Data-Wrangling.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>Data Wrangling</h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>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>3.40</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>3.40</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    3.40              10.0        102      5500       24   \n",
       "4         mpfi  3.19    3.40               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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pylab as plt\n",
    "filename = \"https://archive.ics.uci.edu/ml/machine-learning-databases/autos/imports-85.data\"\n",
    "\n",
    "headers = [\"symboling\",\"normalized-losses\",\"make\",\"fuel-type\",\"aspiration\", \"num-of-doors\",\"body-style\",\n",
    "         \"drive-wheels\",\"engine-location\",\"wheel-base\", \"length\",\"width\",\"height\",\"curb-weight\",\"engine-type\",\n",
    "         \"num-of-cylinders\", \"engine-size\",\"fuel-system\",\"bore\",\"stroke\",\"compression-ratio\",\"horsepower\",\n",
    "         \"peak-rpm\",\"city-mpg\",\"highway-mpg\",\"price\"]\n",
    "\n",
    "df = pd.read_csv(filename, names = headers)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Steps for working with missing data:</b>\n",
    "\n",
    "<ol>\n",
    "    <li>Identify missing data</li>\n",
    "    <li>Deal with missing data</li>\n",
    "    <li>Correct data format</li>\n",
    "</ol>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3 id=\"identify_missing_values\">Identify missing values</h3>\n",
    "<h4>Convert \"?\" to NaN</h4>\n",
    "In the car dataset, missing data comes with the question mark \"?\".\n",
    "We replace \"?\" with NaN (Not a Number), which is Python's default missing value marker for reasons of computational speed and convenience."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>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>NaN</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>NaN</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>NaN</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>3.40</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>3.40</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               NaN  alfa-romero       gas        std          two   \n",
       "1          3               NaN  alfa-romero       gas        std          two   \n",
       "2          1               NaN  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    3.40              10.0        102      5500       24   \n",
       "4         mpfi  3.19    3.40               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": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "df.replace(\"?\", np.nan, inplace = True)\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>Evaluating for Missing Data</h4>\n",
    "\n",
    "The missing values are converted to Python's default. We use Python's built-in functions to identify these missing values. There are two methods to detect missing data:\n",
    "<ol>\n",
    "    <li><b>.isnull()</b></li>\n",
    "    <li><b>.notnull()</b></li>\n",
    "</ol>\n",
    "The output is a boolean value indicating whether the value that is passed into the argument is in fact missing data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>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>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</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      False               True  False      False       False         False   \n",
       "1      False               True  False      False       False         False   \n",
       "2      False               True  False      False       False         False   \n",
       "3      False              False  False      False       False         False   \n",
       "4      False              False  False      False       False         False   \n",
       "\n",
       "   body-style  drive-wheels  engine-location  wheel-base  ...    engine-size  \\\n",
       "0       False         False            False       False  ...          False   \n",
       "1       False         False            False       False  ...          False   \n",
       "2       False         False            False       False  ...          False   \n",
       "3       False         False            False       False  ...          False   \n",
       "4       False         False            False       False  ...          False   \n",
       "\n",
       "   fuel-system   bore  stroke  compression-ratio  horsepower  peak-rpm  \\\n",
       "0        False  False   False              False       False     False   \n",
       "1        False  False   False              False       False     False   \n",
       "2        False  False   False              False       False     False   \n",
       "3        False  False   False              False       False     False   \n",
       "4        False  False   False              False       False     False   \n",
       "\n",
       "   city-mpg  highway-mpg  price  \n",
       "0     False        False  False  \n",
       "1     False        False  False  \n",
       "2     False        False  False  \n",
       "3     False        False  False  \n",
       "4     False        False  False  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_data = df.isnull()\n",
    "missing_data.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\"True\" stands for missing value, while \"False\" stands for not missing value."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>Count missing values in each column</h4>\n",
    "<p>\n",
    "Using a for loop in Python, we can quickly figure out the number of missing values in each column.\"True\" represents a missing value, \"False\"  means the value is present in the dataset.  In the body of the for loop the method  \".value_counts()\"  counts the number of \"True\" values. \n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "symboling\n",
      "False    205\n",
      "Name: symboling, dtype: int64\n",
      "\n",
      "normalized-losses\n",
      "False    164\n",
      "True      41\n",
      "Name: normalized-losses, dtype: int64\n",
      "\n",
      "make\n",
      "False    205\n",
      "Name: make, dtype: int64\n",
      "\n",
      "fuel-type\n",
      "False    205\n",
      "Name: fuel-type, dtype: int64\n",
      "\n",
      "aspiration\n",
      "False    205\n",
      "Name: aspiration, dtype: int64\n",
      "\n",
      "num-of-doors\n",
      "False    203\n",
      "True       2\n",
      "Name: num-of-doors, dtype: int64\n",
      "\n",
      "body-style\n",
      "False    205\n",
      "Name: body-style, dtype: int64\n",
      "\n",
      "drive-wheels\n",
      "False    205\n",
      "Name: drive-wheels, dtype: int64\n",
      "\n",
      "engine-location\n",
      "False    205\n",
      "Name: engine-location, dtype: int64\n",
      "\n",
      "wheel-base\n",
      "False    205\n",
      "Name: wheel-base, dtype: int64\n",
      "\n",
      "length\n",
      "False    205\n",
      "Name: length, dtype: int64\n",
      "\n",
      "width\n",
      "False    205\n",
      "Name: width, dtype: int64\n",
      "\n",
      "height\n",
      "False    205\n",
      "Name: height, dtype: int64\n",
      "\n",
      "curb-weight\n",
      "False    205\n",
      "Name: curb-weight, dtype: int64\n",
      "\n",
      "engine-type\n",
      "False    205\n",
      "Name: engine-type, dtype: int64\n",
      "\n",
      "num-of-cylinders\n",
      "False    205\n",
      "Name: num-of-cylinders, dtype: int64\n",
      "\n",
      "engine-size\n",
      "False    205\n",
      "Name: engine-size, dtype: int64\n",
      "\n",
      "fuel-system\n",
      "False    205\n",
      "Name: fuel-system, dtype: int64\n",
      "\n",
      "bore\n",
      "False    201\n",
      "True       4\n",
      "Name: bore, dtype: int64\n",
      "\n",
      "stroke\n",
      "False    201\n",
      "True       4\n",
      "Name: stroke, dtype: int64\n",
      "\n",
      "compression-ratio\n",
      "False    205\n",
      "Name: compression-ratio, dtype: int64\n",
      "\n",
      "horsepower\n",
      "False    203\n",
      "True       2\n",
      "Name: horsepower, dtype: int64\n",
      "\n",
      "peak-rpm\n",
      "False    203\n",
      "True       2\n",
      "Name: peak-rpm, dtype: int64\n",
      "\n",
      "city-mpg\n",
      "False    205\n",
      "Name: city-mpg, dtype: int64\n",
      "\n",
      "highway-mpg\n",
      "False    205\n",
      "Name: highway-mpg, dtype: int64\n",
      "\n",
      "price\n",
      "False    201\n",
      "True       4\n",
      "Name: price, dtype: int64\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for column in missing_data.columns.values.tolist():\n",
    "    print(column)\n",
    "    print(missing_data[column].value_counts())\n",
    "    print(\"\")    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each column has 205 rows of data and 7 columns containing missing data:\n",
    "<ol>\n",
    "    <li>\"normalized-losses\": 41 missing data</li>\n",
    "    <li>\"num-of-doors\": 2 missing data</li>\n",
    "    <li>\"bore\": 4 missing data</li>\n",
    "    <li>\"stroke\" : 4 missing data</li>\n",
    "    <li>\"horsepower\": 2 missing data</li>\n",
    "    <li>\"peak-rpm\": 2 missing data</li>\n",
    "    <li>\"price\": 4 missing data</li>\n",
    "</ol>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "<h3 id=\"deal_missing_values\">Deal with missing data</h3>\n",
    "<ol>\n",
    "    <li>drop data<br>\n",
    "        a. drop the whole row<br>\n",
    "        b. drop the whole column\n",
    "    </li>\n",
    "    <li>replace data<br>\n",
    "        a. replace it by mean<br>\n",
    "        b. replace it by frequency<br>\n",
    "        c. replace it based on other functions\n",
    "    </li>\n",
    "</ol>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Whole columns should be dropped only if most entries in the column are empty. In our dataset, none of the columns are empty enough to drop entirely.\n",
    "We have some freedom in choosing which method to replace data; however, some methods may seem more reasonable than others. We will apply each method to many different columns:\n",
    "\n",
    "<b>Replace by mean:</b>\n",
    "<ul>\n",
    "    <li>\"normalized-losses\": 41 missing data, replace them with mean</li>\n",
    "    <li>\"stroke\": 4 missing data, replace them with mean</li>\n",
    "    <li>\"bore\": 4 missing data, replace them with mean</li>\n",
    "    <li>\"horsepower\": 2 missing data, replace them with mean</li>\n",
    "    <li>\"peak-rpm\": 2 missing data, replace them with mean</li>\n",
    "</ul>\n",
    "\n",
    "<b>Replace by frequency:</b>\n",
    "<ul>\n",
    "    <li>\"num-of-doors\": 2 missing data, replace them with \"four\". \n",
    "        <ul>\n",
    "            <li>Reason: 84% sedans is four doors. Since four doors is most frequent, it is most likely to occur</li>\n",
    "        </ul>\n",
    "    </li>\n",
    "</ul>\n",
    "\n",
    "<b>Drop the whole row:</b>\n",
    "<ul>\n",
    "    <li>\"price\": 4 missing data, simply delete the whole row\n",
    "        <ul>\n",
    "            <li>Reason: price is what we want to predict. Any data entry without price data cannot be used for prediction; therefore any row now without price data is not useful to us</li>\n",
    "        </ul>\n",
    "    </li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>Calculate the average of the columns : \"normalized-losses\", 'bore', 'horsepower' and 'peak-rpm'  </h4>\n",
    "<h4>Replace \"NaN\" by mean value in that particular column</h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average of normalized-losses: 122.0\n"
     ]
    }
   ],
   "source": [
    "avg_norm_loss = df[\"normalized-losses\"].astype(\"float\").mean(axis=0)\n",
    "print(\"Average of normalized-losses:\", avg_norm_loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"normalized-losses\"].replace(np.nan,avg_norm_loss,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average of Bore Values: 3.3297512437810957\n"
     ]
    }
   ],
   "source": [
    "avg_bore = df[\"bore\"].astype(\"float\").mean(axis=0)\n",
    "print(\"Average of Bore Values:\",avg_bore)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"bore\"].replace(np.nan,avg_bore,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average of Stroke Values: 3.3297512437810957\n"
     ]
    }
   ],
   "source": [
    "avg_stroke = df[\"stroke\"].astype(\"float\").mean(axis=0)\n",
    "print(\"Average of Stroke Values:\",avg_bore)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"stroke\"].replace(np.nan,avg_stroke,inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average horsepower: 104.25615763546799\n"
     ]
    }
   ],
   "source": [
    "avg_horsepower = df['horsepower'].astype(\"float\").mean(axis=0)\n",
    "print(\"Average horsepower:\", avg_horsepower)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['horsepower'].replace(np.nan, avg_horsepower, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average peak rpm: 5125.369458128079\n"
     ]
    }
   ],
   "source": [
    "avg_peakrpm=df['peak-rpm'].astype('float').mean(axis=0)\n",
    "print(\"Average peak rpm:\", avg_peakrpm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['peak-rpm'].replace(np.nan, avg_peakrpm, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To see which values are present in a particular column,use the \".value_counts()\" method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "four    114\n",
       "two      89\n",
       "Name: num-of-doors, dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['num-of-doors'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that four doors are the most common type. We can also use the \".idxmax()\" method to calculate for us the most common type automatically:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'four'"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['num-of-doors'].value_counts().idxmax()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "#replace the missing 'num-of-doors' values by the most frequent \n",
    "df['num-of-doors'].replace(np.nan,\"four\",inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, let's drop all rows that do not have price data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# simply drop whole row with NaN in \"price\" column\n",
    "df.dropna(subset=[\"price\"],axis=0,inplace=True)\n",
    "\n",
    "# reset index, because we droped two rows\n",
    "df.reset_index(drop=True,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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>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>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",
       "      <th>city-L/100km</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>122</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>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",
       "      <td>11.190476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>122</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>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",
       "      <td>11.190476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>122</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>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",
       "      <td>12.368421</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>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</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",
       "      <td>9.791667</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>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</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",
       "      <td>13.055556</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   symboling normalized-losses         make fuel-type aspiration num-of-doors  \\\n",
       "0          3               122  alfa-romero       gas        std          two   \n",
       "1          3               122  alfa-romero       gas        std          two   \n",
       "2          1               122  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     ...       \\\n",
       "0  convertible          rwd           front        88.6     ...        \n",
       "1  convertible          rwd           front        88.6     ...        \n",
       "2    hatchback          rwd           front        94.5     ...        \n",
       "3        sedan          fwd           front        99.8     ...        \n",
       "4        sedan          4wd           front        99.4     ...        \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    3.40               10.0        102     5500        24   \n",
       "4         mpfi  3.19    3.40                8.0        115     5500        18   \n",
       "\n",
       "  highway-mpg  price city-L/100km  \n",
       "0          27  13495    11.190476  \n",
       "1          27  16500    11.190476  \n",
       "2          26  16500    12.368421  \n",
       "3          30  13950     9.791667  \n",
       "4          22  17450    13.055556  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, the dataset with no missing values is obtained."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>Lets list the data types for each column</h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "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",
       "city-L/100km         float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>Some columns are not of the correct data type. Numerical variables should have type 'float' or 'int', and variables with strings such as categories should have type 'object'. For example, 'bore' and 'stroke' variables are numerical values that describe the engines, so we should expect them to be of the type 'float' or 'int'; however, they are shown as type 'object'. We have to convert data types into a proper format for each column using the \"astype()\" method.</p> "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>Convert data types to proper format</h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[[\"bore\", \"stroke\"]] = df[[\"bore\", \"stroke\"]].astype(\"float\")\n",
    "df[[\"normalized-losses\"]] = df[[\"normalized-losses\"]].astype(\"int\")\n",
    "df[[\"price\"]] = df[[\"price\"]].astype(\"float\")\n",
    "df[[\"peak-rpm\"]] = df[[\"peak-rpm\"]].astype(\"float\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>Columns after the conversion</h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "symboling              int64\n",
       "normalized-losses      int32\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            object\n",
       "peak-rpm             float64\n",
       "city-mpg               int64\n",
       "highway-mpg            int64\n",
       "price                float64\n",
       "city-L/100km         float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally the cleaned dataset is obtained with no missing values and all data in its proper format."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 id=\"data_standardization\">Data Standardization</h2>\n",
    "<p>\n",
    "Data is usually collected from different agencies with different formats.\n",
    "(Data Standardization is also a term for a particular type of data normalization, where we subtract the mean and divide by the standard deviation)\n",
    "</p>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>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>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",
       "      <th>city-L/100km</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>122</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>mpfi</td>\n",
       "      <td>3.47</td>\n",
       "      <td>2.68</td>\n",
       "      <td>9.0</td>\n",
       "      <td>111</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>13495.0</td>\n",
       "      <td>11.190476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>122</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>mpfi</td>\n",
       "      <td>3.47</td>\n",
       "      <td>2.68</td>\n",
       "      <td>9.0</td>\n",
       "      <td>111</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>16500.0</td>\n",
       "      <td>11.190476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>122</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>mpfi</td>\n",
       "      <td>2.68</td>\n",
       "      <td>3.47</td>\n",
       "      <td>9.0</td>\n",
       "      <td>154</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>19</td>\n",
       "      <td>26</td>\n",
       "      <td>16500.0</td>\n",
       "      <td>12.368421</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>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>10.0</td>\n",
       "      <td>102</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>24</td>\n",
       "      <td>30</td>\n",
       "      <td>13950.0</td>\n",
       "      <td>9.791667</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>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>8.0</td>\n",
       "      <td>115</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>18</td>\n",
       "      <td>22</td>\n",
       "      <td>17450.0</td>\n",
       "      <td>13.055556</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   symboling  normalized-losses         make fuel-type aspiration  \\\n",
       "0          3                122  alfa-romero       gas        std   \n",
       "1          3                122  alfa-romero       gas        std   \n",
       "2          1                122  alfa-romero       gas        std   \n",
       "3          2                164         audi       gas        std   \n",
       "4          2                164         audi       gas        std   \n",
       "\n",
       "  num-of-doors   body-style drive-wheels engine-location  wheel-base  \\\n",
       "0          two  convertible          rwd           front        88.6   \n",
       "1          two  convertible          rwd           front        88.6   \n",
       "2          two    hatchback          rwd           front        94.5   \n",
       "3         four        sedan          fwd           front        99.8   \n",
       "4         four        sedan          4wd           front        99.4   \n",
       "\n",
       "       ...       fuel-system  bore  stroke  compression-ratio horsepower  \\\n",
       "0      ...              mpfi  3.47    2.68                9.0        111   \n",
       "1      ...              mpfi  3.47    2.68                9.0        111   \n",
       "2      ...              mpfi  2.68    3.47                9.0        154   \n",
       "3      ...              mpfi  3.19    3.40               10.0        102   \n",
       "4      ...              mpfi  3.19    3.40                8.0        115   \n",
       "\n",
       "  peak-rpm  city-mpg highway-mpg    price  city-L/100km  \n",
       "0   5000.0        21          27  13495.0     11.190476  \n",
       "1   5000.0        21          27  16500.0     11.190476  \n",
       "2   5000.0        19          26  16500.0     12.368421  \n",
       "3   5500.0        24          30  13950.0      9.791667  \n",
       "4   5500.0        18          22  17450.0     13.055556  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>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",
       "      <th>city-L/100km</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>122</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>mpfi</td>\n",
       "      <td>3.47</td>\n",
       "      <td>2.68</td>\n",
       "      <td>9.0</td>\n",
       "      <td>111</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>13495.0</td>\n",
       "      <td>11.190476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>122</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>mpfi</td>\n",
       "      <td>3.47</td>\n",
       "      <td>2.68</td>\n",
       "      <td>9.0</td>\n",
       "      <td>111</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>16500.0</td>\n",
       "      <td>11.190476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>122</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>mpfi</td>\n",
       "      <td>2.68</td>\n",
       "      <td>3.47</td>\n",
       "      <td>9.0</td>\n",
       "      <td>154</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>19</td>\n",
       "      <td>26</td>\n",
       "      <td>16500.0</td>\n",
       "      <td>12.368421</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>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>10.0</td>\n",
       "      <td>102</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>24</td>\n",
       "      <td>30</td>\n",
       "      <td>13950.0</td>\n",
       "      <td>9.791667</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>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>8.0</td>\n",
       "      <td>115</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>18</td>\n",
       "      <td>22</td>\n",
       "      <td>17450.0</td>\n",
       "      <td>13.055556</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   symboling  normalized-losses         make fuel-type aspiration  \\\n",
       "0          3                122  alfa-romero       gas        std   \n",
       "1          3                122  alfa-romero       gas        std   \n",
       "2          1                122  alfa-romero       gas        std   \n",
       "3          2                164         audi       gas        std   \n",
       "4          2                164         audi       gas        std   \n",
       "\n",
       "  num-of-doors   body-style drive-wheels engine-location  wheel-base  \\\n",
       "0          two  convertible          rwd           front        88.6   \n",
       "1          two  convertible          rwd           front        88.6   \n",
       "2          two    hatchback          rwd           front        94.5   \n",
       "3         four        sedan          fwd           front        99.8   \n",
       "4         four        sedan          4wd           front        99.4   \n",
       "\n",
       "       ...       fuel-system  bore  stroke  compression-ratio horsepower  \\\n",
       "0      ...              mpfi  3.47    2.68                9.0        111   \n",
       "1      ...              mpfi  3.47    2.68                9.0        111   \n",
       "2      ...              mpfi  2.68    3.47                9.0        154   \n",
       "3      ...              mpfi  3.19    3.40               10.0        102   \n",
       "4      ...              mpfi  3.19    3.40                8.0        115   \n",
       "\n",
       "  peak-rpm  city-mpg highway-mpg    price  city-L/100km  \n",
       "0   5000.0        21          27  13495.0     11.190476  \n",
       "1   5000.0        21          27  16500.0     11.190476  \n",
       "2   5000.0        19          26  16500.0     12.368421  \n",
       "3   5500.0        24          30  13950.0      9.791667  \n",
       "4   5500.0        18          22  17450.0     13.055556  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"city-L/100km\"] = 235 / df[\"city-mpg\"]\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "df[\"highway-mpg\"] = 235/df[\"highway-mpg\"]\n",
    "df.rename(columns={'\"highway-mpg\"':'highway-L/100km'}, inplace=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 id=\"data_normalization\">Data Normalization</h2>\n",
    "\n",
    "<b>Why normalization?</b>\n",
    "<p>Normalization is the process of transforming values of several variables into a similar range. Typical normalizations include scaling the variable so the variable average is 0, scaling the variable so the variance is 1, or scaling variable so the variable values range from 0 to 1\n",
    "</p>\n"
   ]
  },
  {
   "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>length</th>\n",
       "      <th>width</th>\n",
       "      <th>height</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.811148</td>\n",
       "      <td>0.890278</td>\n",
       "      <td>0.816054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.811148</td>\n",
       "      <td>0.890278</td>\n",
       "      <td>0.816054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.822681</td>\n",
       "      <td>0.909722</td>\n",
       "      <td>0.876254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.848630</td>\n",
       "      <td>0.919444</td>\n",
       "      <td>0.908027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.848630</td>\n",
       "      <td>0.922222</td>\n",
       "      <td>0.908027</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     length     width    height\n",
       "0  0.811148  0.890278  0.816054\n",
       "1  0.811148  0.890278  0.816054\n",
       "2  0.822681  0.909722  0.876254\n",
       "3  0.848630  0.919444  0.908027\n",
       "4  0.848630  0.922222  0.908027"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# replace (original value) by (original value)/(maximum value)\n",
    "df[\"length\"] = df[\"length\"]/df[\"length\"].max()\n",
    "df[\"width\"] = df[\"width\"]/df[\"width\"].max()\n",
    "df['height'] = df['height']/df['height'].max() \n",
    "df[[\"length\",\"width\",\"height\"]].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we can see, we've normalized \"length\", \"width\" and \"height\" in the range of [0,1]."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 id=\"binning\">Binning</h2>\n",
    "\n",
    "<p>\n",
    "    Binning is a process of transforming continuous numerical variables into discrete categorical 'bins', for grouped analysis.\n",
    "</p>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"horsepower\"] = df[\"horsepower\"].astype(int,copy=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the histogram of horspower in order to see what the distribution of horsepower looks like."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'HORSEPOWER BINS')"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "plt.hist(df[\"horsepower\"])\n",
    "\n",
    "plt.xlabel(\"horsepower\")\n",
    "plt.ylabel(\"count\")\n",
    "plt.title(\"HORSEPOWER BINS\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>3 bins of equal size bandwidth so we use numpy's <code>linspace(start_value, end_value, numbers_generated</code> function.\n",
    "<p>Since we are building 3 bins of equal length, there should be 4 dividers, so numbers_generated=4.</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 48.        , 119.33333333, 190.66666667, 262.        ])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bins = np.linspace(min(df[\"horsepower\"]),max(df[\"horsepower\"]),4)\n",
    "bins"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " We set group  names:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "group_names = ['Low', 'Medium', 'High']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " We apply the function \"cut\" the determine what each value of \"df['horsepower']\" belongs to. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\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>horsepower</th>\n",
       "      <th>horsepower-binned</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>111</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>111</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>154</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>102</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>115</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>110</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>110</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>110</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>140</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>101</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>101</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>121</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>121</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>121</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>182</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>182</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>182</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>48</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>70</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>70</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    horsepower horsepower-binned\n",
       "0          111               Low\n",
       "1          111               Low\n",
       "2          154            Medium\n",
       "3          102               Low\n",
       "4          115               Low\n",
       "5          110               Low\n",
       "6          110               Low\n",
       "7          110               Low\n",
       "8          140            Medium\n",
       "9          101               Low\n",
       "10         101               Low\n",
       "11         121            Medium\n",
       "12         121            Medium\n",
       "13         121            Medium\n",
       "14         182            Medium\n",
       "15         182            Medium\n",
       "16         182            Medium\n",
       "17          48               Low\n",
       "18          70               Low\n",
       "19          70               Low"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['horsepower-binned'] = pd.cut(df['horsepower'], bins, labels=group_names ,include_lowest = True)\n",
    "df[['horsepower','horsepower-binned']].head(20)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets see the number of vehicles in each bin."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Low       153\n",
       "Medium     43\n",
       "High        5\n",
       "Name: horsepower-binned, dtype: int64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"horsepower-binned\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets plot the distribution of each bin."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'horsepower bins')"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYgAAAEWCAYAAAB8LwAVAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAGStJREFUeJzt3XmUXnWd5/H3B1ARQQKmUEyAII1Oo8OgBkQdkJZuxWUMxxVGMSJ2xKFRR1GxnVZbpRuXadtt5MQGARcQF4RpbZVGIdgKGhRBwCWDLAGEQllcQeA7f9xfyUNxk1RinnoqqffrnOfUvb/7u8/9Vj2n6lN3+91UFZIkTbbJqAuQJM1MBoQkqZcBIUnqZUBIknoZEJKkXgaEJKmXAaEZKcmVSf5y1HXMdEn2S7JyNcuPS/J301mTNh6bjboAScNTVYePugZtuNyD0EYtyUbzT9DG9L1ow2BAaCbbI8nFSW5N8pkkm08sSPLXSVYk+WWSM5M8fGBZJTkiyU+Bn6bz/iQ3tve6OMljWt8HJHlfkquT3NAOyTywLdsvycokf5vkpnbY68UD29k6yclJxpNcleR/JdmkLbsqyePb9EtaTbu1+Vck+WKb3iTJ0Un+X5JfJDktybZt2YK23mFJrga+vqof1GpqPDHJuyZ9P69vP4vrkxw60PeZSS5L8qsk1yY56k/58LThMyA0k70QOADYGdgdeBlAkqcC/9iWbw9cBZw6ad0DgScAuwFPA/YFHgnMAV4E/KL1e3dr3wP4M2Ae8NaB93kYMLe1LwaWJnlUW/YhYGvgEcBTgJcCE39wzwX2a9P7Ale0PhPz57bpV7danwI8HLgZ+Mik7+UpwJ8DT+/5Ga2pxr6+W7e+hwEfSbJNW3Y88Mqq2gp4DKsJJM0SVeXL14x7AVcCLxmYfw9wXJs+HnjPwLItgT8AC9p8AU8dWP5U4CfA3sAmA+0BfgPsMtD2ROBnbXo/4E7gQQPLTwP+DtgUuB3YbWDZK4Fz2vRhwJlt+nLgFcCpbf4q4HEDy/YfeI/t2/eyGbCgfS+PWM3PaZU1tukTgXcN9P0dsNlA3xuBvdv01e17ePCoP39fM+PlHoRmsp8PTP+WLgig+0/7qokFVfVruj2CeQP9rxlY/nXgw3T/md+QZGmSBwNjwBbAhUluSXIL8JXWPuHmqvrNwPxVbftzgfsP1tGmJ2o4F9gnycPowuQzwJOTLKD7D/6i1m8n4PSB7V8O3AU8tO97WYVV1djnF1V158D84M/1ecAzgauSnJvkiWvYrjZyBoQ2RNfR/WEFIMmDgIcA1w70udcwxVX1wap6PPBoukNKbwBuovuP+tFVNae9tq6qLQdW3aa9/4Qd2/ZvovtPf6dJy65t21tB98f31cCyqvoVXeAtAb5ZVXe3da4BnjGw/TlVtXlVrfJ76bGqGtdKVX23qhYB2wFfpNsT0SxmQGhD9Gng0CR7JHkA8A/ABVV1ZV/nJHsmeUKS+9EdUvo9cFf7I/0x4P1Jtmt95yWZfKz/75PcP8k+wLOBz1bVXXR/QI9JslWSnYDXAZ8cWO9c4G+453zDOZPmAY5r77FT2/5YkkXr8DO5T41rs3Jb98VJtq6qPwC30e3JaBYzILTBqaqz6c4DfB64HtgFOGg1qzyYLghupjv88gvgfW3Zm4AVwPlJbgP+HRg8wfvztt51wKeAw6vqR23ZkXSBcwXwTbrgOmFg3XOBrYBlq5gH+ABwJvC1JL8Czqc7ub42Vlfj2jgEuLL9HA4HXrIO76GNSKp8YJDUJ8l+wCerav6oa5FGwT0ISVIvA0KS1GtoAZHkhHa35g8ntR+Z5MdJLk3ynoH2N7c7Y3/cc5JQmnZVdY6HlzSbDXNslxPprj0/eaIhyV8Ai4Ddq+r2gStHdqM7yfhouuu3/z3JI9uVIpKkERhaQFTVsnZT0KBXAcdW1e2tz42tfRHdXaa3Az9LsgLYC/j26rYxd+7cWrBg8iYkSatz4YUX3lRVY2vqN92jQz6S7u7SY+iuRT+qqr5Ld/fp+QP9VnLvu2L/KMkSupuN2HHHHVm+fPlwK5akjUySq9bca/pPUm8GbEM3Js4bgNOShG5MnMl6r7+tqqVVtbCqFo6NrTEAJUnraLoDYiXwhep8B7ibbkyblcAOA/3msw5DBUiS1p/pDogv0o2sSZJH0g12dhPdnaQHtbH5dwZ2Bb4zzbVJkgYM7RxEklPohheem+6ZuW+jG4bghHbp6x3A4upu5b40yWnAZXRDFx/hFUySNFob9FAbCxcuLE9SS9LaSXJhVS1cUz/vpJYk9TIgJEm9DAhJUi8DQpLUa7rvpJ4xFhz9pVGXsNG68thnjboESeuBexCSpF4GhCSplwEhSeplQEiSehkQkqReBoQkqZcBIUnqZUBIknoZEJKkXgaEJKmXASFJ6mVASJJ6GRCSpF5DC4gkJyS5sT1/evKyo5JUkrltPkk+mGRFkouTPG5YdUmSpmaYexAnAgdMbkyyA/BXwNUDzc8Adm2vJcBHh1iXJGkKhhYQVbUM+GXPovcDbwRqoG0RcHJ1zgfmJNl+WLVJktZsWs9BJHkOcG1V/WDSonnANQPzK1tb33ssSbI8yfLx8fEhVSpJmraASLIF8BbgrX2Le9qqp42qWlpVC6tq4djY2PosUZI0YDofOboLsDPwgyQA84HvJdmLbo9hh4G+84HrprE2SdIk07YHUVWXVNV2VbWgqhbQhcLjqurnwJnAS9vVTHsDt1bV9dNVmyTpvoZ5mespwLeBRyVZmeSw1XT/MnAFsAL4GPA/hlWXJGlqhnaIqaoOXsPyBQPTBRwxrFokSWvPO6klSb0MCElSLwNCktTLgJAk9TIgJEm9DAhJUi8DQpLUy4CQJPUyICRJvQwISVIvA0KS1MuAkCT1MiAkSb0MCElSLwNCktTLgJAk9TIgJEm9DAhJUq9hPpP6hCQ3JvnhQNt7k/woycVJTk8yZ2DZm5OsSPLjJE8fVl2SpKkZ5h7EicABk9rOAh5TVbsDPwHeDJBkN+Ag4NFtnf+TZNMh1iZJWoOhBURVLQN+Oanta1V1Z5s9H5jfphcBp1bV7VX1M2AFsNewapMkrdkoz0G8HPi3Nj0PuGZg2crWdh9JliRZnmT5+Pj4kEuUpNlrJAGR5C3AncCnJpp6ulXfulW1tKoWVtXCsbGxYZUoSbPeZtO9wSSLgWcD+1fVRAisBHYY6DYfuG66a5Mk3WNa9yCSHAC8CXhOVf12YNGZwEFJHpBkZ2BX4DvTWZsk6d6GtgeR5BRgP2BukpXA2+iuWnoAcFYSgPOr6vCqujTJacBldIeejqiqu4ZVmyRpzYYWEFV1cE/z8avpfwxwzLDqkSStHe+kliT1MiAkSb0MCElSLwNCktTLgJAk9TIgJEm9DAhJUi8DQpLUy4CQJPUyICRJvQwISVIvA0KS1MuAkCT1MiAkSb0MCElSLwNCktTLgJAk9TIgJEm9hhYQSU5IcmOSHw60bZvkrCQ/bV+3ae1J8sEkK5JcnORxw6pLkjQ1w9yDOBE4YFLb0cDZVbUrcHabB3gGsGt7LQE+OsS6JElTMLSAqKplwC8nNS8CTmrTJwEHDrSfXJ3zgTlJth9WbZKkNZvucxAPrarrAdrX7Vr7POCagX4rW9t9JFmSZHmS5ePj40MtVpJms5lykjo9bdXXsaqWVtXCqlo4NjY25LIkafaa7oC4YeLQUft6Y2tfCeww0G8+cN001yZJGjDdAXEmsLhNLwbOGGh/abuaaW/g1olDUZKk0dhsWG+c5BRgP2BukpXA24BjgdOSHAZcDbygdf8y8ExgBfBb4NBh1SVJmpqhBURVHbyKRfv39C3giGHVIklaezPlJLUkaYYxICRJvQwISVIvA0KS1MuAkCT1MiAkSb0MCElSLwNCktTLgJAk9TIgJEm9phQQSc6eSpskaeOx2rGYkmwObEE34N423PPchgcDDx9ybZKkEVrTYH2vBF5LFwYXck9A3AZ8ZIh1SZJGbLUBUVUfAD6Q5Miq+tA01SRJmgGmNNx3VX0oyZOABYPrVNXJQ6pLkjRiUwqIJJ8AdgEuAu5qzQUYEJK0kZrqA4MWAru1B/tIkmaBqd4H8UPgYcMsRJI0s0x1D2IucFmS7wC3TzRW1XPWZaNJ/ifwCrrDVJfQPYN6e+BUYFvge8AhVXXHury/JOlPN9WAePv62mCSecCr6Q5Z/S7JacBBwDOB91fVqUmOAw4DPrq+titJWjtTvYrp3CFs94FJ/kB3I971wFOB/96Wn0QXSgaEJI3IVIfa+FWS29rr90nuSnLbumywqq4F3gdcTRcMt9LdhHdLVd3Zuq0E5q3L+0uS1o+p7kFsNTif5EBgr3XZYBuyYxGwM3AL8FngGX2bXcX6S4AlADvuuOO6lCBJmoJ1Gs21qr5Id0hoXfwl8LOqGq+qPwBfAJ4EzEkyEVjzgetWse2lVbWwqhaOjY2tYwmSpDWZ6o1yzx2Y3YTuvoh1vSfiamDvJFsAvwP2B5YD3wCeT3cl02LgjHV8f0nSejDVq5j+28D0ncCVdIeJ1lpVXZDkc3SXst4JfB9YCnwJODXJu1rb8evy/pKk9WOq5yAOXZ8braq3AW+b1HwF63heQ5K0/k31Kqb5SU5PcmOSG5J8Psn8YRcnSRqdqZ6k/jhwJt1zIeYB/7e1SZI2UlMNiLGq+nhV3dleJwJeQiRJG7GpBsRNSV6SZNP2egnwi2EWJkkarakGxMuBFwI/p7v7+fl0A+xJkjZSU73M9Z3A4qq6GSDJtnTDZbx8WIVJkkZrqnsQu0+EA0BV/RJ47HBKkiTNBFMNiE3aGErAH/cgprr3IUnaAE31j/z/Br7V7oAuuvMRxwytKknSyE31TuqTkyynG6AvwHOr6rKhViZJGqkpHyZqgWAoSNIssU7DfUuSNn4GhCSplwEhSeplQEiSehkQkqReBoQkqZcBIUnqNZKASDInyeeS/CjJ5UmemGTbJGcl+Wn7us2a30mSNCyj2oP4APCVqvpPwH8BLgeOBs6uql2Bs9u8JGlEpj0gkjwY2Bc4HqCq7qiqW4BFwEmt20nAgdNdmyTpHqPYg3gEMA58PMn3k/xLkgcBD62q6wHa1+1GUJskqRlFQGwGPA74aFU9FvgNa3E4KcmSJMuTLB8fHx9WjZI0640iIFYCK6vqgjb/ObrAuCHJ9gDt6419K1fV0qpaWFULx8bGpqVgSZqNpj0gqurnwDVJHtWa9qcbJfZMYHFrWwycMd21SZLuMaqnwh0JfCrJ/YErgEPpwuq0JIcBVwMvGFFtkiRGFBBVdRGwsGfR/tNdiySpn3dSS5J6GRCSpF4GhCSplwEhSeplQEiSehkQkqReBoQkqZcBIUnqZUBIknoZEJKkXgaEJKmXASFJ6mVASJJ6GRCSpF4GhCSplwEhSeplQEiSehkQkqReo3omNUk2BZYD11bVs5PsDJwKbAt8Dzikqu4YVX2aeRYc/aVRl7DRuvLYZ426BM1Ao9yDeA1w+cD8u4H3V9WuwM3AYSOpSpIEjCggkswHngX8S5sP8FTgc63LScCBo6hNktQZ1R7EPwNvBO5u8w8BbqmqO9v8SmDeKAqTJHWmPSCSPBu4saouHGzu6VqrWH9JkuVJlo+Pjw+lRknSaPYgngw8J8mVdCeln0q3RzEnycRJ8/nAdX0rV9XSqlpYVQvHxsamo15JmpWmPSCq6s1VNb+qFgAHAV+vqhcD3wCe37otBs6Y7tokSfeYSfdBvAl4XZIVdOckjh9xPZI0q43sPgiAqjoHOKdNXwHsNcp6JEn3mEl7EJKkGcSAkCT1MiAkSb0MCElSLwNCktTLgJAk9TIgJEm9DAhJUi8DQpLUy4CQJPUyICRJvQwISVIvA0KS1MuAkCT1MiAkSb0MCElSLwNCktTLgJAk9TIgJEm9pj0gkuyQ5BtJLk9yaZLXtPZtk5yV5Kft6zbTXZsk6R6j2IO4E3h9Vf05sDdwRJLdgKOBs6tqV+DsNi9JGpFpD4iqur6qvtemfwVcDswDFgEntW4nAQdOd22SpHuM9BxEkgXAY4ELgIdW1fXQhQiw3SrWWZJkeZLl4+Pj01WqJM06IwuIJFsCnwdeW1W3TXW9qlpaVQurauHY2NjwCpSkWW4kAZHkfnTh8Kmq+kJrviHJ9m359sCNo6hNktQZxVVMAY4HLq+qfxpYdCawuE0vBs6Y7tokSffYbATbfDJwCHBJkota298CxwKnJTkMuBp4wQhqkyQ10x4QVfVNIKtYvP901iJJWjXvpJYk9TIgJEm9DAhJUi8DQpLUy4CQJPUyICRJvQwISVIvA0KS1MuAkCT1MiAkSb0MCElSLwNCktTLgJAk9TIgJEm9RvE8CEmzxIKjvzTqEjZaVx77rKFvwz0ISVIvA0KS1MuAkCT1mnEBkeSAJD9OsiLJ0aOuR5JmqxkVEEk2BT4CPAPYDTg4yW6jrUqSZqcZFRDAXsCKqrqiqu4ATgUWjbgmSZqVZtplrvOAawbmVwJPGOyQZAmwpM3+OsmPp6m2UZsL3DTqIqYi7x51BTOGn9mGZYP5vOBP/sx2mkqnmRYQ6Wmre81ULQWWTk85M0eS5VW1cNR1aOr8zDYsfl73NdMOMa0EdhiYnw9cN6JaJGlWm2kB8V1g1yQ7J7k/cBBw5ohrkqRZaUYdYqqqO5P8DfBVYFPghKq6dMRlzRSz7rDaRsDPbMPi5zVJqmrNvSRJs85MO8QkSZohDAhJUi8DYgZI8utR16BOkkryiYH5zZKMJ/nXtXyfc5IsbNNfTjJnfdeqqZn8+5XkZUk+3KYPT/LSNaz/x/6zzYw6SS3NAL8BHpPkgVX1O+CvgGv/lDesqmeul8q03lXVcaOuYSZzD2KGSrJTkrOTXNy+7phk0yRXpDMnyd1J9m39z0vyZ6OueyPxb8DE01gOBk6ZWJDkQUlOSPLdJN9Psqi1PzDJqe3z+gzwwIF1rkwyN8mCJD8caD8qydvb9DlJ3p9kWZLLk+yZ5AtJfprkXdPwPc9KSd6e5Kg2vWf7/L6d5L2DnxXw8CRfaZ/He0ZU7rQzIGauDwMnV9XuwKeAD1bVXcBP6AYy/K/AhcA+SR4AzK+qFSOrduNyKnBQks2B3YELBpa9Bfh6Ve0J/AXw3iQPAl4F/LZ9XscAj1+H7d5RVfsCxwFnAEcAjwFeluQh6/zd6IFJLpp4Ae9YRb+PA4dX1ROBuyYt2wN4EfCfgRcl2WHyyhsjA2LmeiLw6Tb9CbpAADgP2Le9/rG170l3k6HWg6q6GFhAt/fw5UmLnwYc3f7QnANsDuxI93l8cmD9i9dh0xM3hV4CXFpV11fV7cAV3HuEAa2d31XVHhMv4K2TO7RzRFtV1bda06cndTm7qm6tqt8DlzHFsYw2dAbEhmPihpXzgH3oRr79MjAH2A9YNpqyNlpnAu9j4PBSE+B5A39wdqyqy9uyNd1UdCf3/p3bfNLy29vXuwemJ+Y9XzhcfePADRr8PO5ilnweBsTM9S26oUYAXgx8s01fADwJuLv9N3MR8Eq64ND6cwLwjqq6ZFL7V4EjkwQgyWNb+zK6z4kkj6E7NDXZDcB2SR7SDgs+eyiVa61V1c3Ar5Ls3ZoOWl3/2cKAmBm2SLJy4PU64NXAoUkuBg4BXgPQDjlcA5zf1j0P2IrusITWk6paWVUf6Fn0TuB+wMXtJOY7W/tHgS3b5/VG4Ds97/kHuuPfFwD/CvxoGLVrnR0GLE3ybbo9iltHXM/IOdSGJAFJtqyqX7fpo4Htq+o1Iy5rpGbFcTRJmoJnJXkz3d/Fq4CXjbac0XMPQpLUy3MQkqReBoQkqZcBIUnqZUBoVpg8DpKkNTMgpDVIskFc7beh1KkNhwGh2WTTJB9LcmmSr7URWPdIcn4bxfP0JNvAH0dX/Yck5wKvSfKCJD9M8oMky1qfTduon99t67+yte/XRmU9PcllSY5LsklbdnCSS9p7vbu1vTDJP7Xp1yS5ok3vkuSbbfrxSc5NcmGSrybZvq/O6f1xamPnfxyaTXYFDq6qv05yGvA8uruej6yqc5O8A3gb8NrWf05VPQUgySXA06vq2tzz8J/DgFuras82dMZ/JPlaW7YX3ai7VwFfAZ6b5FvAu+lGer0Z+FqSA+mG6XhDW28f4BdJ5tENxHhekvsBHwIWVdV4khfRjRj78sl1SuuTAaHZ5GdVdVGbvhDYhe6P67mt7STgswP9PzMw/R/AiS1YvtDangbsnuT5bX5ruhC6A/hOVU3sCZxC98f+D8A5VTXe2j8F7FtVX0yyZZKt6EZt/TTd6LD7tG09im7Y77PaEFCbAtevok5pvTEgNJtMHpFzTY8B/c3ERFUdnuQJdA8SuijJHnTj9RxZVV8dXCnJftx3ZNdi9SOGfhs4FPgx3fhaL6cb8v31dMOJX9qeU7DaOqX1yXMQms1uBW5Osk+bPwQ4t69jkl2q6oKqeitwE91/+l8FXtUOAZHkke3hQQB7Jdm5nXt4Ed1ovBcAT0n3dLlN6Z43MbG9ZcBR7ev36R5GdHtV3UoXGmNJnti2c78kj15/Pwapn3sQmu0WA8cl2YLuwTyHrqLfe5PsSrcXcDbwA7qHAi0AvteG/x4HDmz9vw0cS/cEsmXA6VV1dxvr5xvtfb5cVWe0/ufRhc6yqroryTW00V6r6o52GOuDSbam+739Z+DS9fQzkHo5FpO0nrVDTEdVlc970AbNQ0ySpF7uQUiSerkHIUnqZUBIknoZEJKkXgaEJKmXASFJ6vX/ARI/imTARbPNAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib as plt\n",
    "from matplotlib import pyplot\n",
    "pyplot.bar(group_names, df[\"horsepower-binned\"].value_counts())\n",
    "\n",
    "# set x/y labels and plot title\n",
    "plt.pyplot.xlabel(\"horsepower\")\n",
    "plt.pyplot.ylabel(\"count\")\n",
    "plt.pyplot.title(\"horsepower bins\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>By checking the dataframe above carefully, we can find that the last column provides the bins for \"horsepower\" with 3 categories (\"Low\",\"Medium\" and \"High\"). \n",
    "</p>\n",
    "We successfully narrow the intervals from 57 to 3!\n",
    "</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Bins visualization</h3>\n",
    "\n",
    "Normally, a histogram is used to visualize the distribution of bins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'horsepower bins')"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib as plt\n",
    "from matplotlib import pyplot\n",
    "\n",
    "\n",
    "# draw historgram of attribute \"horsepower\" with bins = 3\n",
    "plt.pyplot.hist(df[\"horsepower\"], bins = 3)\n",
    "\n",
    "# set x/y labels and plot title\n",
    "plt.pyplot.xlabel(\"horsepower\")\n",
    "plt.pyplot.ylabel(\"count\")\n",
    "plt.pyplot.title(\"horsepower bins\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 id=\"indicator\">Indicator variable (or dummy variable)</h2>\n",
    "\n",
    "<p>\n",
    "    An indicator variable (or dummy variable) is a numerical variable used to label categories. They are called 'dummies' because the numbers themselves don't have inherent meaning. \n",
    "</p>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['symboling', 'normalized-losses', 'make', 'fuel-type', 'aspiration',\n",
       "       'num-of-doors', 'body-style', 'drive-wheels', 'engine-location',\n",
       "       'wheel-base', 'length', 'width', 'height', 'curb-weight', 'engine-type',\n",
       "       'num-of-cylinders', 'engine-size', 'fuel-system', 'bore', 'stroke',\n",
       "       'compression-ratio', 'horsepower', 'peak-rpm', 'city-mpg',\n",
       "       'highway-mpg', 'price', 'city-L/100km', 'horsepower-binned'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "get indicator variables and assign it to data frame \"dummy_variable_1\" "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "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>diesel</th>\n",
       "      <th>gas</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   diesel  gas\n",
       "0       0    1\n",
       "1       0    1\n",
       "2       0    1\n",
       "3       0    1\n",
       "4       0    1"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dummy_variable_1 = pd.get_dummies(df[\"fuel-type\"])\n",
    "dummy_variable_1.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "change column names for clarity "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "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>diesel</th>\n",
       "      <th>gas</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   diesel  gas\n",
       "0       0    1\n",
       "1       0    1\n",
       "2       0    1\n",
       "3       0    1\n",
       "4       0    1"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dummy_variable_1.rename(columns={'fuel-type-diesel':'gas', 'fuel-type-diesel':'diesel'}, inplace=True)\n",
    "dummy_variable_1.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now have the value 0 to represent \"gas\" and 1 to represent \"diesel\" in the column \"fuel-type\" and now insert this column back into our original dataset. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "# merge data frame \"df\" and \"dummy_variable_1\" \n",
    "df = pd.concat([df, dummy_variable_1], axis=1)\n",
    "\n",
    "# drop original column \"fuel-type\" from \"df\"\n",
    "df.drop(\"fuel-type\", axis = 1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "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>symboling</th>\n",
       "      <th>normalized-losses</th>\n",
       "      <th>make</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>length</th>\n",
       "      <th>...</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",
       "      <th>city-L/100km</th>\n",
       "      <th>horsepower-binned</th>\n",
       "      <th>diesel</th>\n",
       "      <th>gas</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>122</td>\n",
       "      <td>alfa-romero</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>0.811148</td>\n",
       "      <td>...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>111</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>21</td>\n",
       "      <td>8.703704</td>\n",
       "      <td>13495.0</td>\n",
       "      <td>11.190476</td>\n",
       "      <td>Low</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>122</td>\n",
       "      <td>alfa-romero</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>0.811148</td>\n",
       "      <td>...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>111</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>21</td>\n",
       "      <td>8.703704</td>\n",
       "      <td>16500.0</td>\n",
       "      <td>11.190476</td>\n",
       "      <td>Low</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>122</td>\n",
       "      <td>alfa-romero</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>0.822681</td>\n",
       "      <td>...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>154</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>19</td>\n",
       "      <td>9.038462</td>\n",
       "      <td>16500.0</td>\n",
       "      <td>12.368421</td>\n",
       "      <td>Medium</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>164</td>\n",
       "      <td>audi</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>0.848630</td>\n",
       "      <td>...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>102</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>24</td>\n",
       "      <td>7.833333</td>\n",
       "      <td>13950.0</td>\n",
       "      <td>9.791667</td>\n",
       "      <td>Low</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>164</td>\n",
       "      <td>audi</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>0.848630</td>\n",
       "      <td>...</td>\n",
       "      <td>8.0</td>\n",
       "      <td>115</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>18</td>\n",
       "      <td>10.681818</td>\n",
       "      <td>17450.0</td>\n",
       "      <td>13.055556</td>\n",
       "      <td>Low</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   symboling  normalized-losses         make aspiration num-of-doors  \\\n",
       "0          3                122  alfa-romero        std          two   \n",
       "1          3                122  alfa-romero        std          two   \n",
       "2          1                122  alfa-romero        std          two   \n",
       "3          2                164         audi        std         four   \n",
       "4          2                164         audi        std         four   \n",
       "\n",
       "    body-style drive-wheels engine-location  wheel-base    length ...   \\\n",
       "0  convertible          rwd           front        88.6  0.811148 ...    \n",
       "1  convertible          rwd           front        88.6  0.811148 ...    \n",
       "2    hatchback          rwd           front        94.5  0.822681 ...    \n",
       "3        sedan          fwd           front        99.8  0.848630 ...    \n",
       "4        sedan          4wd           front        99.4  0.848630 ...    \n",
       "\n",
       "   compression-ratio  horsepower  peak-rpm city-mpg highway-mpg    price  \\\n",
       "0                9.0         111    5000.0       21    8.703704  13495.0   \n",
       "1                9.0         111    5000.0       21    8.703704  16500.0   \n",
       "2                9.0         154    5000.0       19    9.038462  16500.0   \n",
       "3               10.0         102    5500.0       24    7.833333  13950.0   \n",
       "4                8.0         115    5500.0       18   10.681818  17450.0   \n",
       "\n",
       "  city-L/100km  horsepower-binned  diesel  gas  \n",
       "0    11.190476                Low       0    1  \n",
       "1    11.190476                Low       0    1  \n",
       "2    12.368421             Medium       0    1  \n",
       "3     9.791667                Low       0    1  \n",
       "4    13.055556                Low       0    1  \n",
       "\n",
       "[5 rows x 29 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The last two columns are now the indicator variable representation of the fuel-type variable. It's all 0s and 1s now."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "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>aspiration-std</th>\n",
       "      <th>aspiration-turbo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   aspiration-std  aspiration-turbo\n",
       "0               1                 0\n",
       "1               1                 0\n",
       "2               1                 0\n",
       "3               1                 0\n",
       "4               1                 0"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dummy_variable_2 = pd.get_dummies(df[\"aspiration\"])\n",
    "dummy_variable_2.rename(columns={'std':\"aspiration-std\",'turbo':\"aspiration-turbo\"},inplace=True)\n",
    "dummy_variable_2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.concat([df,dummy_variable_2],axis=1)\n",
    "df.drop('aspiration',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Save the new csv "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.to_csv('clean_df.csv')"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "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": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}