Cars_Data_Wrangling.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1 align=center><font size=5>Data Analysis with Python</font></h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>Data Wrangling</h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Table of content</h2>\n",
    "\n",
    "<div class=\"alert alert-block alert-info\" style=\"margin-top: 20px\">\n",
    "<ul>\n",
    "    <li><a href=\"#identify_handle_missing_values\">Identify and handle missing values</a>\n",
    "        <ul>\n",
    "            <li><a href=\"#identify_missing_values\">Identify missing values</a></li>\n",
    "            <li><a href=\"#deal_missing_values\">Deal with missing values</a></li>\n",
    "            <li><a href=\"#correct_data_format\">Correct data format</a></li>\n",
    "        </ul>\n",
    "    </li>\n",
    "    <li><a href=\"#data_standardization\">Data standardization</a></li>\n",
    "    <li><a href=\"#data_normalization\">Data Normalization (centering/scaling)</a></li>\n",
    "    <li><a href=\"#binning\">Binning</a></li>\n",
    "    <li><a href=\"#indicator\">Indicator variable</a></li>\n",
    "</ul>\n",
    "    \n",
    "Estimated Time Needed: <strong>30 min</strong>\n",
    "</div>\n",
    " \n",
    "<hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>What is the fuel consumption (L/100k) rate for the diesel car?</h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Import data</h3>\n",
    "<p>\n",
    "You can find the \"Automobile Data Set\" from the following link: <a href=\"https://archive.ics.uci.edu/ml/machine-learning-databases/autos/imports-85.data\">https://archive.ics.uci.edu/ml/machine-learning-databases/autos/imports-85.data</a>. \n",
    "We will be using this data set throughout this implementation.\n",
    "</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import pandas,matplotlib and seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Reading the data set from the URL and adding the related headers.</h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "URL of the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "filename = \"https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/DA0101EN/auto.csv\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Python list headers containing name of headers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "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\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "cars=pd.read_csv(filename,names=headers)  #Importing the dataset into dataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use the method <code>head()</code> to display the first five rows of the dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "<div>\n",
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       "</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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cars.head()   #Bydefault first five rows are displayed otherwise no of rows to be displayed can be passed as a parameter."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use the method <code>tail()</code> to display the Last five rows of the dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "<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>200</th>\n",
       "      <td>-1</td>\n",
       "      <td>95</td>\n",
       "      <td>volvo</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>109.1</td>\n",
       "      <td>...</td>\n",
       "      <td>141</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.78</td>\n",
       "      <td>3.15</td>\n",
       "      <td>9.5</td>\n",
       "      <td>114</td>\n",
       "      <td>5400</td>\n",
       "      <td>23</td>\n",
       "      <td>28</td>\n",
       "      <td>16845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>-1</td>\n",
       "      <td>95</td>\n",
       "      <td>volvo</td>\n",
       "      <td>gas</td>\n",
       "      <td>turbo</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>109.1</td>\n",
       "      <td>...</td>\n",
       "      <td>141</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.78</td>\n",
       "      <td>3.15</td>\n",
       "      <td>8.7</td>\n",
       "      <td>160</td>\n",
       "      <td>5300</td>\n",
       "      <td>19</td>\n",
       "      <td>25</td>\n",
       "      <td>19045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>-1</td>\n",
       "      <td>95</td>\n",
       "      <td>volvo</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>109.1</td>\n",
       "      <td>...</td>\n",
       "      <td>173</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.58</td>\n",
       "      <td>2.87</td>\n",
       "      <td>8.8</td>\n",
       "      <td>134</td>\n",
       "      <td>5500</td>\n",
       "      <td>18</td>\n",
       "      <td>23</td>\n",
       "      <td>21485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>-1</td>\n",
       "      <td>95</td>\n",
       "      <td>volvo</td>\n",
       "      <td>diesel</td>\n",
       "      <td>turbo</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>109.1</td>\n",
       "      <td>...</td>\n",
       "      <td>145</td>\n",
       "      <td>idi</td>\n",
       "      <td>3.01</td>\n",
       "      <td>3.40</td>\n",
       "      <td>23.0</td>\n",
       "      <td>106</td>\n",
       "      <td>4800</td>\n",
       "      <td>26</td>\n",
       "      <td>27</td>\n",
       "      <td>22470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>-1</td>\n",
       "      <td>95</td>\n",
       "      <td>volvo</td>\n",
       "      <td>gas</td>\n",
       "      <td>turbo</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>109.1</td>\n",
       "      <td>...</td>\n",
       "      <td>141</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.78</td>\n",
       "      <td>3.15</td>\n",
       "      <td>9.5</td>\n",
       "      <td>114</td>\n",
       "      <td>5400</td>\n",
       "      <td>19</td>\n",
       "      <td>25</td>\n",
       "      <td>22625</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",
       "200         -1                95  volvo       gas        std         four   \n",
       "201         -1                95  volvo       gas      turbo         four   \n",
       "202         -1                95  volvo       gas        std         four   \n",
       "203         -1                95  volvo    diesel      turbo         four   \n",
       "204         -1                95  volvo       gas      turbo         four   \n",
       "\n",
       "    body-style drive-wheels engine-location  wheel-base  ...  engine-size  \\\n",
       "200      sedan          rwd           front       109.1  ...          141   \n",
       "201      sedan          rwd           front       109.1  ...          141   \n",
       "202      sedan          rwd           front       109.1  ...          173   \n",
       "203      sedan          rwd           front       109.1  ...          145   \n",
       "204      sedan          rwd           front       109.1  ...          141   \n",
       "\n",
       "     fuel-system  bore  stroke compression-ratio horsepower  peak-rpm  \\\n",
       "200         mpfi  3.78    3.15               9.5        114      5400   \n",
       "201         mpfi  3.78    3.15               8.7        160      5300   \n",
       "202         mpfi  3.58    2.87               8.8        134      5500   \n",
       "203          idi  3.01    3.40              23.0        106      4800   \n",
       "204         mpfi  3.78    3.15               9.5        114      5400   \n",
       "\n",
       "    city-mpg highway-mpg  price  \n",
       "200       23          28  16845  \n",
       "201       19          25  19045  \n",
       "202       18          23  21485  \n",
       "203       26          27  22470  \n",
       "204       19          25  22625  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cars.tail()    #Bydefault Last five rows are displayed otherwise no of rows to be displayed can be passed as a parameter."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 id=\"identify_handle_missing_values\">Identify and handle missing values</h2>\n",
    "\n",
    "\n",
    "<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. Here we use the function: \n",
    " <pre>.replace(A, B, inplace = True) </pre>\n",
    "to replace A by B"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "cars.replace('?',np.nan,inplace=True)\n",
    "cars.head()"
   ]
  },
  {
   "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": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
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       "      <th>compression-ratio</th>\n",
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       "      <th>highway-mpg</th>\n",
       "      <th>price</th>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <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": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_cars=cars.isnull()\n",
    "missing_cars.head()"
   ]
  },
  {
   "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. As mentioned above, \"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_cars.columns.values.tolist():\n",
    "    print(column)\n",
    "    print(missing_cars[column].value_counts())\n",
    "    print(\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the summary above, each column has 205 rows of data, seven 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": [
    "<h3 id=\"deal_missing_values\">Deal with missing data</h3>\n",
    "<b>How to deal with missing data?</b>\n",
    "\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": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average of normalized-losses is: 122.0\n"
     ]
    }
   ],
   "source": [
    "avg_norm_loss=cars['normalized-losses'].astype(float).mean()\n",
    "print(\"Average of normalized-losses is:\",avg_norm_loss)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Replace \"NaN\" by mean value in \"normalized-losses\" column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "cars['normalized-losses'].replace(np.nan,avg_norm_loss,inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similarly calculating mean and replacing null values with their mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "avg_stroke=cars['stroke'].astype('float').mean()\n",
    "cars['stroke'].replace(np.nan,avg_stroke,inplace=True)\n",
    "\n",
    "avg_horsepower = cars['horsepower'].astype('float').mean()\n",
    "cars['horsepower'].replace(np.nan, avg_horsepower, inplace=True)\n",
    "\n",
    "avg_peakrpm=cars['peak-rpm'].astype('float').mean()\n",
    "cars['peak-rpm'].replace(np.nan, avg_peakrpm, inplace=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To see which values are present in a particular column, we can use the \".value_counts()\" method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "four    114\n",
       "two      89\n",
       "Name: num-of-doors, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cars['num-of-doors'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here as we can possibly see that 'four' occurs maximum no of times in 'num-of-doors' column,hence we replace the 2 missing values with 'four'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "cars['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": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# simply drop whole row with NaN in \"price\" column\n",
    "cars.dropna(subset=['price'], axis=0, inplace=True)\n",
    "\n",
    "# reset index, because we droped two rows\n",
    "cars.reset_index(drop=True, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>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>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>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>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>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>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               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  ...  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": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cars.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Good!** Now, we obtain the dataset with no missing values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3 id=\"correct_data_format\">Correct data format</h3>\n",
    "<b>We are almost there!</b>\n",
    "<p>The last step in data cleaning is checking and making sure that all data is in the correct format (int, float, text or other).</p>\n",
    "\n",
    "In Pandas, we use \n",
    "<p><b>.dtype()</b> to check the data type</p>\n",
    "<p><b>.astype()</b> to change the data type</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Lets list the data types for each column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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",
       "dtype: object"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cars.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see above, 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 <code>astype()</code> method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "cars[['bore','stroke','price','peak-rpm']]=cars[['bore','stroke','price','peak-rpm']].astype('float')\n",
    "cars['normalized-losses']=cars['normalized-losses'].astype('int')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>Let us list the columns after the conversion</h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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",
       "dtype: object"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cars.dtypes"
   ]
  },
  {
   "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",
    "<b>Example</b>\n",
    "<p>Transform mpg to L/100km:</p>\n",
    "<p>In our dataset, the fuel consumption columns \"city-mpg\" and \"highway-mpg\" are represented by mpg (miles per gallon) unit. Assume we are developing an application in a country that accept the fuel consumption with L/100km standard</p>\n",
    "<p>We will need to apply <b>data transformation</b> to transform mpg into L/100km?</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The formula for unit conversion is\n",
    "\n",
    "L/100km = 235 / mpg\n",
    "\n",
    "We can do many mathematical operations directly in Pandas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</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",
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       "      <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>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>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>5000.0</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>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>5000.0</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>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>5000.0</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>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>5500.0</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>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>5500.0</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 × 26 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",
       "   engine-size  fuel-system  bore  stroke compression-ratio horsepower  \\\n",
       "0          130         mpfi  3.47    2.68               9.0        111   \n",
       "1          130         mpfi  3.47    2.68               9.0        111   \n",
       "2          152         mpfi  2.68    3.47               9.0        154   \n",
       "3          109         mpfi  3.19    3.40              10.0        102   \n",
       "4          136         mpfi  3.19    3.40               8.0        115   \n",
       "\n",
       "   peak-rpm highway-mpg    price  city-L/100km  \n",
       "0    5000.0          27  13495.0     11.190476  \n",
       "1    5000.0          27  16500.0     11.190476  \n",
       "2    5000.0          26  16500.0     12.368421  \n",
       "3    5500.0          30  13950.0      9.791667  \n",
       "4    5500.0          22  17450.0     13.055556  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cars['city-L/100km']=235/cars['city-mpg']\n",
    "#remove city-mpg column using drop()\n",
    "cars.drop(['city-mpg'],axis=1,inplace=True)\n",
    "cars.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
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       "        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>price</th>\n",
       "      <th>city-L/100km</th>\n",
       "      <th>highway-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>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>5000.0</td>\n",
       "      <td>13495.0</td>\n",
       "      <td>11.190476</td>\n",
       "      <td>8.703704</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>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>5000.0</td>\n",
       "      <td>16500.0</td>\n",
       "      <td>11.190476</td>\n",
       "      <td>8.703704</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>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>5000.0</td>\n",
       "      <td>16500.0</td>\n",
       "      <td>12.368421</td>\n",
       "      <td>9.038462</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>5500.0</td>\n",
       "      <td>13950.0</td>\n",
       "      <td>9.791667</td>\n",
       "      <td>7.833333</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>5500.0</td>\n",
       "      <td>17450.0</td>\n",
       "      <td>13.055556</td>\n",
       "      <td>10.681818</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  \\\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",
       "   engine-size  fuel-system  bore  stroke compression-ratio horsepower  \\\n",
       "0          130         mpfi  3.47    2.68               9.0        111   \n",
       "1          130         mpfi  3.47    2.68               9.0        111   \n",
       "2          152         mpfi  2.68    3.47               9.0        154   \n",
       "3          109         mpfi  3.19    3.40              10.0        102   \n",
       "4          136         mpfi  3.19    3.40               8.0        115   \n",
       "\n",
       "   peak-rpm    price  city-L/100km  highway-L/100km  \n",
       "0    5000.0  13495.0     11.190476         8.703704  \n",
       "1    5000.0  16500.0     11.190476         8.703704  \n",
       "2    5000.0  16500.0     12.368421         9.038462  \n",
       "3    5500.0  13950.0      9.791667         7.833333  \n",
       "4    5500.0  17450.0     13.055556        10.681818  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cars['highway-L/100km']=235/cars['highway-mpg']\n",
    "#remove city-mpg column using drop()\n",
    "cars.drop(['highway-mpg'],axis=1,inplace=True)\n",
    "cars.head()"
   ]
  },
  {
   "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",
    "\n",
    "<b>Example</b>\n",
    "<p>To demonstrate normalization, let's say we want to scale the columns \"length\", \"width\" and \"height\" </p>\n",
    "<p><b>Target:</b>would like to Normalize those variables so their value ranges from 0 to 1.</p>\n",
    "<p><b>Approach:</b> replace original value by (original value)/(maximum value)</p>\n",
    "<b>Ways to Normalize</b>\n",
    "<ol>\n",
    "    <li>Simple Feature scaling: x<sub>new</sub>=x<sub>old</sub>/x<sub>max</sub></li>\n",
    "    <li>Min-Max Normalization:x<sub>new</sub>=(x<sub>old</sub>-x<sub>min</sub>)/(x<sub>min</sub>-x<sub>max</sub>)\n",
    "    <li>Z-Score Normalization:x<sub>new</sub>=(x<sub>old</sub>-μ)/σ   ,where 'μ' is the mean and 'σ' is the standard deviation\n",
    "        \n",
    "</ol>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\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>168.8</td>\n",
       "      <td>64.1</td>\n",
       "      <td>48.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>168.8</td>\n",
       "      <td>64.1</td>\n",
       "      <td>48.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>171.2</td>\n",
       "      <td>65.5</td>\n",
       "      <td>52.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>176.6</td>\n",
       "      <td>66.2</td>\n",
       "      <td>54.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>176.6</td>\n",
       "      <td>66.4</td>\n",
       "      <td>54.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>188.8</td>\n",
       "      <td>68.9</td>\n",
       "      <td>55.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>188.8</td>\n",
       "      <td>68.8</td>\n",
       "      <td>55.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>188.8</td>\n",
       "      <td>68.9</td>\n",
       "      <td>55.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>188.8</td>\n",
       "      <td>68.9</td>\n",
       "      <td>55.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>188.8</td>\n",
       "      <td>68.9</td>\n",
       "      <td>55.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>201 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     length  width  height\n",
       "0     168.8   64.1    48.8\n",
       "1     168.8   64.1    48.8\n",
       "2     171.2   65.5    52.4\n",
       "3     176.6   66.2    54.3\n",
       "4     176.6   66.4    54.3\n",
       "..      ...    ...     ...\n",
       "196   188.8   68.9    55.5\n",
       "197   188.8   68.8    55.5\n",
       "198   188.8   68.9    55.5\n",
       "199   188.8   68.9    55.5\n",
       "200   188.8   68.9    55.5\n",
       "\n",
       "[201 rows x 3 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cars[['length','width','height']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will be using simple feature scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "cars['length']=cars['length']/cars['length'].max()\n",
    "cars['width']=cars['width']/cars['width'].max()\n",
    "cars['height']=cars['height']/cars['height'].max()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 id=\"binning\">Binning</h2>\n",
    "<b>Why binning?</b>\n",
    "<p>\n",
    "    Binning is a process of transforming continuous numerical variables into discrete categorical 'bins', for grouped analysis.\n",
    "</p>\n",
    "\n",
    "<b>Example: </b>\n",
    "<p>In our dataset, \"horsepower\" is a real valued variable ranging from 48 to 288, it has 57 unique values. What if we only care about the price difference between cars with high horsepower, medium horsepower, and little horsepower (3 types)? Can we rearrange them into three ‘bins' to simplify analysis? </p>\n",
    "\n",
    "<p>We will use the Pandas method 'cut' to segment the 'horsepower' column into 3 bins </p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Example of Binning Data in pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Convert data to correct format "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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             int32\n",
       "peak-rpm             float64\n",
       "price                float64\n",
       "city-L/100km         float64\n",
       "highway-L/100km      float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cars['horsepower']=cars['horsepower'].astype(int,copy=True)\n",
    "cars\n",
    "cars.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets plot the histogram of horspower, to see what the distribution of horsepower looks like.We can plot histogram either using matplotlib <code>hist()</code> function or use seaborn library's <code>distplot()</code> and set kde to False.We will be using <code>distplot()</code> here to plot the histogram."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'horsepower bins')"
      ]
     },
     "execution_count": 43,
     "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",
    "sns.distplot(cars['horsepower'],kde=False)\n",
    "plt.xlabel('horsepower')\n",
    "plt.ylabel('count')\n",
    "plt.title('horsepower bins')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>We would like 3 bins of equal size bandwidth so we use numpy's <code>linspace(start_value, end_value, numbers_generated</code> function.</p>\n",
    "<p>Since we want to include the minimum value of horsepower we want to set start_value=min(cars[\"horsepower\"]).</p>\n",
    "<p>Since we want to include the maximum value of horsepower we want to set end_value=max(cars[\"horsepower\"]).</p>\n",
    "<p>Since we are building 3 bins of equal length, there should be 4 dividers, so numbers_generated=4.</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We build a bin array, with a minimum value to a maximum value, with bandwidth calculated above. The bins will be values used to determine when one bin ends and another begins."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 48.        , 119.33333333, 190.66666667, 262.        ])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bins=np.linspace(min(cars['horsepower']),max(cars['horsepower']),4)\n",
    "bins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "group=['Low','Medium','High']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We apply the function \"cut\" the determine what each value of \"cars['horsepower']\" belongs to."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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-binned</th>\n",
       "      <th>horsepower</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Low</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Low</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Medium</td>\n",
       "      <td>154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Low</td>\n",
       "      <td>102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Low</td>\n",
       "      <td>115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Low</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Low</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Low</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Medium</td>\n",
       "      <td>140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Low</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Low</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Medium</td>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Medium</td>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Medium</td>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Medium</td>\n",
       "      <td>182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Medium</td>\n",
       "      <td>182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Medium</td>\n",
       "      <td>182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Low</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Low</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Low</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   horsepower-binned  horsepower\n",
       "0                Low         111\n",
       "1                Low         111\n",
       "2             Medium         154\n",
       "3                Low         102\n",
       "4                Low         115\n",
       "5                Low         110\n",
       "6                Low         110\n",
       "7                Low         110\n",
       "8             Medium         140\n",
       "9                Low         101\n",
       "10               Low         101\n",
       "11            Medium         121\n",
       "12            Medium         121\n",
       "13            Medium         121\n",
       "14            Medium         182\n",
       "15            Medium         182\n",
       "16            Medium         182\n",
       "17               Low          48\n",
       "18               Low          70\n",
       "19               Low          70"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cars['horsepower-binned']=pd.cut(cars['horsepower'],bins,labels=group,include_lowest=True)\n",
    "cars[['horsepower-binned','horsepower']].head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets see the number of vehicles in each bin."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Low       153\n",
       "Medium     43\n",
       "High        5\n",
       "Name: horsepower-binned, dtype: int64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cars['horsepower-binned'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets plot the distribution of each bin."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'horsepower bins')"
      ]
     },
     "execution_count": 50,
     "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": [
    "sns.countplot(x='horsepower-binned',data=cars)\n",
    "plt.xlabel(\"horsepower\")\n",
    "plt.ylabel(\"count\")\n",
    "plt.title(\"horsepower bins\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We successfully narrow the intervals from 57 to 3!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 id=\"indicator\">Indicator variable (or dummy variable)</h2>\n",
    "<b>What is an indicator variable?</b>\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",
    "\n",
    "<b>Why we use indicator variables?</b>\n",
    "<p>\n",
    "    So we can use categorical variables for regression analysis in the later modules.\n",
    "</p>\n",
    "<b>Example</b>\n",
    "<p>\n",
    "    We see the column \"fuel-type\" has two unique values, \"gas\" or \"diesel\". Regression doesn't understand words, only numbers. To use this attribute in regression analysis, we convert \"fuel-type\" into indicator variables.\n",
    "</p>\n",
    "\n",
    "<p>\n",
    "    We will use the panda's method 'get_dummies' to assign numerical values to different categories of fuel type. \n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dummy_variable=pd.get_dummies(cars['fuel-type'])\n",
    "dummy_variable.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\". We will now insert this column back into our original dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "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>stroke</th>\n",
       "      <th>compression-ratio</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>peak-rpm</th>\n",
       "      <th>price</th>\n",
       "      <th>city-L/100km</th>\n",
       "      <th>highway-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>2.68</td>\n",
       "      <td>9.0</td>\n",
       "      <td>111</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>13495.0</td>\n",
       "      <td>11.190476</td>\n",
       "      <td>8.703704</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>2.68</td>\n",
       "      <td>9.0</td>\n",
       "      <td>111</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>16500.0</td>\n",
       "      <td>11.190476</td>\n",
       "      <td>8.703704</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>3.47</td>\n",
       "      <td>9.0</td>\n",
       "      <td>154</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>16500.0</td>\n",
       "      <td>12.368421</td>\n",
       "      <td>9.038462</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>3.40</td>\n",
       "      <td>10.0</td>\n",
       "      <td>102</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>13950.0</td>\n",
       "      <td>9.791667</td>\n",
       "      <td>7.833333</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>3.40</td>\n",
       "      <td>8.0</td>\n",
       "      <td>115</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>17450.0</td>\n",
       "      <td>13.055556</td>\n",
       "      <td>10.681818</td>\n",
       "      <td>Low</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>-1</td>\n",
       "      <td>95</td>\n",
       "      <td>volvo</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>109.1</td>\n",
       "      <td>0.907256</td>\n",
       "      <td>...</td>\n",
       "      <td>3.15</td>\n",
       "      <td>9.5</td>\n",
       "      <td>114</td>\n",
       "      <td>5400.0</td>\n",
       "      <td>16845.0</td>\n",
       "      <td>10.217391</td>\n",
       "      <td>8.392857</td>\n",
       "      <td>Low</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>-1</td>\n",
       "      <td>95</td>\n",
       "      <td>volvo</td>\n",
       "      <td>turbo</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>109.1</td>\n",
       "      <td>0.907256</td>\n",
       "      <td>...</td>\n",
       "      <td>3.15</td>\n",
       "      <td>8.7</td>\n",
       "      <td>160</td>\n",
       "      <td>5300.0</td>\n",
       "      <td>19045.0</td>\n",
       "      <td>12.368421</td>\n",
       "      <td>9.400000</td>\n",
       "      <td>Medium</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>-1</td>\n",
       "      <td>95</td>\n",
       "      <td>volvo</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>109.1</td>\n",
       "      <td>0.907256</td>\n",
       "      <td>...</td>\n",
       "      <td>2.87</td>\n",
       "      <td>8.8</td>\n",
       "      <td>134</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>21485.0</td>\n",
       "      <td>13.055556</td>\n",
       "      <td>10.217391</td>\n",
       "      <td>Medium</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>-1</td>\n",
       "      <td>95</td>\n",
       "      <td>volvo</td>\n",
       "      <td>turbo</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>109.1</td>\n",
       "      <td>0.907256</td>\n",
       "      <td>...</td>\n",
       "      <td>3.40</td>\n",
       "      <td>23.0</td>\n",
       "      <td>106</td>\n",
       "      <td>4800.0</td>\n",
       "      <td>22470.0</td>\n",
       "      <td>9.038462</td>\n",
       "      <td>8.703704</td>\n",
       "      <td>Low</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>-1</td>\n",
       "      <td>95</td>\n",
       "      <td>volvo</td>\n",
       "      <td>turbo</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>109.1</td>\n",
       "      <td>0.907256</td>\n",
       "      <td>...</td>\n",
       "      <td>3.15</td>\n",
       "      <td>9.5</td>\n",
       "      <td>114</td>\n",
       "      <td>5400.0</td>\n",
       "      <td>22625.0</td>\n",
       "      <td>12.368421</td>\n",
       "      <td>9.400000</td>\n",
       "      <td>Low</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>201 rows × 28 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",
       "196         -1                 95        volvo        std         four   \n",
       "197         -1                 95        volvo      turbo         four   \n",
       "198         -1                 95        volvo        std         four   \n",
       "199         -1                 95        volvo      turbo         four   \n",
       "200         -1                 95        volvo      turbo         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",
       "196        sedan          rwd           front       109.1  0.907256  ...   \n",
       "197        sedan          rwd           front       109.1  0.907256  ...   \n",
       "198        sedan          rwd           front       109.1  0.907256  ...   \n",
       "199        sedan          rwd           front       109.1  0.907256  ...   \n",
       "200        sedan          rwd           front       109.1  0.907256  ...   \n",
       "\n",
       "     stroke  compression-ratio  horsepower peak-rpm    price  city-L/100km  \\\n",
       "0      2.68                9.0         111   5000.0  13495.0     11.190476   \n",
       "1      2.68                9.0         111   5000.0  16500.0     11.190476   \n",
       "2      3.47                9.0         154   5000.0  16500.0     12.368421   \n",
       "3      3.40               10.0         102   5500.0  13950.0      9.791667   \n",
       "4      3.40                8.0         115   5500.0  17450.0     13.055556   \n",
       "..      ...                ...         ...      ...      ...           ...   \n",
       "196    3.15                9.5         114   5400.0  16845.0     10.217391   \n",
       "197    3.15                8.7         160   5300.0  19045.0     12.368421   \n",
       "198    2.87                8.8         134   5500.0  21485.0     13.055556   \n",
       "199    3.40               23.0         106   4800.0  22470.0      9.038462   \n",
       "200    3.15                9.5         114   5400.0  22625.0     12.368421   \n",
       "\n",
       "    highway-L/100km  horsepower-binned  diesel  gas  \n",
       "0          8.703704                Low       0    1  \n",
       "1          8.703704                Low       0    1  \n",
       "2          9.038462             Medium       0    1  \n",
       "3          7.833333                Low       0    1  \n",
       "4         10.681818                Low       0    1  \n",
       "..              ...                ...     ...  ...  \n",
       "196        8.392857                Low       0    1  \n",
       "197        9.400000             Medium       0    1  \n",
       "198       10.217391             Medium       0    1  \n",
       "199        8.703704                Low       1    0  \n",
       "200        9.400000                Low       0    1  \n",
       "\n",
       "[201 rows x 28 columns]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# merge data frame \"df\" and \"dummy_variable\" \n",
    "cars=pd.concat([cars,dummy_variable],axis=1)\n",
    "#drop original column \"fuel-type\" from \"df\"\n",
    "cars.drop(\"fuel-type\", axis = 1, inplace=True)\n",
    "cars"
   ]
  },
  {
   "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": "markdown",
   "metadata": {},
   "source": [
    "Save the new csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "cars.to_csv(\"cars_clean.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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