ProceessData.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true,
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sea\n",
    "%matplotlib inline\n",
    "\n",
    "names = [\"symboling\", \"normalized-losses\", \"make\", \"fuel-type\", \"aspiration\", \"num-of-doors\", \"body-style\", \"drive-wheels\", \"engine-location\", \"wheel-base\", \"length\", \"width\", \"height\", \"curb-weight\", \"engine-type\", \"num-of-cylinders\", \"engine-size\", \"fuel-system\", \"bore\", \"stroke\", \"compression-ratio\", \"horsepower\", \"peak-rpm\", \"city-mpg\", \"highway-mpg\", \"price\"]\n",
    "\n",
    "# read the data file with pandas\n",
    "# url = \"https://archive.ics.uci.edu/ml/machine-learning-databases/autos/imports-85.data\"\n",
    "# df = pd.read_csv(url, header=None)\n",
    "\n",
    "# change column names\n",
    "# df.columns = names\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "inputHidden": false,
    "outputHidden": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "print()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {}
   },
   "source": [
    "## save to a csv file \n",
    "# df.to_csv(\"./data/cars_data.csv\")\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "metadata": false,
     "name": "#%% md\n"
    }
   },
   "source": [
    "\n",
    "## load file from csv\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "metadata": false,
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "df2 = pd.read_csv(\"./data/cars_data.csv\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "metadata": false,
     "name": "#%% md\n"
    }
   },
   "source": [
    "\n",
    "## analyse the data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "inputHidden": false,
    "outputHidden": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "metadata": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>symboling</th>\n",
       "      <th>normalized-losses</th>\n",
       "      <th>make</th>\n",
       "      <th>fuel-type</th>\n",
       "      <th>aspiration</th>\n",
       "      <th>num-of-doors</th>\n",
       "      <th>body-style</th>\n",
       "      <th>drive-wheels</th>\n",
       "      <th>engine-location</th>\n",
       "      <th>wheel-base</th>\n",
       "      <th>...</th>\n",
       "      <th>engine-size</th>\n",
       "      <th>fuel-system</th>\n",
       "      <th>bore</th>\n",
       "      <th>stroke</th>\n",
       "      <th>compression-ratio</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>peak-rpm</th>\n",
       "      <th>city-mpg</th>\n",
       "      <th>highway-mpg</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>?</td>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>convertible</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>88.6</td>\n",
       "      <td>...</td>\n",
       "      <td>130</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.47</td>\n",
       "      <td>2.68</td>\n",
       "      <td>9.0</td>\n",
       "      <td>111</td>\n",
       "      <td>5000</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>13495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>?</td>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>convertible</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>88.6</td>\n",
       "      <td>...</td>\n",
       "      <td>130</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.47</td>\n",
       "      <td>2.68</td>\n",
       "      <td>9.0</td>\n",
       "      <td>111</td>\n",
       "      <td>5000</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>16500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>?</td>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>94.5</td>\n",
       "      <td>...</td>\n",
       "      <td>152</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>2.68</td>\n",
       "      <td>3.47</td>\n",
       "      <td>9.0</td>\n",
       "      <td>154</td>\n",
       "      <td>5000</td>\n",
       "      <td>19</td>\n",
       "      <td>26</td>\n",
       "      <td>16500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>164</td>\n",
       "      <td>audi</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>99.8</td>\n",
       "      <td>...</td>\n",
       "      <td>109</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.4</td>\n",
       "      <td>10.0</td>\n",
       "      <td>102</td>\n",
       "      <td>5500</td>\n",
       "      <td>24</td>\n",
       "      <td>30</td>\n",
       "      <td>13950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>164</td>\n",
       "      <td>audi</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>4wd</td>\n",
       "      <td>front</td>\n",
       "      <td>99.4</td>\n",
       "      <td>...</td>\n",
       "      <td>136</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.4</td>\n",
       "      <td>8.0</td>\n",
       "      <td>115</td>\n",
       "      <td>5500</td>\n",
       "      <td>18</td>\n",
       "      <td>22</td>\n",
       "      <td>17450</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   symboling normalized-losses         make fuel-type aspiration num-of-doors  \\\n",
       "0          3                 ?  alfa-romero       gas        std          two   \n",
       "1          3                 ?  alfa-romero       gas        std          two   \n",
       "2          1                 ?  alfa-romero       gas        std          two   \n",
       "3          2               164         audi       gas        std         four   \n",
       "4          2               164         audi       gas        std         four   \n",
       "\n",
       "    body-style drive-wheels engine-location  wheel-base  ...    engine-size  \\\n",
       "0  convertible          rwd           front        88.6  ...            130   \n",
       "1  convertible          rwd           front        88.6  ...            130   \n",
       "2    hatchback          rwd           front        94.5  ...            152   \n",
       "3        sedan          fwd           front        99.8  ...            109   \n",
       "4        sedan          4wd           front        99.4  ...            136   \n",
       "\n",
       "   fuel-system  bore  stroke compression-ratio horsepower  peak-rpm city-mpg  \\\n",
       "0         mpfi  3.47    2.68               9.0        111      5000       21   \n",
       "1         mpfi  3.47    2.68               9.0        111      5000       21   \n",
       "2         mpfi  2.68    3.47               9.0        154      5000       19   \n",
       "3         mpfi  3.19     3.4              10.0        102      5500       24   \n",
       "4         mpfi  3.19     3.4               8.0        115      5500       18   \n",
       "\n",
       "  highway-mpg  price  \n",
       "0          27  13495  \n",
       "1          27  16500  \n",
       "2          26  16500  \n",
       "3          30  13950  \n",
       "4          22  17450  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "metadata": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(205, 26)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "metadata": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>symboling</th>\n",
       "      <th>normalized-losses</th>\n",
       "      <th>make</th>\n",
       "      <th>fuel-type</th>\n",
       "      <th>aspiration</th>\n",
       "      <th>num-of-doors</th>\n",
       "      <th>body-style</th>\n",
       "      <th>drive-wheels</th>\n",
       "      <th>engine-location</th>\n",
       "      <th>wheel-base</th>\n",
       "      <th>...</th>\n",
       "      <th>engine-size</th>\n",
       "      <th>fuel-system</th>\n",
       "      <th>bore</th>\n",
       "      <th>stroke</th>\n",
       "      <th>compression-ratio</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>peak-rpm</th>\n",
       "      <th>city-mpg</th>\n",
       "      <th>highway-mpg</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>205.000000</td>\n",
       "      <td>205</td>\n",
       "      <td>205</td>\n",
       "      <td>205</td>\n",
       "      <td>205</td>\n",
       "      <td>205</td>\n",
       "      <td>205</td>\n",
       "      <td>205</td>\n",
       "      <td>205</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>205</td>\n",
       "      <td>205</td>\n",
       "      <td>205</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>205</td>\n",
       "      <td>205</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>NaN</td>\n",
       "      <td>52</td>\n",
       "      <td>22</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "      <td>39</td>\n",
       "      <td>37</td>\n",
       "      <td>NaN</td>\n",
       "      <td>60</td>\n",
       "      <td>24</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>NaN</td>\n",
       "      <td>?</td>\n",
       "      <td>toyota</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.62</td>\n",
       "      <td>3.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>68</td>\n",
       "      <td>5500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>NaN</td>\n",
       "      <td>41</td>\n",
       "      <td>32</td>\n",
       "      <td>185</td>\n",
       "      <td>168</td>\n",
       "      <td>114</td>\n",
       "      <td>96</td>\n",
       "      <td>120</td>\n",
       "      <td>202</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>94</td>\n",
       "      <td>23</td>\n",
       "      <td>20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19</td>\n",
       "      <td>37</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.834146</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>98.756585</td>\n",
       "      <td>...</td>\n",
       "      <td>126.907317</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.142537</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.219512</td>\n",
       "      <td>30.751220</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.245307</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.021776</td>\n",
       "      <td>...</td>\n",
       "      <td>41.642693</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.972040</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.542142</td>\n",
       "      <td>6.886443</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-2.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>86.600000</td>\n",
       "      <td>...</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>94.500000</td>\n",
       "      <td>...</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.600000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>102.400000</td>\n",
       "      <td>...</td>\n",
       "      <td>141.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.400000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>120.900000</td>\n",
       "      <td>...</td>\n",
       "      <td>326.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         symboling normalized-losses    make fuel-type aspiration  \\\n",
       "count   205.000000               205     205       205        205   \n",
       "unique         NaN                52      22         2          2   \n",
       "top            NaN                 ?  toyota       gas        std   \n",
       "freq           NaN                41      32       185        168   \n",
       "mean      0.834146               NaN     NaN       NaN        NaN   \n",
       "std       1.245307               NaN     NaN       NaN        NaN   \n",
       "min      -2.000000               NaN     NaN       NaN        NaN   \n",
       "25%       0.000000               NaN     NaN       NaN        NaN   \n",
       "50%       1.000000               NaN     NaN       NaN        NaN   \n",
       "75%       2.000000               NaN     NaN       NaN        NaN   \n",
       "max       3.000000               NaN     NaN       NaN        NaN   \n",
       "\n",
       "       num-of-doors body-style drive-wheels engine-location  wheel-base  ...   \\\n",
       "count           205        205          205             205  205.000000  ...    \n",
       "unique            3          5            3               2         NaN  ...    \n",
       "top            four      sedan          fwd           front         NaN  ...    \n",
       "freq            114         96          120             202         NaN  ...    \n",
       "mean            NaN        NaN          NaN             NaN   98.756585  ...    \n",
       "std             NaN        NaN          NaN             NaN    6.021776  ...    \n",
       "min             NaN        NaN          NaN             NaN   86.600000  ...    \n",
       "25%             NaN        NaN          NaN             NaN   94.500000  ...    \n",
       "50%             NaN        NaN          NaN             NaN   97.000000  ...    \n",
       "75%             NaN        NaN          NaN             NaN  102.400000  ...    \n",
       "max             NaN        NaN          NaN             NaN  120.900000  ...    \n",
       "\n",
       "        engine-size  fuel-system  bore  stroke compression-ratio horsepower  \\\n",
       "count    205.000000          205   205     205        205.000000        205   \n",
       "unique          NaN            8    39      37               NaN         60   \n",
       "top             NaN         mpfi  3.62     3.4               NaN         68   \n",
       "freq            NaN           94    23      20               NaN         19   \n",
       "mean     126.907317          NaN   NaN     NaN         10.142537        NaN   \n",
       "std       41.642693          NaN   NaN     NaN          3.972040        NaN   \n",
       "min       61.000000          NaN   NaN     NaN          7.000000        NaN   \n",
       "25%       97.000000          NaN   NaN     NaN          8.600000        NaN   \n",
       "50%      120.000000          NaN   NaN     NaN          9.000000        NaN   \n",
       "75%      141.000000          NaN   NaN     NaN          9.400000        NaN   \n",
       "max      326.000000          NaN   NaN     NaN         23.000000        NaN   \n",
       "\n",
       "        peak-rpm    city-mpg highway-mpg price  \n",
       "count        205  205.000000  205.000000   205  \n",
       "unique        24         NaN         NaN   187  \n",
       "top         5500         NaN         NaN     ?  \n",
       "freq          37         NaN         NaN     4  \n",
       "mean         NaN   25.219512   30.751220   NaN  \n",
       "std          NaN    6.542142    6.886443   NaN  \n",
       "min          NaN   13.000000   16.000000   NaN  \n",
       "25%          NaN   19.000000   25.000000   NaN  \n",
       "50%          NaN   24.000000   30.000000   NaN  \n",
       "75%          NaN   30.000000   34.000000   NaN  \n",
       "max          NaN   49.000000   54.000000   NaN  \n",
       "\n",
       "[11 rows x 26 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.describe(include = \"all\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "metadata": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "symboling              int64\n",
       "normalized-losses     object\n",
       "make                  object\n",
       "fuel-type             object\n",
       "aspiration            object\n",
       "num-of-doors          object\n",
       "body-style            object\n",
       "drive-wheels          object\n",
       "engine-location       object\n",
       "wheel-base           float64\n",
       "length               float64\n",
       "width                float64\n",
       "height               float64\n",
       "curb-weight            int64\n",
       "engine-type           object\n",
       "num-of-cylinders      object\n",
       "engine-size            int64\n",
       "fuel-system           object\n",
       "bore                  object\n",
       "stroke                object\n",
       "compression-ratio    float64\n",
       "horsepower            object\n",
       "peak-rpm              object\n",
       "city-mpg               int64\n",
       "highway-mpg            int64\n",
       "price                 object\n",
       "dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "metadata": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 205 entries, 0 to 204\n",
      "Data columns (total 26 columns):\n",
      "symboling            205 non-null int64\n",
      "normalized-losses    205 non-null object\n",
      "make                 205 non-null object\n",
      "fuel-type            205 non-null object\n",
      "aspiration           205 non-null object\n",
      "num-of-doors         205 non-null object\n",
      "body-style           205 non-null object\n",
      "drive-wheels         205 non-null object\n",
      "engine-location      205 non-null object\n",
      "wheel-base           205 non-null float64\n",
      "length               205 non-null float64\n",
      "width                205 non-null float64\n",
      "height               205 non-null float64\n",
      "curb-weight          205 non-null int64\n",
      "engine-type          205 non-null object\n",
      "num-of-cylinders     205 non-null object\n",
      "engine-size          205 non-null int64\n",
      "fuel-system          205 non-null object\n",
      "bore                 205 non-null object\n",
      "stroke               205 non-null object\n",
      "compression-ratio    205 non-null float64\n",
      "horsepower           205 non-null object\n",
      "peak-rpm             205 non-null object\n",
      "city-mpg             205 non-null int64\n",
      "highway-mpg          205 non-null int64\n",
      "price                205 non-null object\n",
      "dtypes: float64(5), int64(5), object(16)\n",
      "memory usage: 41.7+ KB\n"
     ]
    }
   ],
   "source": [
    "df2.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "metadata": false,
     "name": "#%% md\n"
    }
   },
   "source": [
    "\n",
    "## covert data types\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "metadata": false,
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "df2[\"normalized-losses\"] = pd.to_numeric(df2[\"normalized-losses\"], errors = \"coerce\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "metadata": false,
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "df2[\"price\"] = pd.to_numeric(df2[\"price\"], errors = \"coerce\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "metadata": false,
     "name": "#%% md\n"
    }
   },
   "source": [
    "\n",
    "## convert multiple columns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "metadata": false,
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "df2[[\"horsepower\", \"peak-rpm\"]] = df2[[\"horsepower\", \"peak-rpm\"]].apply(pd.to_numeric, errors = \"coerce\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "metadata": false,
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "df2[[\"wheel-base\", \"engine-size\"]] = df2[[\"wheel-base\", \"engine-size\"]].apply(pd.to_numeric, errors = \"coerce\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "metadata": false,
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "df2[[\"bore\", \"stroke\"]] = df2[[\"bore\", \"stroke\"]].apply(pd.to_numeric, errors = \"coerce\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "metadata": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>symboling</th>\n",
       "      <th>normalized-losses</th>\n",
       "      <th>make</th>\n",
       "      <th>fuel-type</th>\n",
       "      <th>aspiration</th>\n",
       "      <th>num-of-doors</th>\n",
       "      <th>body-style</th>\n",
       "      <th>drive-wheels</th>\n",
       "      <th>engine-location</th>\n",
       "      <th>wheel-base</th>\n",
       "      <th>...</th>\n",
       "      <th>engine-size</th>\n",
       "      <th>fuel-system</th>\n",
       "      <th>bore</th>\n",
       "      <th>stroke</th>\n",
       "      <th>compression-ratio</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>peak-rpm</th>\n",
       "      <th>city-mpg</th>\n",
       "      <th>highway-mpg</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>205.000000</td>\n",
       "      <td>164.000000</td>\n",
       "      <td>205</td>\n",
       "      <td>205</td>\n",
       "      <td>205</td>\n",
       "      <td>205</td>\n",
       "      <td>205</td>\n",
       "      <td>205</td>\n",
       "      <td>205</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>205</td>\n",
       "      <td>201.000000</td>\n",
       "      <td>201.000000</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>203.000000</td>\n",
       "      <td>203.000000</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>201.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>toyota</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32</td>\n",
       "      <td>185</td>\n",
       "      <td>168</td>\n",
       "      <td>114</td>\n",
       "      <td>96</td>\n",
       "      <td>120</td>\n",
       "      <td>202</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>94</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.834146</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>98.756585</td>\n",
       "      <td>...</td>\n",
       "      <td>126.907317</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.329751</td>\n",
       "      <td>3.255423</td>\n",
       "      <td>10.142537</td>\n",
       "      <td>104.256158</td>\n",
       "      <td>5125.369458</td>\n",
       "      <td>25.219512</td>\n",
       "      <td>30.751220</td>\n",
       "      <td>13207.129353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.245307</td>\n",
       "      <td>35.442168</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.021776</td>\n",
       "      <td>...</td>\n",
       "      <td>41.642693</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.273539</td>\n",
       "      <td>0.316717</td>\n",
       "      <td>3.972040</td>\n",
       "      <td>39.714369</td>\n",
       "      <td>479.334560</td>\n",
       "      <td>6.542142</td>\n",
       "      <td>6.886443</td>\n",
       "      <td>7947.066342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-2.000000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>86.600000</td>\n",
       "      <td>...</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.540000</td>\n",
       "      <td>2.070000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>4150.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>5118.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>94.500000</td>\n",
       "      <td>...</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.150000</td>\n",
       "      <td>3.110000</td>\n",
       "      <td>8.600000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>4800.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>7775.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>115.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.310000</td>\n",
       "      <td>3.290000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>5200.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>10295.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>102.400000</td>\n",
       "      <td>...</td>\n",
       "      <td>141.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.590000</td>\n",
       "      <td>3.410000</td>\n",
       "      <td>9.400000</td>\n",
       "      <td>116.000000</td>\n",
       "      <td>5500.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>16500.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>256.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>120.900000</td>\n",
       "      <td>...</td>\n",
       "      <td>326.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.940000</td>\n",
       "      <td>4.170000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>288.000000</td>\n",
       "      <td>6600.000000</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>45400.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         symboling  normalized-losses    make fuel-type aspiration  \\\n",
       "count   205.000000         164.000000     205       205        205   \n",
       "unique         NaN                NaN      22         2          2   \n",
       "top            NaN                NaN  toyota       gas        std   \n",
       "freq           NaN                NaN      32       185        168   \n",
       "mean      0.834146         122.000000     NaN       NaN        NaN   \n",
       "std       1.245307          35.442168     NaN       NaN        NaN   \n",
       "min      -2.000000          65.000000     NaN       NaN        NaN   \n",
       "25%       0.000000          94.000000     NaN       NaN        NaN   \n",
       "50%       1.000000         115.000000     NaN       NaN        NaN   \n",
       "75%       2.000000         150.000000     NaN       NaN        NaN   \n",
       "max       3.000000         256.000000     NaN       NaN        NaN   \n",
       "\n",
       "       num-of-doors body-style drive-wheels engine-location  wheel-base  \\\n",
       "count           205        205          205             205  205.000000   \n",
       "unique            3          5            3               2         NaN   \n",
       "top            four      sedan          fwd           front         NaN   \n",
       "freq            114         96          120             202         NaN   \n",
       "mean            NaN        NaN          NaN             NaN   98.756585   \n",
       "std             NaN        NaN          NaN             NaN    6.021776   \n",
       "min             NaN        NaN          NaN             NaN   86.600000   \n",
       "25%             NaN        NaN          NaN             NaN   94.500000   \n",
       "50%             NaN        NaN          NaN             NaN   97.000000   \n",
       "75%             NaN        NaN          NaN             NaN  102.400000   \n",
       "max             NaN        NaN          NaN             NaN  120.900000   \n",
       "\n",
       "            ...       engine-size  fuel-system        bore      stroke  \\\n",
       "count       ...        205.000000          205  201.000000  201.000000   \n",
       "unique      ...               NaN            8         NaN         NaN   \n",
       "top         ...               NaN         mpfi         NaN         NaN   \n",
       "freq        ...               NaN           94         NaN         NaN   \n",
       "mean        ...        126.907317          NaN    3.329751    3.255423   \n",
       "std         ...         41.642693          NaN    0.273539    0.316717   \n",
       "min         ...         61.000000          NaN    2.540000    2.070000   \n",
       "25%         ...         97.000000          NaN    3.150000    3.110000   \n",
       "50%         ...        120.000000          NaN    3.310000    3.290000   \n",
       "75%         ...        141.000000          NaN    3.590000    3.410000   \n",
       "max         ...        326.000000          NaN    3.940000    4.170000   \n",
       "\n",
       "       compression-ratio  horsepower     peak-rpm    city-mpg  highway-mpg  \\\n",
       "count         205.000000  203.000000   203.000000  205.000000   205.000000   \n",
       "unique               NaN         NaN          NaN         NaN          NaN   \n",
       "top                  NaN         NaN          NaN         NaN          NaN   \n",
       "freq                 NaN         NaN          NaN         NaN          NaN   \n",
       "mean           10.142537  104.256158  5125.369458   25.219512    30.751220   \n",
       "std             3.972040   39.714369   479.334560    6.542142     6.886443   \n",
       "min             7.000000   48.000000  4150.000000   13.000000    16.000000   \n",
       "25%             8.600000   70.000000  4800.000000   19.000000    25.000000   \n",
       "50%             9.000000   95.000000  5200.000000   24.000000    30.000000   \n",
       "75%             9.400000  116.000000  5500.000000   30.000000    34.000000   \n",
       "max            23.000000  288.000000  6600.000000   49.000000    54.000000   \n",
       "\n",
       "               price  \n",
       "count     201.000000  \n",
       "unique           NaN  \n",
       "top              NaN  \n",
       "freq             NaN  \n",
       "mean    13207.129353  \n",
       "std      7947.066342  \n",
       "min      5118.000000  \n",
       "25%      7775.000000  \n",
       "50%     10295.000000  \n",
       "75%     16500.000000  \n",
       "max     45400.000000  \n",
       "\n",
       "[11 rows x 26 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.describe(include = \"all\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "metadata": false,
     "name": "#%% md\n"
    }
   },
   "source": [
    "\n",
    "## deal with NaN\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "metadata": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(160, 26)\n"
     ]
    }
   ],
   "source": [
    "# dealing with NaN data, here we remove NaN lines in price column\n",
    "df2.dropna(subset = [\"price\"], axis = 0, inplace = True)\n",
    "df2.dropna(axis = 0, inplace = True)\n",
    "print(df2.shape)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "metadata": false,
     "name": "#%% md\n"
    }
   },
   "source": [
    "\n",
    "## save data to a csv again\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "metadata": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>symboling</th>\n",
       "      <th>normalized-losses</th>\n",
       "      <th>make</th>\n",
       "      <th>fuel-type</th>\n",
       "      <th>aspiration</th>\n",
       "      <th>num-of-doors</th>\n",
       "      <th>body-style</th>\n",
       "      <th>drive-wheels</th>\n",
       "      <th>engine-location</th>\n",
       "      <th>wheel-base</th>\n",
       "      <th>...</th>\n",
       "      <th>engine-size</th>\n",
       "      <th>fuel-system</th>\n",
       "      <th>bore</th>\n",
       "      <th>stroke</th>\n",
       "      <th>compression-ratio</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>peak-rpm</th>\n",
       "      <th>city-mpg</th>\n",
       "      <th>highway-mpg</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>160.000000</td>\n",
       "      <td>160.000000</td>\n",
       "      <td>160</td>\n",
       "      <td>160</td>\n",
       "      <td>160</td>\n",
       "      <td>160</td>\n",
       "      <td>160</td>\n",
       "      <td>160</td>\n",
       "      <td>160</td>\n",
       "      <td>160.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>160.000000</td>\n",
       "      <td>160</td>\n",
       "      <td>160.000000</td>\n",
       "      <td>160.000000</td>\n",
       "      <td>160.000000</td>\n",
       "      <td>160.000000</td>\n",
       "      <td>160.000000</td>\n",
       "      <td>160.000000</td>\n",
       "      <td>160.000000</td>\n",
       "      <td>160.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>toyota</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31</td>\n",
       "      <td>145</td>\n",
       "      <td>132</td>\n",
       "      <td>95</td>\n",
       "      <td>80</td>\n",
       "      <td>106</td>\n",
       "      <td>160</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>65</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.737500</td>\n",
       "      <td>121.300000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>98.235625</td>\n",
       "      <td>...</td>\n",
       "      <td>119.093750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.298438</td>\n",
       "      <td>3.237312</td>\n",
       "      <td>10.145125</td>\n",
       "      <td>95.875000</td>\n",
       "      <td>5116.250000</td>\n",
       "      <td>26.506250</td>\n",
       "      <td>32.068750</td>\n",
       "      <td>11427.681250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.189511</td>\n",
       "      <td>35.602417</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.163763</td>\n",
       "      <td>...</td>\n",
       "      <td>30.411186</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.267348</td>\n",
       "      <td>0.294210</td>\n",
       "      <td>3.882507</td>\n",
       "      <td>30.625708</td>\n",
       "      <td>465.290536</td>\n",
       "      <td>6.081208</td>\n",
       "      <td>6.440948</td>\n",
       "      <td>5863.789011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-2.000000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>86.600000</td>\n",
       "      <td>...</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.540000</td>\n",
       "      <td>2.070000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>4150.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>5118.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>94.500000</td>\n",
       "      <td>...</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.050000</td>\n",
       "      <td>3.107500</td>\n",
       "      <td>8.700000</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>4800.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>7383.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>114.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>96.900000</td>\n",
       "      <td>...</td>\n",
       "      <td>110.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.270000</td>\n",
       "      <td>3.270000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>5200.000000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>9164.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>148.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100.600000</td>\n",
       "      <td>...</td>\n",
       "      <td>134.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.550000</td>\n",
       "      <td>3.410000</td>\n",
       "      <td>9.400000</td>\n",
       "      <td>114.000000</td>\n",
       "      <td>5500.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>14559.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>256.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>115.600000</td>\n",
       "      <td>...</td>\n",
       "      <td>258.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.940000</td>\n",
       "      <td>4.170000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>6600.000000</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>35056.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         symboling  normalized-losses    make fuel-type aspiration  \\\n",
       "count   160.000000         160.000000     160       160        160   \n",
       "unique         NaN                NaN      18         2          2   \n",
       "top            NaN                NaN  toyota       gas        std   \n",
       "freq           NaN                NaN      31       145        132   \n",
       "mean      0.737500         121.300000     NaN       NaN        NaN   \n",
       "std       1.189511          35.602417     NaN       NaN        NaN   \n",
       "min      -2.000000          65.000000     NaN       NaN        NaN   \n",
       "25%       0.000000          94.000000     NaN       NaN        NaN   \n",
       "50%       1.000000         114.000000     NaN       NaN        NaN   \n",
       "75%       2.000000         148.000000     NaN       NaN        NaN   \n",
       "max       3.000000         256.000000     NaN       NaN        NaN   \n",
       "\n",
       "       num-of-doors body-style drive-wheels engine-location  wheel-base  \\\n",
       "count           160        160          160             160  160.000000   \n",
       "unique            3          5            3               1         NaN   \n",
       "top            four      sedan          fwd           front         NaN   \n",
       "freq             95         80          106             160         NaN   \n",
       "mean            NaN        NaN          NaN             NaN   98.235625   \n",
       "std             NaN        NaN          NaN             NaN    5.163763   \n",
       "min             NaN        NaN          NaN             NaN   86.600000   \n",
       "25%             NaN        NaN          NaN             NaN   94.500000   \n",
       "50%             NaN        NaN          NaN             NaN   96.900000   \n",
       "75%             NaN        NaN          NaN             NaN  100.600000   \n",
       "max             NaN        NaN          NaN             NaN  115.600000   \n",
       "\n",
       "            ...       engine-size  fuel-system        bore      stroke  \\\n",
       "count       ...        160.000000          160  160.000000  160.000000   \n",
       "unique      ...               NaN            6         NaN         NaN   \n",
       "top         ...               NaN         mpfi         NaN         NaN   \n",
       "freq        ...               NaN           65         NaN         NaN   \n",
       "mean        ...        119.093750          NaN    3.298438    3.237312   \n",
       "std         ...         30.411186          NaN    0.267348    0.294210   \n",
       "min         ...         61.000000          NaN    2.540000    2.070000   \n",
       "25%         ...         97.000000          NaN    3.050000    3.107500   \n",
       "50%         ...        110.000000          NaN    3.270000    3.270000   \n",
       "75%         ...        134.500000          NaN    3.550000    3.410000   \n",
       "max         ...        258.000000          NaN    3.940000    4.170000   \n",
       "\n",
       "       compression-ratio  horsepower     peak-rpm    city-mpg  highway-mpg  \\\n",
       "count         160.000000  160.000000   160.000000  160.000000   160.000000   \n",
       "unique               NaN         NaN          NaN         NaN          NaN   \n",
       "top                  NaN         NaN          NaN         NaN          NaN   \n",
       "freq                 NaN         NaN          NaN         NaN          NaN   \n",
       "mean           10.145125   95.875000  5116.250000   26.506250    32.068750   \n",
       "std             3.882507   30.625708   465.290536    6.081208     6.440948   \n",
       "min             7.000000   48.000000  4150.000000   15.000000    18.000000   \n",
       "25%             8.700000   69.000000  4800.000000   23.000000    28.000000   \n",
       "50%             9.000000   88.000000  5200.000000   26.000000    32.000000   \n",
       "75%             9.400000  114.000000  5500.000000   31.000000    37.000000   \n",
       "max            23.000000  200.000000  6600.000000   49.000000    54.000000   \n",
       "\n",
       "               price  \n",
       "count     160.000000  \n",
       "unique           NaN  \n",
       "top              NaN  \n",
       "freq             NaN  \n",
       "mean    11427.681250  \n",
       "std      5863.789011  \n",
       "min      5118.000000  \n",
       "25%      7383.500000  \n",
       "50%      9164.000000  \n",
       "75%     14559.250000  \n",
       "max     35056.000000  \n",
       "\n",
       "[11 rows x 26 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.to_csv(\"./data/cars_data_02.csv\", index = False)\n",
    "df3 = pd.read_csv(\"./data/cars_data_02.csv\")\n",
    "df3.dtypes\n",
    "df3.describe(include = \"all\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "metadata": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>length</th>\n",
       "      <th>width</th>\n",
       "      <th>height</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>160.000000</td>\n",
       "      <td>160.000000</td>\n",
       "      <td>160.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>172.319375</td>\n",
       "      <td>65.596250</td>\n",
       "      <td>53.878750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>11.548860</td>\n",
       "      <td>1.946999</td>\n",
       "      <td>2.276608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>141.100000</td>\n",
       "      <td>60.300000</td>\n",
       "      <td>49.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>165.525000</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>52.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>172.200000</td>\n",
       "      <td>65.400000</td>\n",
       "      <td>54.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>177.800000</td>\n",
       "      <td>66.500000</td>\n",
       "      <td>55.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>202.600000</td>\n",
       "      <td>71.700000</td>\n",
       "      <td>59.800000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           length       width      height\n",
       "count  160.000000  160.000000  160.000000\n",
       "mean   172.319375   65.596250   53.878750\n",
       "std     11.548860    1.946999    2.276608\n",
       "min    141.100000   60.300000   49.400000\n",
       "25%    165.525000   64.000000   52.000000\n",
       "50%    172.200000   65.400000   54.100000\n",
       "75%    177.800000   66.500000   55.500000\n",
       "max    202.600000   71.700000   59.800000"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3[[\"length\", \"width\", \"height\"]].describe(include = \"all\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "metadata": false,
     "name": "#%% md\n"
    }
   },
   "source": [
    "\n",
    "## centering and scalling with Z-score\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "metadata": false,
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "df3[[\"length\"]] = (df3[[\"length\"]] - df3[[\"length\"]].mean())/df3[[\"length\"]].std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "metadata": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 160 entries, 0 to 159\n",
      "Data columns (total 1 columns):\n",
      "length    160 non-null float64\n",
      "dtypes: float64(1)\n",
      "memory usage: 1.3 KB\n"
     ]
    }
   ],
   "source": [
    "df3[[\"length\"]].info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "metadata": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "symboling              int64\n",
       "normalized-losses    float64\n",
       "make                  object\n",
       "fuel-type             object\n",
       "aspiration            object\n",
       "num-of-doors          object\n",
       "body-style            object\n",
       "drive-wheels          object\n",
       "engine-location       object\n",
       "wheel-base           float64\n",
       "length               float64\n",
       "width                float64\n",
       "height               float64\n",
       "curb-weight            int64\n",
       "engine-type           object\n",
       "num-of-cylinders      object\n",
       "engine-size            int64\n",
       "fuel-system           object\n",
       "bore                 float64\n",
       "stroke               float64\n",
       "compression-ratio    float64\n",
       "horsepower           float64\n",
       "peak-rpm             float64\n",
       "city-mpg               int64\n",
       "highway-mpg            int64\n",
       "price                float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "metadata": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(160, 26)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df3)\n",
    "df3.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "metadata": false
    }
   },
   "source": [
    "\n",
    "## binning\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "metadata": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  5118.        ,  15097.33333333,  25076.66666667,  35056.        ])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bins = np.linspace(min(df3[\"price\"]), max(df3[\"price\"]), 4)\n",
    "group_names = [\"Low\", \"Medium\", \"High\"]\n",
    "df3[\"price_binned\"] = pd.cut(df3[\"price\"], bins, labels=group_names, include_lowest=True)\n",
    "\n",
    "bins"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "inputHidden": false,
    "outputHidden": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "\n",
    "## turning categorical variables into quantitative variables\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "metadata": false,
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "pd.get_dummies(df3[\"fuel-type\"])\n",
    "df3 = df3.append(pd.get_dummies(df3[\"fuel-type\"]))\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "metadata": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>aspiration</th>\n",
       "      <th>body-style</th>\n",
       "      <th>bore</th>\n",
       "      <th>city-mpg</th>\n",
       "      <th>compression-ratio</th>\n",
       "      <th>curb-weight</th>\n",
       "      <th>diesel</th>\n",
       "      <th>drive-wheels</th>\n",
       "      <th>engine-location</th>\n",
       "      <th>engine-size</th>\n",
       "      <th>...</th>\n",
       "      <th>normalized-losses</th>\n",
       "      <th>num-of-cylinders</th>\n",
       "      <th>num-of-doors</th>\n",
       "      <th>peak-rpm</th>\n",
       "      <th>price</th>\n",
       "      <th>price_binned</th>\n",
       "      <th>stroke</th>\n",
       "      <th>symboling</th>\n",
       "      <th>wheel-base</th>\n",
       "      <th>width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>std</td>\n",
       "      <td>sedan</td>\n",
       "      <td>3.19</td>\n",
       "      <td>24.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2337.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>109.0</td>\n",
       "      <td>...</td>\n",
       "      <td>164.0</td>\n",
       "      <td>four</td>\n",
       "      <td>four</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>13950.0</td>\n",
       "      <td>Low</td>\n",
       "      <td>3.4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>99.8</td>\n",
       "      <td>66.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>std</td>\n",
       "      <td>sedan</td>\n",
       "      <td>3.19</td>\n",
       "      <td>18.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2824.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4wd</td>\n",
       "      <td>front</td>\n",
       "      <td>136.0</td>\n",
       "      <td>...</td>\n",
       "      <td>164.0</td>\n",
       "      <td>five</td>\n",
       "      <td>four</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>17450.0</td>\n",
       "      <td>Medium</td>\n",
       "      <td>3.4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>99.4</td>\n",
       "      <td>66.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>std</td>\n",
       "      <td>sedan</td>\n",
       "      <td>3.19</td>\n",
       "      <td>19.0</td>\n",
       "      <td>8.5</td>\n",
       "      <td>2844.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>136.0</td>\n",
       "      <td>...</td>\n",
       "      <td>158.0</td>\n",
       "      <td>five</td>\n",
       "      <td>four</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>17710.0</td>\n",
       "      <td>Medium</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>105.8</td>\n",
       "      <td>71.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  aspiration body-style  bore  city-mpg  compression-ratio  curb-weight  \\\n",
       "0        std      sedan  3.19      24.0               10.0       2337.0   \n",
       "1        std      sedan  3.19      18.0                8.0       2824.0   \n",
       "2        std      sedan  3.19      19.0                8.5       2844.0   \n",
       "\n",
       "   diesel drive-wheels engine-location  engine-size  ...   normalized-losses  \\\n",
       "0     NaN          fwd           front        109.0  ...               164.0   \n",
       "1     NaN          4wd           front        136.0  ...               164.0   \n",
       "2     NaN          fwd           front        136.0  ...               158.0   \n",
       "\n",
       "  num-of-cylinders num-of-doors  peak-rpm    price  price_binned  stroke  \\\n",
       "0             four         four    5500.0  13950.0           Low     3.4   \n",
       "1             five         four    5500.0  17450.0        Medium     3.4   \n",
       "2             five         four    5500.0  17710.0        Medium     3.4   \n",
       "\n",
       "   symboling wheel-base  width  \n",
       "0        2.0       99.8   66.2  \n",
       "1        2.0       99.4   66.4  \n",
       "2        1.0      105.8   71.4  \n",
       "\n",
       "[3 rows x 29 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.head(3)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false,
    "inputHidden": false,
    "outputHidden": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "aspiration            object\n",
       "body-style            object\n",
       "bore                 float64\n",
       "city-mpg             float64\n",
       "compression-ratio    float64\n",
       "curb-weight          float64\n",
       "diesel               float64\n",
       "drive-wheels          object\n",
       "engine-location       object\n",
       "engine-size          float64\n",
       "engine-type           object\n",
       "fuel-system           object\n",
       "fuel-type             object\n",
       "gas                  float64\n",
       "height               float64\n",
       "highway-mpg          float64\n",
       "horsepower           float64\n",
       "length               float64\n",
       "make                  object\n",
       "normalized-losses    float64\n",
       "num-of-cylinders      object\n",
       "num-of-doors          object\n",
       "peak-rpm             float64\n",
       "price                float64\n",
       "price_binned          object\n",
       "stroke               float64\n",
       "symboling            float64\n",
       "wheel-base           float64\n",
       "width                float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## analyse data "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false,
    "inputHidden": false,
    "outputHidden": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fwd    106\n",
      "rwd     46\n",
      "4wd      8\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "drive_wheels_counts = df3[\"drive-wheels\"].value_counts()\n",
    "\n",
    "\n",
    "# change the name of the column\n",
    "drive_wheels_counts.rename(columns={\"drive-wheels\":\"value_counts\"}, inplace=True)\n",
    "print(drive_wheels_counts)\n",
    "\n",
    "drive_wheels_counts.index.name = \"drive-wheels\"\n",
    "drive_wheels_counts.to_csv(\"./data/drive_wheel_counts.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## display a box-plot using seeaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false,
    "inputHidden": false,
    "outputHidden": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAY4AAAEFCAYAAAD0cwBnAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAG1tJREFUeJzt3X2UVfV97/H3MMOzM2RsBmm8iS5N8wF7Y0MpD71IQxsSH5LKSo2sFmPpylr4cL0Xc3OLNoAV7yXaSEIjdhVSTBSL6dL6EA0JQh6qIo0PMZroBb4Kty5t7nJ1hIEZHgdm5v6x9+DJOHPmbDl7zhzm81qLxT6/8zv7/PYZOJ/57d/ev19NV1cXZmZmpRpW6QaYmVl1cXCYmVkmDg4zM8vEwWFmZpk4OMzMLJO6Sjcgb83Nbb5szMwso6am+pq+nnOPw8zMMnFwmJlZJg4OMzPLxMFhZmaZODjMzCwTB4eZWUY7d25n587tlW5GxZzyl+OamZXbo48+BMDEiedVuCWVkVtwSKoF1gECuoBrgOHARuC1tNqaiLhf0kLgauA4sCIiNkoaDWwAxgNtwIKIaJY0A7gjrbslIm7J6xjMzHrauXM7ETtObA/F8MjzVNUfA0TETGAZ8BVgCrAqImanf+6XNAFYBMwELgRukzQSuBZ4OSJmAfem+wBYC8wHLgCmS5qc4zGYmf2a7t5Gz+2hJLfgiIjvAlelD88C9pEEx6clPSXpW5LqgWnAtog4GhH7gV3A+STB8Hj6+k3AHEkNwMiI2B0RXcBmYE5ex2BmZu+W6xhHRByXtB74LPA54Ezgroh4QdJS4GbgJWB/wcvagHFAQ0F5YVlrj7rnFGtDY+MY6upqy3A0ZmawYMGVLFmy5MR2U1N9hVs08HIfHI+IBZJuBJ4F/ktE/Cp96hHgTuApoPCTryfpnbQWlPdWVljep5aWQyd7CGZmJ0yYcDbSpBPbzc1tFW5RPooFYm6nqiRdKenL6cNDQCfwsKRpadkngBeA54BZkkZJGgdMAl4BtgGXpHUvBrZGRCvQLulcSTUkYyJb8zoGM7PezJ17GXPnXlbpZlRMTV5rjksaC9wNTCC5mupvgDdJehnHgLeAqyKiNb2q6iqSILs1Ih6SNAZYD/wm0A7Mj4i30quqvgHUklxVtbRYOzw7rplZdsVmx80tOAYLB4eZWXaeVt3MzMrGwWFmZpk4OMzMLBMHh5mZZeLgMDOzTBwcZmaWiYPDzMwycXCYmVkmDg4zM8vEwWFmlpGXjjUzs0yG+tKx7nGYmWXQvXRsxI4h2+twcJiZZeClYx0cZmaWkYPDzCyDwgWchupiTh4cNzPLYOLE804sHTtUB8e9kJOZnbIeeOA+nn/+2bLvt7W1FYCGhoay7xtg6tTpzJt3RS77LlWxhZzc4zAzy6ij43ilm1BR7nGYmWW0ePEiAFauXF3hluTHS8eamVnZODjMzCwTB4eZmWXi4DAzs0wcHGZmlomDw8zMMsntPg5JtcA6QEAXcA1wBLgnffwKcF1EdEpaCFwNHAdWRMRGSaOBDcB4oA1YEBHNkmYAd6R1t0TELXkdg5mZvVuePY4/BoiImcAy4CvAKmBZRMwCaoC5kiYAi4CZwIXAbZJGAtcCL6d17033AbAWmA9cAEyXNDnHYzAzsx5yC46I+C5wVfrwLGAfMAV4Mi3bBMwBpgHbIuJoROwHdgHnkwTD44V1JTUAIyNid0R0AZvTfZiZ2QDJdcqRiDguaT3wWeBzwCfTL3xITj+NAxqA/QUv6628sKy1R91zirWhsXEMdXW1J3kkZmbvqK1NfuduaqqvcEsqI/e5qiJigaQbgWeB0QVP1ZP0QlrT7WLl/dXtU0vLoZNpvpnZu3R0dALQ3NxW4Zbkp1go5naqStKVkr6cPjwEdAI/kzQ7LbsY2Ao8B8ySNErSOGASycD5NuCSwroR0Qq0SzpXUg3JmMjWvI7BzMzeLc8ex8PA3ZKeAoYDXwR2AOskjUi3H4yIDkmrSQJgGLA0Io5IWgOsl/Q00E4yIA7J1Vn3AbUkV1WVf85kMzPrk2fHNTPLyLPjmpmZZeDgMDOzTBwcZmaWiYPDzMwycXCYmVkmDg4zM8vEwWFmZpk4OMzMLBMHh5mZZeLgMDOzTBwcZmaWiYPDzMwycXCYmVkmDg4zM8vEwWFmZpk4OMzMLBMHh5mZZeLgMDOzTBwcZmaWiYPDzMwycXCYmVkmDg4zM8vEwWFmZpk4OMzMLBMHh5mZZVKXx04lDQe+DZwNjARWAG8CG4HX0mprIuJ+SQuBq4HjwIqI2ChpNLABGA+0AQsiolnSDOCOtO6WiLglj/abmVnf8upxfB7YExGzgIuAvwOmAKsiYnb6535JE4BFwEzgQuA2SSOBa4GX09ffCyxL97sWmA9cAEyXNDmn9puZWR9y6XEA/ww8mG7XkPQQpgCSNJek1/FFYBqwLSKOAkcl7QLOJwmG29PXbwJuktQAjIyI3SQ72gzMAV7M6RjMzKwXuQRHRBwAkFRPEiDLSE5Z3RURL0haCtwMvATsL3hpGzAOaCgoLyxr7VH3nP7a0tg4hrq62pM6HjOzQrW1ycmapqb6CrekMvLqcSDpg8AjwN9HxHckvS8i9qVPPwLcCTwFFH7y9cA+koCoL1JWWF5US8uhkzkMM7N36ejoBKC5ua3CLclPsVDMZYxD0hnAFuDGiPh2WrxZ0rR0+xPAC8BzwCxJoySNAyYBrwDbgEvSuhcDWyOiFWiXdK6kGpIxka15tN/MzPqWV49jCdBIMjZxU1r2JeBvJR0D3gKuiohWSatJAmAYsDQijkhaA6yX9DTQTjIgDnANcB9QS3JV1bM5tb8q7Ny5HYCJE8+rcEvMbCjJa4zjeuD6Xp6a2UvddcC6HmWHgMt7qfsMMKNMzax6jz76EODgMLOB5RsAq9TOnduJ2EHEjhM9DzOzgeDgqFLdvY2e22ZmeXNwmJlZJg6OKjV37mW9bpuZ5S23+zgsXxMnnoc06cS2mdlAcXBUMfc0zKwSHBxVzD0NM6sEj3GYmVkmDg4zM8vEwWFmZpk4OMzMLBMHh5mZZeLgMDOzTBwcZmaWiYPDzMwycXCYmVkmDg4zM8vEwWFmZpmUPFeVpJnAR4G7gekR8VRurTIzs0GrpB6HpOuBFcCXgNOAb0r6yzwbZmZmg1Opp6r+ArgQOBgRe4CpwBfyapSZmQ1epQZHR0S0Fzw+AnTk0B4zMxvkarq6uvqtJOnrQBdwKXADcBXwakR8Md/mnbzm5rb+D9DMKurWW5fT0rK30s0oWXdbGxtPr3BLsmlsPJ0lS5aXVLepqb6mr+dKHRxfDCwEfgH8OfB94JslvtbMrKiWlr3s3fM2pw2rjgs9azs7AWivorA7kLa5HEoNjjFAXURcLulM4GpgBHC8bC0xsyHttGHD+Py46voNvpps2F++kCs1OL4D/DLdbiMZG/lHoNdFryUNB74NnA2MJLkiaztwD8kpr1eA6yKiU9JCkiA6DqyIiI2SRgMbgPHp+y2IiGZJM4A70rpbIuKWTEdrZmYnrdR+4VkRsQwgIlrT7XOL1P88sCciZgEXAX8HrAKWpWU1wFxJE4BFwEySq7ZukzQSuBZ4Oa17L7As3e9aYD5wATBd0uTSD9XMzMqh1ODokvTR7geSJgLHitT/Z+CmdLuGpIcwBXgyLdsEzAGmAdsi4mhE7Ad2AeeTBMPjhXUlNQAjI2J3RHQBm9N9mJnZACr1VNVfAj+U9O8kQfB+4Mq+KkfEAQBJ9cCDJD2Gr6Vf+JCcfhoHNAD7C17aW3lhWWuPuuf01/DGxjHU1dX2V83MKqi2tjoGxatdbe0wmprqT3o/JQVHRPxI0odIphw5lhTF0WKvkfRB4BHg7yPiO5JuL3i6HthHEgT1/ZT3V7eolpZD/VUxswrr6CjfFT/Wt46OTpqb20qqWyxgisa8pOXp33eTjC9cB3wRWCPp20VedwawBbgxIrrrvShpdrp9MbAVeA6YJWmUpHHAJJKB823AJYV1I6IVaJd0rqQakjGRrcXab2Zm5ddfj+OF9O8nMu53CdAI3CSpe6zjemC1pBHADuDBiOiQtJokAIYBSyPiiKQ1wHpJTwPtJAPiANcA9wG1JFdVPZuxXWZmdpJKvXN8S0R8agDaU3a+c9xs8Fu8eBHtLXt9H0eONuzfy4jG01m5cnVJ9YvdOV7qiNSodMzCBpGdO7ezc+f2SjfDzIaYUq+qGg+8Luk/gMPdhRHR71VNlp9HH30IgIkTz6twS8xsKCk1OC4FPg38Eck9GT8AfpxXo6x/O3duJ2LHiW2Hh5kNlFJPVS0FZgD/QDJtyEUkd3xbhXT3Nnpum5nlrdQex/SImNj9QNL3SC6bNTOzIabUHsebkj5c8PgM4Fc5tMdKNHnylF63zczyVmpwDAd+IWlT2tvYDpwp6SeSfpJf86wvL774Qq/bZmZ5K/VU1c09Hn+t3A0xG2q6L6X2hQ1WbUqdq+rJ/mvZQJo8ecqJq6p8qqo6+XJqq1aekrJK+VRVdeu+nDpih2/itKrj4DCrAF9ObdXMwVGl5s69rNdtM7O8OTiq1MSJ5yFNQprkc+RVyMFv1azUq6psEPIXTvXqDv7ubbNq4uCoYv7CqW4O/nccPHiQo52dbNi/t9JNOWUd6Oxk5MGDZdmXg8OsQhz8Vq0cHGZWcWPHjmV4+1Ev5JSjDfv3MmLs2LLsy4PjZmaWiYPDzMwycXCYmVkmDo4q5jXHzawSPDhexTxJnplVgnscVcqT5JlZpTg4qpQnyTOzSnFwmJlZJrmOcUiaDnw1ImZLmgxsBF5Ln14TEfdLWghcDRwHVkTERkmjgQ3AeKANWBARzZJmAHekdbdExC15tn8w80JOZlYpufU4JN0A3AWMSoumAKsiYnb6535JE4BFwEzgQuA2SSOBa4GXI2IWcC+wLN3HWmA+cAEwPQ2jIckLOZlZpeR5qmo38CcFj6cAn5b0lKRvSaoHpgHbIuJoROwHdgHnkwTD4+nrNgFzJDUAIyNid0R0AZuBOTm238zMepHbqaqIeEjS2QVFzwF3RcQLkpYCNwMvAfsL6rQB44CGgvLCstYedc/prx2NjWOoq6t9r4cxaC1YcCVLliw5sd3UVF/hFpm9d7W1Hm4dCLW1w8ryXTGQ93E8EhH7ureBO4GngMKjqAf2kQREfZGywvKiWloOnVyrB6kJE84+sZ7DhAln09zcVuEWmb13HR2dlW7CkNDR0Vnyd0WxgBnImN8saVq6/QngBZJeyCxJoySNAyYBrwDbgEvSuhcDWyOiFWiXdK6kGpIxka0D2P5BZ+7cy7ymg5kNuIHscVwL3CnpGPAWcFVEtEpaTRIAw4ClEXFE0hpgvaSngXaSAXGAa4D7gFqSq6qeHcD2v2cPPHAfzz9f/qYeTBdlGVumqZILTZ06nXnzrij7fs2s+uUaHBHxOjAj3f45ydVTPeusA9b1KDsEXN5L3We692fQ3n4UyCc4zMz64rmqBsC8eVfk8tv74sWLAFi5cnXZ921m1hdfymBmZpk4OMzMLBMHh5mZZeLgMDOzTDw4bmaDwoHOTjbs31vpZpTkSGdyw+KoYdXzu/eBzk5OL9O+HBxmVnGNjeX6ShsYB1uSgBtRRe0+nfJ9zg4OM6u4JUuWV7oJmQz1S+Grp59lZmaDgoPDzMwycXCYmVkmDg4zM8vEwWFmZpk4OMzMLBMHh5mZZeL7OMyKyGsRLvBCXFa93OMwq5D29qMnFuMyqybucZgVkdciXOC7j616OTjslHDrrctpaamOCfK6dbe3O0CqQWPj6VU3PYiVn4PDTgktLXvZs/dtho2unn/SncO6AGg5vK/CLSlN5+HjlW6CDRLV87/MrB/DRtfReNGHKt2MU1bL429Uugk2SHhw3MzMMnFwmJlZJg4OMzPLxMFhZmaZ5Do4Lmk68NWImC3pw8A9QBfwCnBdRHRKWghcDRwHVkTERkmjgQ3AeKANWBARzZJmAHekdbdExC15tt/MzN4ttx6HpBuAu4BRadEqYFlEzAJqgLmSJgCLgJnAhcBtkkYC1wIvp3XvBZal+1gLzAcuAKZLmpxX+83MrHd59jh2A38C/GP6eArwZLq9CfgU0AFsi4ijwFFJu4DzSYLh9oK6N0lqAEZGxG4ASZuBOcCL5WisbyAbOL6JzKy65RYcEfGQpLMLimoioivdbgPGAQ3A/oI6vZUXlrX2qHtOf+1obBxDXV1tv+1tbd3Hnj17qBk+ut+6g0VX2mHc23qowi0pXdexw9TWDqOpqb6s+62t9XDdQMjjZ1eNuv+9DdXPYiBvAOws2K4H9pEEQX0/5f3VLaqlpbQv1Y6OTmqGj+a0D19aUn17bw7seoyOjk6am9vKut+Ojs7+K9lJy+NnV426/72dyp9FsVAcyF/TXpQ0O92+GNgKPAfMkjRK0jhgEsnA+TbgksK6EdEKtEs6V1INyZjI1gFsv5mZMbA9jv8JrJM0AtgBPBgRHZJWkwTAMGBpRByRtAZYL+lpoJ1kQBzgGuA+oJbkqqp8FkowM7M+5RocEfE6MCPdfhX4eC911gHrepQdAi7vpe4z3fszM7PK8CSHdko4ePAgnUePeyK+HHUePs7BzoOVboYNAr4UxczMMnGPw04JY8eOpX3YMU+rnqOWx99g7Ojyr49u1cc9DjMzy8TBYWZmmTg4zMwsEweHmZll4sHx1MGDB+k6doQDux6rdFNOaV3HDnPwYFf/Fd+DzsPVdTluZ3sHAMNG9D+X2mDQefg4VM9UbgA88MB9PP98+e8TznuC0alTpzNv3hW57LscHBx2SmhsPL3STcis5Ujy5dM4+n0VbkmJRlfn55yHESNGVroJFVXT1ZXPb3+DRXNzW0kHuHjxIva2HvIkhzk7sOsxTm8Yw8qVqyvdlIrr/m3Vn4UNRk1N9TV9Peceh1kReZ3qgHxPdwz2Ux1W3RwcZhUy1E93WPVycJgVMW/eFf7N3awHX45rZmaZuMdRoOvY4aq6HLerox2AmtoRFW5J6bqOHQbGVLoZZnYSHByparzMsKXlCACNDdX0RTymKj9rM3uHL8etYr6c08zyUuxyXI9xmJlZJg4OMzPLxMFhZmaZODjMzCwTB4eZmWXi4DAzs0wcHGZmlomDw8zMMhnwO8cl/RxoTR/+G/AV4B6gC3gFuC4iOiUtBK4GjgMrImKjpNHABmA80AYsiIjmAT4EM7MhbUDvHJc0CvhpREwuKHsMWBURT0haC2wGfgr8EPg9YBTwdLp9HdAQEcsl/Snw+xFxfbH3HAx3jue9fGUeU3h4PQezoW0wLeT0O8AYSVvS914CTAGeTJ/fBHwK6AC2RcRR4KikXcD5wAXA7QV1bxrAtg86Xs/BzCphoIPjEPA14C7gt0i+/GsiortX0AaMAxqA/QWv6628u6yoxsYx1NXVlqXx79V1110DXFPRNpiZlctAB8erwK40KF6VtIekx9GtHthHMgZS3095d1lRLS2HytBsM7Ohpampvs/nBvqqqi8AXweQ9AGSHsQWSbPT5y8GtgLPAbMkjZI0DphEMnC+DbikR10zMxtAAz04PoLkCqoPkVxFdSPwNrAOGAHsABZGREd6VdVVJOF2a0Q8JGkMsB74TaAdmB8RbxV7z8EwOG5mVm2KDY57PQ4zM3sXr8dhZmZl4+AwM7NMHBxmZpaJg8PMzDI55QfHzcysvNzjMDOzTBwcZmaWiYPDzMwycXCYmVkmDg4zM8vEwWFmZpk4OMzMLBMHxyAnqU7Sv0j6V0mNJb6m6IzBNrAkjZf0pqSJGV4zUdITOTbLciLpGknLK92OPA30Qk6W3QdI1lmf0m9NG3QkDQe+CRyudFvMysXBMfitBT4iaTcgoAn4d2A8cAD4KTAV+Afgt4HdgBcjHzy+RvIz/DIwUtLGiPiMpD8FlkTE+ZJmAguAm4H7gBrAvcZBQNJfkCxA91HgbyLiq5LWAu0RsUjSUuDfgDeAO4AW4DjwTIWaPCB8qmrw+6/A/wGeBH4fuIhkNcRPpH+2AJ8FRkXEDJIvqDGVaaoVSr90miNic1p0FDhL0kiSFSw7JZ0BzAUeBpYC/xQRfwh8twJNtt61AL8DfDJ9LGB6un0RsBFYA/xZRMwhCZJTmoOjejxMsmzuhSRfMJ8ELgUeAj5CstwuEfEG8GaF2mi/7gvAJ9Oxio8B9wLPAn8IfJCkdzEHmAX8mIKfI8kyyTY4RPr/aoykaSQrlTZLmgrsj4hW4IyIeDWtf8r/7Bwc1eOHwMeB9wM/AKYAH4uI54HtJL2R7rXcz6xUI+0dEfEHEfHxiJgNvAT8OXA38FfAL4HNwH8HdkXEMQp+jiSnH21w6Ez//j5wO0kvfwtwJ/BI+tyvJE1Kt0/5n52Do0pExFGSnsTPI6ITCJLfXgEeBfZIehb4Bsk67jY4/ZTkVMeWiPgl8CGS3iTACuCzaQ/l0so0z4p4GJgJ/IQk9H+P5P8ewNXAvZJ+DJxVmeYNHE+rbmZmmbjHYWZmmTg4zMwsEweHmZll4uAwM7NMHBxmZpaJg8OsB0n3pHd9F5ZdKul/DcB7Ly/HBHmSzpb0+kk3yKwXnqvKrAQR8RjwWKXbYTYYODhsyJNUA3wd+Azw/4Ba4AlJO0lupjwCbABmk9wEdlVEfCZ97X8jmSrkfwAr0zq1wD0R8bc93qcJ+EVEfCB9/CvgSxFxv6S/4p07lKdJ+leSGQDujojlkmr72n/62nlp+Wbgxh7vOx+4AeggmUfp8xFx5OQ+NRvKfKrKDC4DJpPMLnw58OG0XCRfsnMK6m4CfrdgbZQ/IwmVhQAR8bvANGCupFmFbxIRzcAbkv5zujZHHck0MpBMergx3T6DZD6rKcBiSfV97V/SRWm9qekxnAlc0eP4VgCfSqfm3wmUvC6IWW8cHGZpTyIijqVf7j9Iy/8jIl4vrJjOKfUwcJmks4DfiIjnSCYrvFTSSyRTwfwnkqm4e/o+yazGf0QyDfcsSeOACRGxPa2zKSKORsTbJD2e04vsfw7JTK0vAD8nmQbjt3u85/eAbZJWAhsj4qWsH5BZIZ+qMoMufv2XqOPp330tvrQB+N9AI/CdtKwWuCEiHgaQ9H7gYDqg3j3v1F+ThNJyktNfN5GcYppPcoqp5/t3t62mr/0DtwLfiIhVafn70te/v3sHEXG9pG8BnwY2SFoeERuKfyRmfXOPwwx+BFwuaWR6CuqiYpUj4hmSlRmvJAkRSCa+WyhpuKTTgKeB6RHx1xHxsfTPYyS9go8AH4mIncC/AMt45zRVX3rdf1p+paTTJNWRrOPxue4XpUsPvwa8HRG3kUztPrnUD8asNw4OG/Ii4lHgCZIFsh4jmd68P/cDByLi/6aP1wKvAS8CPyMZ1H6il/fqIvnS35EW/QRoSN+/mF73HxHfI1mT5dm0/S8B6wve7zhJT+dHkn4G/AGwqoTjM+uTZ8c1M7NM3OMwM7NMHBxmZpaJg8PMzDJxcJiZWSYODjMzy8TBYWZmmTg4zMwsk/8P0ueZ8ouzeSQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x257aacb1518>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sea.boxplot(x = \"drive-wheels\", y = \"price\", data = df3)\n",
    "\n",
    "df3.to_csv(\"./data/cars_data_df3.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false,
    "inputHidden": false,
    "outputHidden": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x257a9003320>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x257a8eb9cf8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = df3[\"price\"]\n",
    "y = df3[\"engine-size\"]\n",
    "plt.scatter(x,y)\n",
    "plt.title(\"The Title of The Scatterplot\")\n",
    "plt.xlabel(\"the label of x axis\")\n",
    "plt.ylabel(\"the label of y axis\")"
   ]
  }
 ],
 "metadata": {
  "kernel_info": {
   "name": "python3"
  },
  "kernelspec": {
   "display_name": "Python [default]",
   "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": "3.5.2"
  },
  "nteract": {
   "version": "nteract-on-jupyter@2.0.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}