EDA part 2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>Data Wrangling or Cleaning</h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>What is the purpose of Data Wrangling?</h4>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data Wrangling is the process of converting data from the initial format to a format that may be better for analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>We will be using our previous dataset, ie. motor.csv</h4> "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# let import the needed libraries \n",
    "import pandas as pd\n",
    "import matplotlib.pylab as plt \n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use the Pandas method <b>read_csv()</b> to load the data from the home directory. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('motor.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Use the method <b>head()</b> to display the first five rows of the dataframe. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
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       "<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>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": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To see what the data set looks like, we'll use the head() method.\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Missing Value\n",
    "\n",
    "As we can see, several question marks appeared in the dataframe; those are missing values which may hinder our further analysis. \n",
    "<div>So, how do we identify all those missing values and deal with them?</div> \n",
    "\n",
    "\n",
    "<b>How to work with missing data?</b>\n",
    "\n",
    "Steps for working with missing data:\n",
    "<ol>\n",
    "    <li>dentify missing data</li>\n",
    "    <li>deal with missing data</li>\n",
    "    <li>correct data format</li>\n",
    "</ol>"
   ]
  },
  {
   "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) to replace A by B </pre>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
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   "outputs": [
    {
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       "  <thead>\n",
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       "      <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",
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       "      <th>wheel-base</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",
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       "      <th>highway-mpg</th>\n",
       "      <th>price</th>\n",
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       "      <th>0</th>\n",
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       "      <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",
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       "      <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",
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       "      <td>16500</td>\n",
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       "      <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": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "# replace \"?\" to NaN\n",
    "df.replace(\"?\", np.nan, inplace = True)\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
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       "  <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",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>-1</td>\n",
       "      <td>74</td>\n",
       "      <td>volvo</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>wagon</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>104.3</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>24</td>\n",
       "      <td>28</td>\n",
       "      <td>16515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>-2</td>\n",
       "      <td>103</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>104.3</td>\n",
       "      <td>...</td>\n",
       "      <td>130</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.62</td>\n",
       "      <td>3.15</td>\n",
       "      <td>7.5</td>\n",
       "      <td>162</td>\n",
       "      <td>5100</td>\n",
       "      <td>17</td>\n",
       "      <td>22</td>\n",
       "      <td>18420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>-1</td>\n",
       "      <td>74</td>\n",
       "      <td>volvo</td>\n",
       "      <td>gas</td>\n",
       "      <td>turbo</td>\n",
       "      <td>four</td>\n",
       "      <td>wagon</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>104.3</td>\n",
       "      <td>...</td>\n",
       "      <td>130</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.62</td>\n",
       "      <td>3.15</td>\n",
       "      <td>7.5</td>\n",
       "      <td>162</td>\n",
       "      <td>5100</td>\n",
       "      <td>17</td>\n",
       "      <td>22</td>\n",
       "      <td>18950</td>\n",
       "    </tr>\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>8 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     symboling normalized-losses   make fuel-type aspiration num-of-doors  \\\n",
       "197         -1                74  volvo       gas        std         four   \n",
       "198         -2               103  volvo       gas      turbo         four   \n",
       "199         -1                74  volvo       gas      turbo         four   \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",
       "197      wagon          rwd           front       104.3  ...          141   \n",
       "198      sedan          rwd           front       104.3  ...          130   \n",
       "199      wagon          rwd           front       104.3  ...          130   \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",
       "197         mpfi  3.78    3.15               9.5        114      5400   \n",
       "198         mpfi  3.62    3.15               7.5        162      5100   \n",
       "199         mpfi  3.62    3.15               7.5        162      5100   \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",
       "197       24          28  16515  \n",
       "198       17          22  18420  \n",
       "199       17          22  18950  \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",
       "[8 rows x 26 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let see the tail \n",
    "df.tail(8) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dentify_missing_values\n",
    "\n",
    "<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": 27,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>symboling</th>\n",
       "      <th>normalized-losses</th>\n",
       "      <th>make</th>\n",
       "      <th>fuel-type</th>\n",
       "      <th>aspiration</th>\n",
       "      <th>num-of-doors</th>\n",
       "      <th>body-style</th>\n",
       "      <th>drive-wheels</th>\n",
       "      <th>engine-location</th>\n",
       "      <th>wheel-base</th>\n",
       "      <th>...</th>\n",
       "      <th>engine-size</th>\n",
       "      <th>fuel-system</th>\n",
       "      <th>bore</th>\n",
       "      <th>stroke</th>\n",
       "      <th>compression-ratio</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>peak-rpm</th>\n",
       "      <th>city-mpg</th>\n",
       "      <th>highway-mpg</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   symboling  normalized-losses   make  fuel-type  aspiration  num-of-doors  \\\n",
       "0      False               True  False      False       False         False   \n",
       "1      False               True  False      False       False         False   \n",
       "2      False               True  False      False       False         False   \n",
       "3      False              False  False      False       False         False   \n",
       "4      False              False  False      False       False         False   \n",
       "\n",
       "   body-style  drive-wheels  engine-location  wheel-base  ...  engine-size  \\\n",
       "0       False         False            False       False  ...        False   \n",
       "1       False         False            False       False  ...        False   \n",
       "2       False         False            False       False  ...        False   \n",
       "3       False         False            False       False  ...        False   \n",
       "4       False         False            False       False  ...        False   \n",
       "\n",
       "   fuel-system   bore  stroke  compression-ratio  horsepower  peak-rpm  \\\n",
       "0        False  False   False              False       False     False   \n",
       "1        False  False   False              False       False     False   \n",
       "2        False  False   False              False       False     False   \n",
       "3        False  False   False              False       False     False   \n",
       "4        False  False   False              False       False     False   \n",
       "\n",
       "   city-mpg  highway-mpg  price  \n",
       "0     False        False  False  \n",
       "1     False        False  False  \n",
       "2     False        False  False  \n",
       "3     False        False  False  \n",
       "4     False        False  False  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_data = df.isnull()\n",
    "missing_data.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\"True\" stands for missing value, while \"False\" stands for not missing value."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>Count missing values in each column</h4>\n",
    "<p>\n",
    "Using a for loop in Python, we can quickly figure out the number of missing values in each column. 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": 28,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "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": [
    "# sort columns with missing values and deduce then number of missing value in each.\n",
    "for column in missing_data.columns.values.tolist():\n",
    "    print(column)\n",
    "    print (missing_data[column].value_counts())\n",
    "    print(\"\")    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "symboling             0\n",
       "normalized-losses    41\n",
       "make                  0\n",
       "fuel-type             0\n",
       "aspiration            0\n",
       "num-of-doors          2\n",
       "body-style            0\n",
       "drive-wheels          0\n",
       "engine-location       0\n",
       "wheel-base            0\n",
       "length                0\n",
       "width                 0\n",
       "height                0\n",
       "curb-weight           0\n",
       "engine-type           0\n",
       "num-of-cylinders      0\n",
       "engine-size           0\n",
       "fuel-system           0\n",
       "bore                  4\n",
       "stroke                4\n",
       "compression-ratio     0\n",
       "horsepower            2\n",
       "peak-rpm              2\n",
       "city-mpg              0\n",
       "highway-mpg           0\n",
       "price                 4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum() "
   ]
  },
  {
   "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": [
    "###  Rules in Dropping missing data\n",
    "1. 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",
    "\n",
    "2. Also, we have some freedom in choosing which method to replace data; however, some methods may seem more reasonable than others. We will apply each method to many different columns:\n",
    "\n",
    "<b>Replace by mean:</b>\n",
    "<ul>\n",
    "    <li>\"normalized-losses\": 41 missing data, replace them with mean</li>\n",
    "    <li>\"stroke\": 4 missing data, replace them with mean</li>\n",
    "    <li>\"bore\": 4 missing data, replace them with mean</li>\n",
    "    <li>\"horsepower\": 2 missing data, replace them with mean</li>\n",
    "    <li>\"peak-rpm\": 2 missing data, replace them with mean</li>\n",
    "</ul>\n",
    "\n",
    "<b>Replace by frequency:</b>\n",
    "<ul>\n",
    "    <li>\"num-of-doors\": 2 missing data, replace them with \"four\". \n",
    "        <ul>\n",
    "            <li>Reason: 84% sedans is four doors. Since four doors is most frequent, it is most likely to occur</li>\n",
    "        </ul>\n",
    "    </li>\n",
    "</ul>\n",
    "\n",
    "<b>Drop the whole row:</b>\n",
    "<ul>\n",
    "    <li>\"price\": 4 missing data, simply delete the whole row\n",
    "        <ul>\n",
    "            <li>Reason: price is what we want to predict. Any data entry without price data cannot be used for prediction; therefore any row now without price data is not useful to us</li>\n",
    "        </ul>\n",
    "    </li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>Calculate the average of the column </h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>symboling</th>\n",
       "      <th>normalized-losses</th>\n",
       "      <th>make</th>\n",
       "      <th>fuel-type</th>\n",
       "      <th>aspiration</th>\n",
       "      <th>num-of-doors</th>\n",
       "      <th>body-style</th>\n",
       "      <th>drive-wheels</th>\n",
       "      <th>engine-location</th>\n",
       "      <th>wheel-base</th>\n",
       "      <th>...</th>\n",
       "      <th>engine-size</th>\n",
       "      <th>fuel-system</th>\n",
       "      <th>bore</th>\n",
       "      <th>stroke</th>\n",
       "      <th>compression-ratio</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>peak-rpm</th>\n",
       "      <th>city-mpg</th>\n",
       "      <th>highway-mpg</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>convertible</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>88.6</td>\n",
       "      <td>...</td>\n",
       "      <td>130</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.47</td>\n",
       "      <td>2.68</td>\n",
       "      <td>9.0</td>\n",
       "      <td>111</td>\n",
       "      <td>5000</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>13495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>convertible</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>88.6</td>\n",
       "      <td>...</td>\n",
       "      <td>130</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.47</td>\n",
       "      <td>2.68</td>\n",
       "      <td>9.0</td>\n",
       "      <td>111</td>\n",
       "      <td>5000</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>16500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>94.5</td>\n",
       "      <td>...</td>\n",
       "      <td>152</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>2.68</td>\n",
       "      <td>3.47</td>\n",
       "      <td>9.0</td>\n",
       "      <td>154</td>\n",
       "      <td>5000</td>\n",
       "      <td>19</td>\n",
       "      <td>26</td>\n",
       "      <td>16500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>164</td>\n",
       "      <td>audi</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>99.8</td>\n",
       "      <td>...</td>\n",
       "      <td>109</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>10.0</td>\n",
       "      <td>102</td>\n",
       "      <td>5500</td>\n",
       "      <td>24</td>\n",
       "      <td>30</td>\n",
       "      <td>13950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>164</td>\n",
       "      <td>audi</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>4wd</td>\n",
       "      <td>front</td>\n",
       "      <td>99.4</td>\n",
       "      <td>...</td>\n",
       "      <td>136</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>8.0</td>\n",
       "      <td>115</td>\n",
       "      <td>5500</td>\n",
       "      <td>18</td>\n",
       "      <td>22</td>\n",
       "      <td>17450</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   symboling normalized-losses         make fuel-type aspiration num-of-doors  \\\n",
       "0          3               NaN  alfa-romero       gas        std          two   \n",
       "1          3               NaN  alfa-romero       gas        std          two   \n",
       "2          1               NaN  alfa-romero       gas        std          two   \n",
       "3          2               164         audi       gas        std         four   \n",
       "4          2               164         audi       gas        std         four   \n",
       "\n",
       "    body-style drive-wheels engine-location  wheel-base  ...  engine-size  \\\n",
       "0  convertible          rwd           front        88.6  ...          130   \n",
       "1  convertible          rwd           front        88.6  ...          130   \n",
       "2    hatchback          rwd           front        94.5  ...          152   \n",
       "3        sedan          fwd           front        99.8  ...          109   \n",
       "4        sedan          4wd           front        99.4  ...          136   \n",
       "\n",
       "   fuel-system  bore  stroke compression-ratio horsepower  peak-rpm city-mpg  \\\n",
       "0         mpfi  3.47    2.68               9.0        111      5000       21   \n",
       "1         mpfi  3.47    2.68               9.0        111      5000       21   \n",
       "2         mpfi  2.68    3.47               9.0        154      5000       19   \n",
       "3         mpfi  3.19    3.40              10.0        102      5500       24   \n",
       "4         mpfi  3.19    3.40               8.0        115      5500       18   \n",
       "\n",
       "  highway-mpg  price  \n",
       "0          27  13495  \n",
       "1          27  16500  \n",
       "2          26  16500  \n",
       "3          30  13950  \n",
       "4          22  17450  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let see the table again\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average of normalized-losses: 122.0\n"
     ]
    }
   ],
   "source": [
    "avg_norm_loss = df[\"normalized-losses\"].astype(\"float\").mean(axis=0)\n",
    "print(\"Average of normalized-losses:\", avg_norm_loss)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>Replace \"NaN\" by mean value in \"normalized-losses\" column</h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"normalized-losses\"].replace(np.nan, avg_norm_loss, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>Calculate the mean value for 'bore' column</h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average of bore: 3.3297512437810943\n"
     ]
    }
   ],
   "source": [
    "avg_bore=df['bore'].astype('float').mean(axis=0)\n",
    "print(\"Average of bore:\", avg_bore)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>Replace NaN by mean value</h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"bore\"].replace(np.nan, avg_bore, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Do same for stoke"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average stroke is: 3.255422885572139\n"
     ]
    }
   ],
   "source": [
    "# Write your code below and press Shift+Enter to execute \n",
    "avg_stroke = df['stroke'].astype('float').mean(axis=0)\n",
    "print(\"Average stroke is:\",avg_stroke)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>Calculate the mean value for the  'horsepower' column:</h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average horsepower: 104.25615763546799\n"
     ]
    }
   ],
   "source": [
    "avg_horsepower = df['horsepower'].astype('float').mean(axis=0)\n",
    "print(\"Average horsepower:\", avg_horsepower)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>Replace \"NaN\" by mean value:</h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['stroke'].replace(np.nan, avg_stroke, inplace=True)\n",
    "df['horsepower'].replace(np.nan, avg_horsepower, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>Calculate the mean value for 'peak-rpm' column:</h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average peak rpm: 5125.369458128079\n"
     ]
    }
   ],
   "source": [
    "avg_peakrpm=df['peak-rpm'].astype('float').mean(axis=0)\n",
    "print(\"Average peak rpm:\", avg_peakrpm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>Replace NaN by mean value:</h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['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": 41,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "four    114\n",
       "two      89\n",
       "Name: num-of-doors, dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['num-of-doors'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that four doors are the most common type. We can also use the \".idxmax()\" method to calculate for us the most common type automatically:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'four'"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['num-of-doors'].value_counts().idxmax()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The replacement procedure is very similar to what we have seen previously"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "#replace the missing 'num-of-doors' values by the most frequent \n",
    "df[\"num-of-doors\"].replace(np.nan, \"four\", inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, let's drop all rows that do not have price data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# simply drop whole row with NaN in \"price\" column\n",
    "df.dropna(subset=[\"price\"], axis=0, inplace=True)\n",
    "\n",
    "# reset index, because we droped two rows\n",
    "df.reset_index(drop=True, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "symboling            0\n",
       "normalized-losses    0\n",
       "make                 0\n",
       "fuel-type            0\n",
       "aspiration           0\n",
       "num-of-doors         0\n",
       "body-style           0\n",
       "drive-wheels         0\n",
       "engine-location      0\n",
       "wheel-base           0\n",
       "length               0\n",
       "width                0\n",
       "height               0\n",
       "curb-weight          0\n",
       "engine-type          0\n",
       "num-of-cylinders     0\n",
       "engine-size          0\n",
       "fuel-system          0\n",
       "bore                 0\n",
       "stroke               0\n",
       "compression-ratio    0\n",
       "horsepower           0\n",
       "peak-rpm             0\n",
       "city-mpg             0\n",
       "highway-mpg          0\n",
       "price                0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
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       "  <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",
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       "      <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",
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       "      <td>rwd</td>\n",
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       "      <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",
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       "    <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",
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       "      <td>alfa-romero</td>\n",
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       "      <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": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Great!</b>\n",
    "Now, we obtain the dataset with no missing values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3 id=\"correct_data_format\">Correct data format</h3>\n",
    "\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 others).</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": [
    "<h4>Lets list the data types for each column</h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "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": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>NOTE:</h2>\n",
    "<p>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 \"astype()\" method.</p> "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>So, lets convert data types to proper format</h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "df[[\"bore\", \"stroke\"]] = df[[\"bore\", \"stroke\"]].astype(\"float\")\n",
    "df[[\"normalized-losses\"]] = df[[\"normalized-losses\"]].astype(\"int\")\n",
    "df[[\"price\"]] = df[[\"price\"]].astype(\"float\")\n",
    "df[[\"peak-rpm\"]] = df[[\"peak-rpm\"]].astype(\"float\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>Let us list the columns after the conversion</h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "symboling              int64\n",
       "normalized-losses      int64\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": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3> Great </h3>\n",
    "\n",
    "Now, we finally obtain the cleaned dataset with no missing values and all data in its proper format."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 id=\"data_standardization\">Data Standardization</h2>\n",
    "<p>\n",
    "Data is usually collected from different agencies with different formats.\n",
    "(Data Standardization is also a term for a particular type of data normalization, where we subtract the mean and divide by the standard deviation)\n",
    "</p>\n",
    "    \n",
    "<b>What is Standardization?</b>\n",
    "<p>Standardization is the process of transforming data into a common format which allows us, the researcher to make the meaningful comparison.\n",
    "</p>\n",
    "\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>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>The formula for unit conversion is<p>\n",
    "L/100km = 235 / mpg\n",
    "<p>We can do many mathematical operations directly in Pandas.</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
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       "      <td>mpfi</td>\n",
       "      <td>2.68</td>\n",
       "      <td>3.47</td>\n",
       "      <td>9.0</td>\n",
       "      <td>154</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>19</td>\n",
       "      <td>26</td>\n",
       "      <td>16500.0</td>\n",
       "    </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>24</td>\n",
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       "      <td>13950.0</td>\n",
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       "      <th>4</th>\n",
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       "      <td>sedan</td>\n",
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       "      <td>8.0</td>\n",
       "      <td>115</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>18</td>\n",
       "      <td>22</td>\n",
       "      <td>17450.0</td>\n",
       "    </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 city-mpg  highway-mpg    price  \n",
       "0    5000.0       21           27  13495.0  \n",
       "1    5000.0       21           27  16500.0  \n",
       "2    5000.0       19           26  16500.0  \n",
       "3    5500.0       24           30  13950.0  \n",
       "4    5500.0       18           22  17450.0  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let see the tables again\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>fuel-system</th>\n",
       "      <th>bore</th>\n",
       "      <th>stroke</th>\n",
       "      <th>compression-ratio</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>peak-rpm</th>\n",
       "      <th>city-mpg</th>\n",
       "      <th>highway-mpg</th>\n",
       "      <th>price</th>\n",
       "      <th>city-L/100km</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>122</td>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>convertible</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>88.6</td>\n",
       "      <td>...</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.47</td>\n",
       "      <td>2.68</td>\n",
       "      <td>9.0</td>\n",
       "      <td>111</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>13495.0</td>\n",
       "      <td>11.190476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>122</td>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>convertible</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>88.6</td>\n",
       "      <td>...</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.47</td>\n",
       "      <td>2.68</td>\n",
       "      <td>9.0</td>\n",
       "      <td>111</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>16500.0</td>\n",
       "      <td>11.190476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>122</td>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>94.5</td>\n",
       "      <td>...</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>2.68</td>\n",
       "      <td>3.47</td>\n",
       "      <td>9.0</td>\n",
       "      <td>154</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>19</td>\n",
       "      <td>26</td>\n",
       "      <td>16500.0</td>\n",
       "      <td>12.368421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>164</td>\n",
       "      <td>audi</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>99.8</td>\n",
       "      <td>...</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>10.0</td>\n",
       "      <td>102</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>24</td>\n",
       "      <td>30</td>\n",
       "      <td>13950.0</td>\n",
       "      <td>9.791667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>164</td>\n",
       "      <td>audi</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>4wd</td>\n",
       "      <td>front</td>\n",
       "      <td>99.4</td>\n",
       "      <td>...</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>8.0</td>\n",
       "      <td>115</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>18</td>\n",
       "      <td>22</td>\n",
       "      <td>17450.0</td>\n",
       "      <td>13.055556</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   symboling  normalized-losses         make fuel-type aspiration  \\\n",
       "0          3                122  alfa-romero       gas        std   \n",
       "1          3                122  alfa-romero       gas        std   \n",
       "2          1                122  alfa-romero       gas        std   \n",
       "3          2                164         audi       gas        std   \n",
       "4          2                164         audi       gas        std   \n",
       "\n",
       "  num-of-doors   body-style drive-wheels engine-location  wheel-base  ...  \\\n",
       "0          two  convertible          rwd           front        88.6  ...   \n",
       "1          two  convertible          rwd           front        88.6  ...   \n",
       "2          two    hatchback          rwd           front        94.5  ...   \n",
       "3         four        sedan          fwd           front        99.8  ...   \n",
       "4         four        sedan          4wd           front        99.4  ...   \n",
       "\n",
       "   fuel-system  bore  stroke  compression-ratio horsepower peak-rpm  city-mpg  \\\n",
       "0         mpfi  3.47    2.68                9.0        111   5000.0        21   \n",
       "1         mpfi  3.47    2.68                9.0        111   5000.0        21   \n",
       "2         mpfi  2.68    3.47                9.0        154   5000.0        19   \n",
       "3         mpfi  3.19    3.40               10.0        102   5500.0        24   \n",
       "4         mpfi  3.19    3.40                8.0        115   5500.0        18   \n",
       "\n",
       "  highway-mpg    price  city-L/100km  \n",
       "0          27  13495.0     11.190476  \n",
       "1          27  16500.0     11.190476  \n",
       "2          26  16500.0     12.368421  \n",
       "3          30  13950.0      9.791667  \n",
       "4          22  17450.0     13.055556  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert mpg to L/100km by mathematical operation (235 divided by mpg)\n",
    "df['city-L/100km'] = 235/df[\"city-mpg\"]\n",
    "\n",
    "# check your transformed data \n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>symboling</th>\n",
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       "      <th>highway-mpg</th>\n",
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       "      <td>88.6</td>\n",
       "      <td>...</td>\n",
       "      <td>3.47</td>\n",
       "      <td>2.68</td>\n",
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       "      <td>hatchback</td>\n",
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       "      <td>front</td>\n",
       "      <td>94.5</td>\n",
       "      <td>...</td>\n",
       "      <td>2.68</td>\n",
       "      <td>3.47</td>\n",
       "      <td>9.0</td>\n",
       "      <td>154</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>19</td>\n",
       "      <td>26</td>\n",
       "      <td>16500.0</td>\n",
       "      <td>12.368421</td>\n",
       "      <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>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>10.0</td>\n",
       "      <td>102</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>24</td>\n",
       "      <td>30</td>\n",
       "      <td>13950.0</td>\n",
       "      <td>9.791667</td>\n",
       "      <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",
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       "      <td>3.40</td>\n",
       "      <td>8.0</td>\n",
       "      <td>115</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>18</td>\n",
       "      <td>22</td>\n",
       "      <td>17450.0</td>\n",
       "      <td>13.055556</td>\n",
       "      <td>10.681818</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 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",
       "   bore  stroke  compression-ratio  horsepower peak-rpm city-mpg  highway-mpg  \\\n",
       "0  3.47    2.68                9.0         111   5000.0       21           27   \n",
       "1  3.47    2.68                9.0         111   5000.0       21           27   \n",
       "2  2.68    3.47                9.0         154   5000.0       19           26   \n",
       "3  3.19    3.40               10.0         102   5500.0       24           30   \n",
       "4  3.19    3.40                8.0         115   5500.0       18           22   \n",
       "\n",
       "     price  city-L/100km  high-L/100km  \n",
       "0  13495.0     11.190476      8.703704  \n",
       "1  16500.0     11.190476      8.703704  \n",
       "2  16500.0     12.368421      9.038462  \n",
       "3  13950.0      9.791667      7.833333  \n",
       "4  17450.0     13.055556     10.681818  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# transform mpg to L/100km by math operation ( 235 divided by mpg)\n",
    "df['high-L/100km'] = 235/df['highway-mpg']\n",
    "#df.rename(columns={' \" highway-mpg\"':'highway-L/100km'}, inplace=True) \n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 id=\"data_normalization\">Data Normalization and Scaling</h2>\n",
    "\n",
    "<b>Why normalization?</b>\n",
    "<p>Normalization is similar to scaling but they are not the same. It is the process of changing our observations so that they can be described as a normal distribution. When we are planning to use machine learning algorithms like <b>linear discriminate analysis  or Gaussian Naive Bayes</b>,because they assume normal distribution \n",
    "</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3> Lets use our target variable ie price to show an example </h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7217bfd1d0>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# lets view the distribution \n",
    "sns.distplot(df['price'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The skew value is : 1.8096753390980749\n"
     ]
    }
   ],
   "source": [
    "# let determine the degree of skewness\n",
    "sk = df['price'].skew() \n",
    "print(f'The skew value is : {sk}') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The skew value is:0.6786520728037038\n"
     ]
    }
   ],
   "source": [
    "# we can coreect the skewness by either finding the exponential or log of price\n",
    "nprice = np.log(df['price'])\n",
    "print(f'The skew value is:{nprice.skew()}') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7217b5b080>"
      ]
     },
     "execution_count": 56,
     "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.distplot(nprice)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Scaling</h3>\n",
    "In scaling we are changing the range of data. This means that we're transforming our data so that it fits within a specific scale, like 0-100 or 0-1. We want to scale data when we're using methods based on measures of how far apart data points are, like <b>support vector machines (SVM) or k-nearest neighbors (KNN)</b>. With these algorithms, a change of \"1\" in any numeric feature is given the same importance.\n",
    "<p>To demonstrate scaling, we may want to scale the columns <b>\"length\", \"width\" and \"height\"</b> of our dataset </p>\n",
    "<p><b>Our Target:</b>would be to scale those variables so their value ranges from 0 to 1.</p>\n",
    "<p><b>Approach:</b> replace original value by (original value)/(maximum value). Though may other approaches exits</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>length</th>\n",
       "      <th>width</th>\n",
       "      <th>height</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.811148</td>\n",
       "      <td>0.890278</td>\n",
       "      <td>0.816054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.811148</td>\n",
       "      <td>0.890278</td>\n",
       "      <td>0.816054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.822681</td>\n",
       "      <td>0.909722</td>\n",
       "      <td>0.876254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.848630</td>\n",
       "      <td>0.919444</td>\n",
       "      <td>0.908027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.848630</td>\n",
       "      <td>0.922222</td>\n",
       "      <td>0.908027</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     length     width    height\n",
       "0  0.811148  0.890278  0.816054\n",
       "1  0.811148  0.890278  0.816054\n",
       "2  0.822681  0.909722  0.876254\n",
       "3  0.848630  0.919444  0.908027\n",
       "4  0.848630  0.922222  0.908027"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# replace (original value) by (original value)/(maximum value)\n",
    "df['length'] = df['length']/df['length'].max()\n",
    "df['width'] = df['width']/df['width'].max()\n",
    "df['height'] = df['height']/df['height'].max()\n",
    "df[['length','width','height']].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we can see, we've normalized \"length\", \"width\" and \"height\" in the range of [0,1]."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 id=\"binning\">Binning</h2>\n",
    "<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>\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Example of Binning Data In Pandas</h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Convert data to correct format "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "df[\"horsepower\"]=df[\"horsepower\"].astype(int, copy=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets plot the histogram of horspower, to see what the distribution of horsepower looks like."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'horsepower bins')"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "#from matplotlib import pyplot\n",
    "plt.hist(df[\"horsepower\"])\n",
    "\n",
    "# set x/y labels and plot title\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(df[\"horsepower\"]).</p>\n",
    "<p>Since we want to include the maximum value of horsepower we want to set end_value=max(df[\"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": 67,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 48.        , 119.33333333, 190.66666667, 262.        ])"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# linspace returns evenly spaced numbers over a specified internal\n",
    "bins = np.linspace(min(df[\"horsepower\"]), max(df[\"horsepower\"]), 4)\n",
    "bins"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " We set group  names:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "group_names = ['Low', 'Medium', 'High'] # group name set in list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " We apply the function \"cut\" the determine what each value of \"df['horsepower']\" belongs to. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>horsepower</th>\n",
       "      <th>horsepower-binned</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>111</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>111</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>154</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>102</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>115</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>110</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>110</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>110</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>140</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>101</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   horsepower horsepower-binned\n",
       "0         111               Low\n",
       "1         111               Low\n",
       "2         154            Medium\n",
       "3         102               Low\n",
       "4         115               Low\n",
       "5         110               Low\n",
       "6         110               Low\n",
       "7         110               Low\n",
       "8         140            Medium\n",
       "9         101               Low"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['horsepower-binned'] = pd.cut(df['horsepower'], bins, labels=group_names, include_lowest=True )\n",
    "df[['horsepower','horsepower-binned']].head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets see the number of vehicles in each bin."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Low       153\n",
       "Medium     43\n",
       "High        5\n",
       "Name: horsepower-binned, dtype: int64"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"horsepower-binned\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets plot the distribution of each bin."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'horsepower bins')"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "#from matplotlib import pyplot\n",
    "plt.bar(group_names, df[\"horsepower-binned\"].value_counts())\n",
    "\n",
    "# set x/y labels and plot title\n",
    "plt.xlabel(\"horsepower\")\n",
    "plt.ylabel(\"count\")\n",
    "plt.title(\"horsepower bins\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>\n",
    "    Check the dataframe above carefully, you will find the last column provides the bins for \"horsepower\" with 3 categories (\"Low\",\"Medium\" and \"High\"). \n",
    "</p>\n",
    "<p>\n",
    "    We successfully narrow the intervals from 57 to 3!\n",
    "</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Bins visualization</h3>\n",
    "Normally, a histogram is used to visualize the distribution of bins we created above. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'horsepower bins')"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "#from matplotlib import pyplot\n",
    "\n",
    "a = (0,1,2)\n",
    "\n",
    "# draw historgram of attribute \"horsepower\" with bins = 3\n",
    "plt.hist(df[\"horsepower\"], bins = 3)\n",
    "\n",
    "# set x/y labels and plot title\n",
    "plt.xlabel(\"horsepower\")\n",
    "plt.ylabel(\"count\")\n",
    "plt.title(\"horsepower bins\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The plot above shows the binning result for attribute \"horsepower\". "
   ]
  },
  {
   "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 as we will see in 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": 75,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['symboling', 'normalized-losses', 'make', 'fuel-type', 'aspiration',\n",
       "       'num-of-doors', 'body-style', 'drive-wheels', 'engine-location',\n",
       "       'wheel-base', 'length', 'width', 'height', 'curb-weight', 'engine-type',\n",
       "       'num-of-cylinders', 'engine-size', 'fuel-system', 'bore', 'stroke',\n",
       "       'compression-ratio', 'horsepower', 'peak-rpm', 'city-mpg',\n",
       "       'highway-mpg', 'price', 'city-L/100km', 'high-mpg',\n",
       "       'horsepower-binned'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "get indicator variables and assign it to data frame \"dummy_variable_1\" "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "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": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dummy_variable_1 = pd.get_dummies(df[\"fuel-type\"])\n",
    "dummy_variable_1.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "change column names for clarity "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\n",
       "\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>diesel</th>\n",
       "      <th>gas</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <th>4</th>\n",
       "      <td>0</td>\n",
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       "</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": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dummy_variable_1.rename(columns={'fuel-type-diesel':'gas', 'fuel-type-diesel':'diesel'}, inplace=True)\n",
    "dummy_variable_1.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now have the value 0 to represent \"gas\" and 1 to represent \"diesel\" in the column \"fuel-type\". We will now insert this column back into our original dataset. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "# merge data frame \"df\" and \"dummy_variable_1\" \n",
    "df = pd.concat([df, dummy_variable_1], axis=1)\n",
    "\n",
    "# drop original column \"fuel-type\" from \"df\"\n",
    "df.drop(\"fuel-type\", axis = 1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>horsepower</th>\n",
       "      <th>peak-rpm</th>\n",
       "      <th>city-mpg</th>\n",
       "      <th>highway-mpg</th>\n",
       "      <th>price</th>\n",
       "      <th>city-L/100km</th>\n",
       "      <th>high-mpg</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",
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       "      <td>std</td>\n",
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       "      <td>...</td>\n",
       "      <td>111</td>\n",
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       "      <td>13495.0</td>\n",
       "      <td>11.190476</td>\n",
       "      <td>8.703704</td>\n",
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       "      <td>std</td>\n",
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       "      <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>111</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>16500.0</td>\n",
       "      <td>11.190476</td>\n",
       "      <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>154</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>19</td>\n",
       "      <td>26</td>\n",
       "      <td>16500.0</td>\n",
       "      <td>12.368421</td>\n",
       "      <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>102</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>24</td>\n",
       "      <td>30</td>\n",
       "      <td>13950.0</td>\n",
       "      <td>9.791667</td>\n",
       "      <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>115</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>18</td>\n",
       "      <td>22</td>\n",
       "      <td>17450.0</td>\n",
       "      <td>13.055556</td>\n",
       "      <td>10.681818</td>\n",
       "      <td>Low</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   symboling  normalized-losses         make aspiration num-of-doors  \\\n",
       "0          3                122  alfa-romero        std          two   \n",
       "1          3                122  alfa-romero        std          two   \n",
       "2          1                122  alfa-romero        std          two   \n",
       "3          2                164         audi        std         four   \n",
       "4          2                164         audi        std         four   \n",
       "\n",
       "    body-style drive-wheels engine-location  wheel-base    length  ...  \\\n",
       "0  convertible          rwd           front        88.6  0.811148  ...   \n",
       "1  convertible          rwd           front        88.6  0.811148  ...   \n",
       "2    hatchback          rwd           front        94.5  0.822681  ...   \n",
       "3        sedan          fwd           front        99.8  0.848630  ...   \n",
       "4        sedan          4wd           front        99.4  0.848630  ...   \n",
       "\n",
       "   horsepower  peak-rpm  city-mpg highway-mpg    price  city-L/100km  \\\n",
       "0         111    5000.0        21          27  13495.0     11.190476   \n",
       "1         111    5000.0        21          27  16500.0     11.190476   \n",
       "2         154    5000.0        19          26  16500.0     12.368421   \n",
       "3         102    5500.0        24          30  13950.0      9.791667   \n",
       "4         115    5500.0        18          22  17450.0     13.055556   \n",
       "\n",
       "    high-mpg  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",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The last two columns are now the indicator variable representation of the fuel-type variable. It's all 0s and 1s now."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Lets do the same thing  to the column of \"aspiration\": \"std\" to 0, while \"turbo\" to 1</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>aspiration</th>\n",
       "      <th>aspiration-turbo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
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       "      <th>4</th>\n",
       "      <td>1</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   aspiration  aspiration-turbo\n",
       "0           1                 0\n",
       "1           1                 0\n",
       "2           1                 0\n",
       "3           1                 0\n",
       "4           1                 0"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get dummy variable \n",
    "dummy2 = pd.get_dummies(df['aspiration'])\n",
    "# change column for clarity\n",
    "dummy2.rename(columns={'std':'aspiration','turbo':'aspiration-turbo'}, inplace=True)\n",
    "dummy2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Merge the new dataframe to the original dataframe then drop the column 'aspiration'</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
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       "      <td>0.811148</td>\n",
       "      <td>...</td>\n",
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       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>16500.0</td>\n",
       "      <td>11.190476</td>\n",
       "      <td>8.703704</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>hatchback</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
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       "      <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>19</td>\n",
       "      <td>26</td>\n",
       "      <td>16500.0</td>\n",
       "      <td>12.368421</td>\n",
       "      <td>9.038462</td>\n",
       "      <td>0</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>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>24</td>\n",
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       "      <td>13950.0</td>\n",
       "      <td>9.791667</td>\n",
       "      <td>7.833333</td>\n",
       "      <td>0</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>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>18</td>\n",
       "      <td>22</td>\n",
       "      <td>17450.0</td>\n",
       "      <td>13.055556</td>\n",
       "      <td>10.681818</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   symboling  normalized-losses         make fuel-type num-of-doors  \\\n",
       "0          3                122  alfa-romero       gas          two   \n",
       "1          3                122  alfa-romero       gas          two   \n",
       "2          1                122  alfa-romero       gas          two   \n",
       "3          2                164         audi       gas         four   \n",
       "4          2                164         audi       gas         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",
       "   stroke  compression-ratio  horsepower peak-rpm city-mpg  highway-mpg  \\\n",
       "0    2.68                9.0         111   5000.0       21           27   \n",
       "1    2.68                9.0         111   5000.0       21           27   \n",
       "2    3.47                9.0         154   5000.0       19           26   \n",
       "3    3.40               10.0         102   5500.0       24           30   \n",
       "4    3.40                8.0         115   5500.0       18           22   \n",
       "\n",
       "     price  city-L/100km  high-L/100km  aspiration-turbo  \n",
       "0  13495.0     11.190476      8.703704                 0  \n",
       "1  16500.0     11.190476      8.703704                 0  \n",
       "2  16500.0     12.368421      9.038462                 0  \n",
       "3  13950.0      9.791667      7.833333                 0  \n",
       "4  17450.0     13.055556     10.681818                 0  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# merge the new dataframe to the original dataframe\n",
    "df = pd.concat([df, dummy2], axis=1)\n",
    "# drop original column 'aspiration' from df\n",
    "df.drop('aspiration', axis=1, inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>let save our clean dataset to a new csv file</b> "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('cl.csv')"
   ]
  }
 ],
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