle Rear.seat.room  Luggage.room  Weight  \\\n",
       "0     177        102     68           37           26.5          11.0    2705   \n",
       "1     195        115     71           38           30.0          15.0    3560   \n",
       "2     180        102     67           37           28.0          14.0    3375   \n",
       "3     193        106     70           37           31.0          17.0    3405   \n",
       "4     186        109     69           39           27.0          13.0    3640   \n",
       "5     189        105     69           41           28.0          16.0    2880   \n",
       "6     200        111     74           42           30.5          17.0    3470   \n",
       "7     216        116     78           45           30.5          21.0    4105   \n",
       "\n",
       "    Origin              Make  \n",
       "0  non-USA     Acura Integra  \n",
       "1  non-USA      Acura Legend  \n",
       "2  non-USA           Audi 90  \n",
       "3  non-USA          Audi 100  \n",
       "4  non-USA          BMW 535i  \n",
       "5      USA     Buick Century  \n",
       "6      USA     Buick LeSabre  \n",
       "7      USA  Buick Roadmaster  \n",
       "\n",
       "[8 rows x 27 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['MPG.city']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Manufacturer           object\n",
       "Model                  object\n",
       "Type                   object\n",
       "Min.Price             float64\n",
       "Price                 float64\n",
       "Max.Price             float64\n",
       "MPG.city                int64\n",
       "MPG.highway             int64\n",
       "AirBags                object\n",
       "DriveTrain             object\n",
       "Cylinders              object\n",
       "EngineSize            float64\n",
       "Horsepower              int64\n",
       "RPM                     int64\n",
       "Rev.per.mile            int64\n",
       "Man.trans.avail        object\n",
       "Fuel.tank.capacity    float64\n",
       "Passengers              int64\n",
       "Length                  int64\n",
       "Wheelbase               int64\n",
       "Width                   int64\n",
       "Turn.circle             int64\n",
       "Rear.seat.room        float64\n",
       "Luggage.room          float64\n",
       "Weight                  int64\n",
       "Origin                 object\n",
       "Make                   object\n",
       "dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10de4de10>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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p2/c+/sw8Wl0fj4gHgQ3AE3O6HAaunLN+BXC0zePsBHYCNJvNHB0dXXDcyclJ2vW5bYhf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\nDH5JKozBL0mFMfglqTD/F18Gfy8o2k/0AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d4bab00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "df.Horsepower.hist(bins = 10)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    Acura\n",
       "1    Acura\n",
       "2     Audi\n",
       "3     Audi\n",
       "4      BMW\n",
       "Name: Manufacturer, dtype: object"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Manufacturer'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    Acura\n",
       "1    Acura\n",
       "2     Audi\n",
       "3     Audi\n",
       "4      BMW\n",
       "Name: Manufacturer, dtype: object"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Manufacturer.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    93.000000\n",
       "mean     22.365591\n",
       "std       5.619812\n",
       "min      15.000000\n",
       "25%      18.000000\n",
       "50%      21.000000\n",
       "75%      25.000000\n",
       "max      46.000000\n",
       "Name: MPG.city, dtype: float64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['MPG.city'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Min.Price</th>\n",
       "      <th>Price</th>\n",
       "      <th>Max.Price</th>\n",
       "      <th>MPG.city</th>\n",
       "      <th>MPG.highway</th>\n",
       "      <th>EngineSize</th>\n",
       "      <th>Horsepower</th>\n",
       "      <th>RPM</th>\n",
       "      <th>Rev.per.mile</th>\n",
       "      <th>Fuel.tank.capacity</th>\n",
       "      <th>Passengers</th>\n",
       "      <th>Length</th>\n",
       "      <th>Wheelbase</th>\n",
       "      <th>Width</th>\n",
       "      <th>Turn.circle</th>\n",
       "      <th>Rear.seat.room</th>\n",
       "      <th>Luggage.room</th>\n",
       "      <th>Weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Min.Price</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.970601</td>\n",
       "      <td>0.906756</td>\n",
       "      <td>-0.622875</td>\n",
       "      <td>-0.579966</td>\n",
       "      <td>0.645488</td>\n",
       "      <td>0.802444</td>\n",
       "      <td>-0.042598</td>\n",
       "      <td>-0.470395</td>\n",
       "      <td>0.635369</td>\n",
       "      <td>0.061236</td>\n",
       "      <td>0.553859</td>\n",
       "      <td>0.516758</td>\n",
       "      <td>0.492878</td>\n",
       "      <td>0.428603</td>\n",
       "      <td>0.376642</td>\n",
       "      <td>0.413485</td>\n",
       "      <td>0.666554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Price</th>\n",
       "      <td>0.970601</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.981580</td>\n",
       "      <td>-0.594562</td>\n",
       "      <td>-0.560680</td>\n",
       "      <td>0.597425</td>\n",
       "      <td>0.788218</td>\n",
       "      <td>-0.004955</td>\n",
       "      <td>-0.426395</td>\n",
       "      <td>0.619480</td>\n",
       "      <td>0.057860</td>\n",
       "      <td>0.503628</td>\n",
       "      <td>0.500864</td>\n",
       "      <td>0.456028</td>\n",
       "      <td>0.392590</td>\n",
       "      <td>0.311499</td>\n",
       "      <td>0.366569</td>\n",
       "      <td>0.647179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Max.Price</th>\n",
       "      <td>0.906756</td>\n",
       "      <td>0.981580</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.547811</td>\n",
       "      <td>-0.522561</td>\n",
       "      <td>0.535012</td>\n",
       "      <td>0.744445</td>\n",
       "      <td>0.025015</td>\n",
       "      <td>-0.374024</td>\n",
       "      <td>0.581294</td>\n",
       "      <td>0.053216</td>\n",
       "      <td>0.442933</td>\n",
       "      <td>0.467501</td>\n",
       "      <td>0.408414</td>\n",
       "      <td>0.347785</td>\n",
       "      <td>0.247260</td>\n",
       "      <td>0.315315</td>\n",
       "      <td>0.605142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MPG.city</th>\n",
       "      <td>-0.622875</td>\n",
       "      <td>-0.594562</td>\n",
       "      <td>-0.547811</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.943936</td>\n",
       "      <td>-0.710003</td>\n",
       "      <td>-0.672636</td>\n",
       "      <td>0.363045</td>\n",
       "      <td>0.695857</td>\n",
       "      <td>-0.813144</td>\n",
       "      <td>-0.416856</td>\n",
       "      <td>-0.666239</td>\n",
       "      <td>-0.667108</td>\n",
       "      <td>-0.720534</td>\n",
       "      <td>-0.666389</td>\n",
       "      <td>-0.384347</td>\n",
       "      <td>-0.494894</td>\n",
       "      <td>-0.843139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MPG.highway</th>\n",
       "      <td>-0.579966</td>\n",
       "      <td>-0.560680</td>\n",
       "      <td>-0.522561</td>\n",
       "      <td>0.943936</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.626795</td>\n",
       "      <td>-0.619044</td>\n",
       "      <td>0.313469</td>\n",
       "      <td>0.587497</td>\n",
       "      <td>-0.786039</td>\n",
       "      <td>-0.466386</td>\n",
       "      <td>-0.542897</td>\n",
       "      <td>-0.615384</td>\n",
       "      <td>-0.640359</td>\n",
       "      <td>-0.593683</td>\n",
       "      <td>-0.366684</td>\n",
       "      <td>-0.371629</td>\n",
       "      <td>-0.810658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EngineSize</th>\n",
       "      <td>0.645488</td>\n",
       "      <td>0.597425</td>\n",
       "      <td>0.535012</td>\n",
       "      <td>-0.710003</td>\n",
       "      <td>-0.626795</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.732120</td>\n",
       "      <td>-0.547898</td>\n",
       "      <td>-0.824009</td>\n",
       "      <td>0.759306</td>\n",
       "      <td>0.372721</td>\n",
       "      <td>0.780283</td>\n",
       "      <td>0.732484</td>\n",
       "      <td>0.867110</td>\n",
       "      <td>0.778464</td>\n",
       "      <td>0.502750</td>\n",
       "      <td>0.680827</td>\n",
       "      <td>0.845075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Horsepower</th>\n",
       "      <td>0.802444</td>\n",
       "      <td>0.788218</td>\n",
       "      <td>0.744445</td>\n",
       "      <td>-0.672636</td>\n",
       "      <td>-0.619044</td>\n",
       "      <td>0.732120</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.036688</td>\n",
       "      <td>-0.600314</td>\n",
       "      <td>0.711790</td>\n",
       "      <td>0.009264</td>\n",
       "      <td>0.550865</td>\n",
       "      <td>0.486854</td>\n",
       "      <td>0.644413</td>\n",
       "      <td>0.561216</td>\n",
       "      <td>0.256732</td>\n",
       "      <td>0.359217</td>\n",
       "      <td>0.738798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RPM</th>\n",
       "      <td>-0.042598</td>\n",
       "      <td>-0.004955</td>\n",
       "      <td>0.025015</td>\n",
       "      <td>0.363045</td>\n",
       "      <td>0.313469</td>\n",
       "      <td>-0.547898</td>\n",
       "      <td>0.036688</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.494764</td>\n",
       "      <td>-0.333345</td>\n",
       "      <td>-0.467138</td>\n",
       "      <td>-0.441249</td>\n",
       "      <td>-0.467812</td>\n",
       "      <td>-0.539721</td>\n",
       "      <td>-0.505651</td>\n",
       "      <td>-0.342175</td>\n",
       "      <td>-0.524845</td>\n",
       "      <td>-0.427931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rev.per.mile</th>\n",
       "      <td>-0.470395</td>\n",
       "      <td>-0.426395</td>\n",
       "      <td>-0.374024</td>\n",
       "      <td>0.695857</td>\n",
       "      <td>0.587497</td>\n",
       "      <td>-0.824009</td>\n",
       "      <td>-0.600314</td>\n",
       "      <td>0.494764</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.609710</td>\n",
       "      <td>-0.334976</td>\n",
       "      <td>-0.690233</td>\n",
       "      <td>-0.636824</td>\n",
       "      <td>-0.780460</td>\n",
       "      <td>-0.733160</td>\n",
       "      <td>-0.377010</td>\n",
       "      <td>-0.592792</td>\n",
       "      <td>-0.735264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fuel.tank.capacity</th>\n",
       "      <td>0.635369</td>\n",
       "      <td>0.619480</td>\n",
       "      <td>0.581294</td>\n",
       "      <td>-0.813144</td>\n",
       "      <td>-0.786039</td>\n",
       "      <td>0.759306</td>\n",
       "      <td>0.711790</td>\n",
       "      <td>-0.333345</td>\n",
       "      <td>-0.609710</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.472095</td>\n",
       "      <td>0.690461</td>\n",
       "      <td>0.757674</td>\n",
       "      <td>0.798719</td>\n",
       "      <td>0.671343</td>\n",
       "      <td>0.509689</td>\n",
       "      <td>0.613437</td>\n",
       "      <td>0.894018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Passengers</th>\n",
       "      <td>0.061236</td>\n",
       "      <td>0.057860</td>\n",
       "      <td>0.053216</td>\n",
       "      <td>-0.416856</td>\n",
       "      <td>-0.466386</td>\n",
       "      <td>0.372721</td>\n",
       "      <td>0.009264</td>\n",
       "      <td>-0.467138</td>\n",
       "      <td>-0.334976</td>\n",
       "      <td>0.472095</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.485294</td>\n",
       "      <td>0.694054</td>\n",
       "      <td>0.489979</td>\n",
       "      <td>0.449025</td>\n",
       "      <td>0.694134</td>\n",
       "      <td>0.653317</td>\n",
       "      <td>0.553273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Length</th>\n",
       "      <td>0.553859</td>\n",
       "      <td>0.503628</td>\n",
       "      <td>0.442933</td>\n",
       "      <td>-0.666239</td>\n",
       "      <td>-0.542897</td>\n",
       "      <td>0.780283</td>\n",
       "      <td>0.550865</td>\n",
       "      <td>-0.441249</td>\n",
       "      <td>-0.690233</td>\n",
       "      <td>0.690461</td>\n",
       "      <td>0.485294</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.823650</td>\n",
       "      <td>0.822148</td>\n",
       "      <td>0.738955</td>\n",
       "      <td>0.549958</td>\n",
       "      <td>0.712962</td>\n",
       "      <td>0.806274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wheelbase</th>\n",
       "      <td>0.516758</td>\n",
       "      <td>0.500864</td>\n",
       "      <td>0.467501</td>\n",
       "      <td>-0.667108</td>\n",
       "      <td>-0.615384</td>\n",
       "      <td>0.732484</td>\n",
       "      <td>0.486854</td>\n",
       "      <td>-0.467812</td>\n",
       "      <td>-0.636824</td>\n",
       "      <td>0.757674</td>\n",
       "      <td>0.694054</td>\n",
       "      <td>0.823650</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.807213</td>\n",
       "      <td>0.723324</td>\n",
       "      <td>0.667259</td>\n",
       "      <td>0.734127</td>\n",
       "      <td>0.871895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Width</th>\n",
       "      <td>0.492878</td>\n",
       "      <td>0.456028</td>\n",
       "      <td>0.408414</td>\n",
       "      <td>-0.720534</td>\n",
       "      <td>-0.640359</td>\n",
       "      <td>0.867110</td>\n",
       "      <td>0.644413</td>\n",
       "      <td>-0.539721</td>\n",
       "      <td>-0.780460</td>\n",
       "      <td>0.798719</td>\n",
       "      <td>0.489979</td>\n",
       "      <td>0.822148</td>\n",
       "      <td>0.807213</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.817854</td>\n",
       "      <td>0.465618</td>\n",
       "      <td>0.673490</td>\n",
       "      <td>0.874961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Turn.circle</th>\n",
       "      <td>0.428603</td>\n",
       "      <td>0.392590</td>\n",
       "      <td>0.347785</td>\n",
       "      <td>-0.666389</td>\n",
       "      <td>-0.593683</td>\n",
       "      <td>0.778464</td>\n",
       "      <td>0.561216</td>\n",
       "      <td>-0.505651</td>\n",
       "      <td>-0.733160</td>\n",
       "      <td>0.671343</td>\n",
       "      <td>0.449025</td>\n",
       "      <td>0.738955</td>\n",
       "      <td>0.723324</td>\n",
       "      <td>0.817854</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.466328</td>\n",
       "      <td>0.585018</td>\n",
       "      <td>0.778043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rear.seat.room</th>\n",
       "      <td>0.376642</td>\n",
       "      <td>0.311499</td>\n",
       "      <td>0.247260</td>\n",
       "      <td>-0.384347</td>\n",
       "      <td>-0.366684</td>\n",
       "      <td>0.502750</td>\n",
       "      <td>0.256732</td>\n",
       "      <td>-0.342175</td>\n",
       "      <td>-0.377010</td>\n",
       "      <td>0.509689</td>\n",
       "      <td>0.694134</td>\n",
       "      <td>0.549958</td>\n",
       "      <td>0.667259</td>\n",
       "      <td>0.465618</td>\n",
       "      <td>0.466328</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.651968</td>\n",
       "      <td>0.526250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Luggage.room</th>\n",
       "      <td>0.413485</td>\n",
       "      <td>0.366569</td>\n",
       "      <td>0.315315</td>\n",
       "      <td>-0.494894</td>\n",
       "      <td>-0.371629</td>\n",
       "      <td>0.680827</td>\n",
       "      <td>0.359217</td>\n",
       "      <td>-0.524845</td>\n",
       "      <td>-0.592792</td>\n",
       "      <td>0.613437</td>\n",
       "      <td>0.653317</td>\n",
       "      <td>0.712962</td>\n",
       "      <td>0.734127</td>\n",
       "      <td>0.673490</td>\n",
       "      <td>0.585018</td>\n",
       "      <td>0.651968</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.637226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Weight</th>\n",
       "      <td>0.666554</td>\n",
       "      <td>0.647179</td>\n",
       "      <td>0.605142</td>\n",
       "      <td>-0.843139</td>\n",
       "      <td>-0.810658</td>\n",
       "      <td>0.845075</td>\n",
       "      <td>0.738798</td>\n",
       "      <td>-0.427931</td>\n",
       "      <td>-0.735264</td>\n",
       "      <td>0.894018</td>\n",
       "      <td>0.553273</td>\n",
       "      <td>0.806274</td>\n",
       "      <td>0.871895</td>\n",
       "      <td>0.874961</td>\n",
       "      <td>0.778043</td>\n",
       "      <td>0.526250</td>\n",
       "      <td>0.637226</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Min.Price     Price  Max.Price  MPG.city  MPG.highway  \\\n",
       "Min.Price            1.000000  0.970601   0.906756 -0.622875    -0.579966   \n",
       "Price                0.970601  1.000000   0.981580 -0.594562    -0.560680   \n",
       "Max.Price            0.906756  0.981580   1.000000 -0.547811    -0.522561   \n",
       "MPG.city            -0.622875 -0.594562  -0.547811  1.000000     0.943936   \n",
       "MPG.highway         -0.579966 -0.560680  -0.522561  0.943936     1.000000   \n",
       "EngineSize           0.645488  0.597425   0.535012 -0.710003    -0.626795   \n",
       "Horsepower           0.802444  0.788218   0.744445 -0.672636    -0.619044   \n",
       "RPM                 -0.042598 -0.004955   0.025015  0.363045     0.313469   \n",
       "Rev.per.mile        -0.470395 -0.426395  -0.374024  0.695857     0.587497   \n",
       "Fuel.tank.capacity   0.635369  0.619480   0.581294 -0.813144    -0.786039   \n",
       "Passengers           0.061236  0.057860   0.053216 -0.416856    -0.466386   \n",
       "Length               0.553859  0.503628   0.442933 -0.666239    -0.542897   \n",
       "Wheelbase            0.516758  0.500864   0.467501 -0.667108    -0.615384   \n",
       "Width                0.492878  0.456028   0.408414 -0.720534    -0.640359   \n",
       "Turn.circle          0.428603  0.392590   0.347785 -0.666389    -0.593683   \n",
       "Rear.seat.room       0.376642  0.311499   0.247260 -0.384347    -0.366684   \n",
       "Luggage.room         0.413485  0.366569   0.315315 -0.494894    -0.371629   \n",
       "Weight               0.666554  0.647179   0.605142 -0.843139    -0.810658   \n",
       "\n",
       "                    EngineSize  Horsepower       RPM  Rev.per.mile  \\\n",
       "Min.Price             0.645488    0.802444 -0.042598     -0.470395   \n",
       "Price                 0.597425    0.788218 -0.004955     -0.426395   \n",
       "Max.Price             0.535012    0.744445  0.025015     -0.374024   \n",
       "MPG.city             -0.710003   -0.672636  0.363045      0.695857   \n",
       "MPG.highway          -0.626795   -0.619044  0.313469      0.587497   \n",
       "EngineSize            1.000000    0.732120 -0.547898     -0.824009   \n",
       "Horsepower            0.732120    1.000000  0.036688     -0.600314   \n",
       "RPM                  -0.547898    0.036688  1.000000      0.494764   \n",
       "Rev.per.mile         -0.824009   -0.600314  0.494764      1.000000   \n",
       "Fuel.tank.capacity    0.759306    0.711790 -0.333345     -0.609710   \n",
       "Passengers            0.372721    0.009264 -0.467138     -0.334976   \n",
       "Length                0.780283    0.550865 -0.441249     -0.690233   \n",
       "Wheelbase             0.732484    0.486854 -0.467812     -0.636824   \n",
       "Width                 0.867110    0.644413 -0.539721     -0.780460   \n",
       "Turn.circle           0.778464    0.561216 -0.505651     -0.733160   \n",
       "Rear.seat.room        0.502750    0.256732 -0.342175     -0.377010   \n",
       "Luggage.room          0.680827    0.359217 -0.524845     -0.592792   \n",
       "Weight                0.845075    0.738798 -0.427931     -0.735264   \n",
       "\n",
       "                    Fuel.tank.capacity  Passengers    Length  Wheelbase  \\\n",
       "Min.Price                     0.635369    0.061236  0.553859   0.516758   \n",
       "Price                         0.619480    0.057860  0.503628   0.500864   \n",
       "Max.Price                     0.581294    0.053216  0.442933   0.467501   \n",
       "MPG.city                     -0.813144   -0.416856 -0.666239  -0.667108   \n",
       "MPG.highway                  -0.786039   -0.466386 -0.542897  -0.615384   \n",
       "EngineSize                    0.759306    0.372721  0.780283   0.732484   \n",
       "Horsepower                    0.711790    0.009264  0.550865   0.486854   \n",
       "RPM                          -0.333345   -0.467138 -0.441249  -0.467812   \n",
       "Rev.per.mile                 -0.609710   -0.334976 -0.690233  -0.636824   \n",
       "Fuel.tank.capacity            1.000000    0.472095  0.690461   0.757674   \n",
       "Passengers                    0.472095    1.000000  0.485294   0.694054   \n",
       "Length                        0.690461    0.485294  1.000000   0.823650   \n",
       "Wheelbase                     0.757674    0.694054  0.823650   1.000000   \n",
       "Width                         0.798719    0.489979  0.822148   0.807213   \n",
       "Turn.circle                   0.671343    0.449025  0.738955   0.723324   \n",
       "Rear.seat.room                0.509689    0.694134  0.549958   0.667259   \n",
       "Luggage.room                  0.613437    0.653317  0.712962   0.734127   \n",
       "Weight                        0.894018    0.553273  0.806274   0.871895   \n",
       "\n",
       "                       Width  Turn.circle  Rear.seat.room  Luggage.room  \\\n",
       "Min.Price           0.492878     0.428603        0.376642      0.413485   \n",
       "Price               0.456028     0.392590        0.311499      0.366569   \n",
       "Max.Price           0.408414     0.347785        0.247260      0.315315   \n",
       "MPG.city           -0.720534    -0.666389       -0.384347     -0.494894   \n",
       "MPG.highway        -0.640359    -0.593683       -0.366684     -0.371629   \n",
       "EngineSize          0.867110     0.778464        0.502750      0.680827   \n",
       "Horsepower          0.644413     0.561216        0.256732      0.359217   \n",
       "RPM                -0.539721    -0.505651       -0.342175     -0.524845   \n",
       "Rev.per.mile       -0.780460    -0.733160       -0.377010     -0.592792   \n",
       "Fuel.tank.capacity  0.798719     0.671343        0.509689      0.613437   \n",
       "Passengers          0.489979     0.449025        0.694134      0.653317   \n",
       "Length              0.822148     0.738955        0.549958      0.712962   \n",
       "Wheelbase           0.807213     0.723324        0.667259      0.734127   \n",
       "Width               1.000000     0.817854        0.465618      0.673490   \n",
       "Turn.circle         0.817854     1.000000        0.466328      0.585018   \n",
       "Rear.seat.room      0.465618     0.466328        1.000000      0.651968   \n",
       "Luggage.room        0.673490     0.585018        0.651968      1.000000   \n",
       "Weight              0.874961     0.778043        0.526250      0.637226   \n",
       "\n",
       "                      Weight  \n",
       "Min.Price           0.666554  \n",
       "Price               0.647179  \n",
       "Max.Price           0.605142  \n",
       "MPG.city           -0.843139  \n",
       "MPG.highway        -0.810658  \n",
       "EngineSize          0.845075  \n",
       "Horsepower          0.738798  \n",
       "RPM                -0.427931  \n",
       "Rev.per.mile       -0.735264  \n",
       "Fuel.tank.capacity  0.894018  \n",
       "Passengers          0.553273  \n",
       "Length              0.806274  \n",
       "Wheelbase           0.871895  \n",
       "Width               0.874961  \n",
       "Turn.circle         0.778043  \n",
       "Rear.seat.room      0.526250  \n",
       "Luggage.room        0.637226  \n",
       "Weight              1.000000  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10f2e47b8>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SIWrGVUqSJC2HFSJJktReV9do92BEWCGSJEmNZ4VIkiS15xwiSZKkZrBCJEmS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\niFJKKaXU9/4f50dO41QGwPIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f32b0f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "corr = df.corr()\n",
    "fig, ax = plt.subplots(1,1, figsize =(9,9))\n",
    "sns.heatmap(corr, \n",
    "            xticklabels=corr.columns.values,\n",
    "            yticklabels=corr.columns.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Min.Price</th>\n",
       "      <th>Price</th>\n",
       "      <th>Max.Price</th>\n",
       "      <th>MPG.city</th>\n",
       "      <th>MPG.highway</th>\n",
       "      <th>EngineSize</th>\n",
       "      <th>Horsepower</th>\n",
       "      <th>RPM</th>\n",
       "      <th>Rev.per.mile</th>\n",
       "      <th>Fuel.tank.capacity</th>\n",
       "      <th>Passengers</th>\n",
       "      <th>Length</th>\n",
       "      <th>Wheelbase</th>\n",
       "      <th>Width</th>\n",
       "      <th>Turn.circle</th>\n",
       "      <th>Rear.seat.room</th>\n",
       "      <th>Luggage.room</th>\n",
       "      <th>Weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>93.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>91.000000</td>\n",
       "      <td>82.000000</td>\n",
       "      <td>93.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>17.125806</td>\n",
       "      <td>19.509677</td>\n",
       "      <td>21.898925</td>\n",
       "      <td>22.365591</td>\n",
       "      <td>29.086022</td>\n",
       "      <td>2.667742</td>\n",
       "      <td>143.827957</td>\n",
       "      <td>5280.645161</td>\n",
       "      <td>2332.204301</td>\n",
       "      <td>16.664516</td>\n",
       "      <td>5.086022</td>\n",
       "      <td>183.204301</td>\n",
       "      <td>103.946237</td>\n",
       "      <td>69.376344</td>\n",
       "      <td>38.956989</td>\n",
       "      <td>27.829670</td>\n",
       "      <td>13.890244</td>\n",
       "      <td>3072.903226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.746029</td>\n",
       "      <td>9.659430</td>\n",
       "      <td>11.030457</td>\n",
       "      <td>5.619812</td>\n",
       "      <td>5.331726</td>\n",
       "      <td>1.037363</td>\n",
       "      <td>52.374410</td>\n",
       "      <td>596.731690</td>\n",
       "      <td>496.506525</td>\n",
       "      <td>3.279370</td>\n",
       "      <td>1.038979</td>\n",
       "      <td>14.602382</td>\n",
       "      <td>6.819674</td>\n",
       "      <td>3.778986</td>\n",
       "      <td>3.223265</td>\n",
       "      <td>2.989072</td>\n",
       "      <td>2.997967</td>\n",
       "      <td>589.896510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>6.700000</td>\n",
       "      <td>7.400000</td>\n",
       "      <td>7.900000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>3800.000000</td>\n",
       "      <td>1320.000000</td>\n",
       "      <td>9.200000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>141.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1695.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>10.800000</td>\n",
       "      <td>12.200000</td>\n",
       "      <td>14.700000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>1.800000</td>\n",
       "      <td>103.000000</td>\n",
       "      <td>4800.000000</td>\n",
       "      <td>1985.000000</td>\n",
       "      <td>14.500000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>174.000000</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>67.000000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>2620.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>14.700000</td>\n",
       "      <td>17.700000</td>\n",
       "      <td>19.600000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>2.400000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>5200.000000</td>\n",
       "      <td>2340.000000</td>\n",
       "      <td>16.400000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>183.000000</td>\n",
       "      <td>103.000000</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>27.500000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>3040.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>20.300000</td>\n",
       "      <td>23.300000</td>\n",
       "      <td>25.300000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>3.300000</td>\n",
       "      <td>170.000000</td>\n",
       "      <td>5750.000000</td>\n",
       "      <td>2565.000000</td>\n",
       "      <td>18.800000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>192.000000</td>\n",
       "      <td>110.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>3525.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>45.400000</td>\n",
       "      <td>61.900000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>5.700000</td>\n",
       "      <td>300.000000</td>\n",
       "      <td>6500.000000</td>\n",
       "      <td>3755.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>219.000000</td>\n",
       "      <td>119.000000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>4105.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Min.Price      Price  Max.Price   MPG.city  MPG.highway  EngineSize  \\\n",
       "count  93.000000  93.000000  93.000000  93.000000    93.000000   93.000000   \n",
       "mean   17.125806  19.509677  21.898925  22.365591    29.086022    2.667742   \n",
       "std     8.746029   9.659430  11.030457   5.619812     5.331726    1.037363   \n",
       "min     6.700000   7.400000   7.900000  15.000000    20.000000    1.000000   \n",
       "25%    10.800000  12.200000  14.700000  18.000000    26.000000    1.800000   \n",
       "50%    14.700000  17.700000  19.600000  21.000000    28.000000    2.400000   \n",
       "75%    20.300000  23.300000  25.300000  25.000000    31.000000    3.300000   \n",
       "max    45.400000  61.900000  80.000000  46.000000    50.000000    5.700000   \n",
       "\n",
       "       Horsepower          RPM  Rev.per.mile  Fuel.tank.capacity  Passengers  \\\n",
       "count   93.000000    93.000000     93.000000           93.000000   93.000000   \n",
       "mean   143.827957  5280.645161   2332.204301           16.664516    5.086022   \n",
       "std     52.374410   596.731690    496.506525            3.279370    1.038979   \n",
       "min     55.000000  3800.000000   1320.000000            9.200000    2.000000   \n",
       "25%    103.000000  4800.000000   1985.000000           14.500000    4.000000   \n",
       "50%    140.000000  5200.000000   2340.000000           16.400000    5.000000   \n",
       "75%    170.000000  5750.000000   2565.000000           18.800000    6.000000   \n",
       "max    300.000000  6500.000000   3755.000000           27.000000    8.000000   \n",
       "\n",
       "           Length   Wheelbase      Width  Turn.circle  Rear.seat.room  \\\n",
       "count   93.000000   93.000000  93.000000    93.000000       91.000000   \n",
       "mean   183.204301  103.946237  69.376344    38.956989       27.829670   \n",
       "std     14.602382    6.819674   3.778986     3.223265        2.989072   \n",
       "min    141.000000   90.000000  60.000000    32.000000       19.000000   \n",
       "25%    174.000000   98.000000  67.000000    37.000000       26.000000   \n",
       "50%    183.000000  103.000000  69.000000    39.000000       27.500000   \n",
       "75%    192.000000  110.000000  72.000000    41.000000       30.000000   \n",
       "max    219.000000  119.000000  78.000000    45.000000       36.000000   \n",
       "\n",
       "       Luggage.room       Weight  \n",
       "count     82.000000    93.000000  \n",
       "mean      13.890244  3072.903226  \n",
       "std        2.997967   589.896510  \n",
       "min        6.000000  1695.000000  \n",
       "25%       12.000000  2620.000000  \n",
       "50%       14.000000  3040.000000  \n",
       "75%       15.000000  3525.000000  \n",
       "max       22.000000  4105.000000  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Manufacturer           object\n",
       "Model                  object\n",
       "Type                   object\n",
       "Min.Price             float64\n",
       "Price                 float64\n",
       "Max.Price             float64\n",
       "MPG.city                int64\n",
       "MPG.highway             int64\n",
       "AirBags                object\n",
       "DriveTrain             object\n",
       "Cylinders              object\n",
       "EngineSize            float64\n",
       "Horsepower              int64\n",
       "RPM                     int64\n",
       "Rev.per.mile            int64\n",
       "Man.trans.avail        object\n",
       "Fuel.tank.capacity    float64\n",
       "Passengers              int64\n",
       "Length                  int64\n",
       "Wheelbase               int64\n",
       "Width                   int64\n",
       "Turn.circle             int64\n",
       "Rear.seat.room        float64\n",
       "Luggage.room          float64\n",
       "Weight                  int64\n",
       "Origin                 object\n",
       "Make                   object\n",
       "dtype: object"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Midsize    22\n",
       "Small      21\n",
       "Compact    16\n",
       "Sporty     14\n",
       "Large      11\n",
       "Van         9\n",
       "Name: Type, dtype: int64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Type.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "USA        48\n",
       "non-USA    45\n",
       "Name: Origin, dtype: int64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Origin.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10, 27)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "condition = (df['Origin'] == 'non-USA' ) & (df.Type == 'Midsize')\n",
    "condition\n",
    "\n",
    "df[condition].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4, 27)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col = 'Origin'\n",
    "condition = df[col].isnull()\n",
    "\n",
    "df[condition].shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "df['log_weight'] = df.Weight.apply(  lambda w : np.log(w + 1) ) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10e0c16a0>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ib8MAAMQg5ivho0ePqre3V36/X2vXrlV1dbXcbrfmzJmjgoKCRNQIAEBKGlYIT5s2TU1N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\nALAxPgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10def4978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['log_weight'].hist(bins = 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10e203630>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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1d3errKxMGzduVGVlpdLT0zV9+nQVFBTEY0YAAFLSkCJ8//33q7GxUZJUXFwcvr+kpEQl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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e1c7240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Weight'].hist(bins = 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}