Data_Analysis_03_Standardization,_Normalization,_Binning,_Dummies.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Data Analysis 03-Standardization, Normalization, Binning, Dummies",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ds-sinhalen/Data-Analysis-03-Standardization-Normalization-Binning-Dummy-Variable-/blob/master/Data_Analysis_03_Standardization%2C_Normalization%2C_Binning%2C_Dummies.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7o8axEp-BZ34",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "agTSdPKaBgpl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df = pd.read_csv(\"cleaned_data.csv\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Aksfiu5TETWJ",
        "colab_type": "code",
        "outputId": "d5e852da-2a24-4eba-908f-d7200f172f30",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 309
        }
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>length</th>\n",
              "      <th>width</th>\n",
              "      <th>height</th>\n",
              "      <th>curb-weight</th>\n",
              "      <th>engine-type</th>\n",
              "      <th>num-of-cylinders</th>\n",
              "      <th>engine-size</th>\n",
              "      <th>fuel-system</th>\n",
              "      <th>bore</th>\n",
              "      <th>stroke</th>\n",
              "      <th>compression-ratio</th>\n",
              "      <th>horsepower</th>\n",
              "      <th>peak-rpm</th>\n",
              "      <th>city-mpg</th>\n",
              "      <th>highway-mpg</th>\n",
              "      <th>price</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3</td>\n",
              "      <td>122</td>\n",
              "      <td>alfa-romero</td>\n",
              "      <td>gas</td>\n",
              "      <td>std</td>\n",
              "      <td>two</td>\n",
              "      <td>convertible</td>\n",
              "      <td>rwd</td>\n",
              "      <td>front</td>\n",
              "      <td>88.6</td>\n",
              "      <td>168.8</td>\n",
              "      <td>64.1</td>\n",
              "      <td>48.8</td>\n",
              "      <td>2548</td>\n",
              "      <td>dohc</td>\n",
              "      <td>four</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>111.0</td>\n",
              "      <td>5000.0</td>\n",
              "      <td>21</td>\n",
              "      <td>27</td>\n",
              "      <td>13495.0</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>168.8</td>\n",
              "      <td>64.1</td>\n",
              "      <td>48.8</td>\n",
              "      <td>2548</td>\n",
              "      <td>dohc</td>\n",
              "      <td>four</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>111.0</td>\n",
              "      <td>5000.0</td>\n",
              "      <td>21</td>\n",
              "      <td>27</td>\n",
              "      <td>16500.0</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>171.2</td>\n",
              "      <td>65.5</td>\n",
              "      <td>52.4</td>\n",
              "      <td>2823</td>\n",
              "      <td>ohcv</td>\n",
              "      <td>six</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>154.0</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>176.6</td>\n",
              "      <td>66.2</td>\n",
              "      <td>54.3</td>\n",
              "      <td>2337</td>\n",
              "      <td>ohc</td>\n",
              "      <td>four</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>102.0</td>\n",
              "      <td>5500.0</td>\n",
              "      <td>24</td>\n",
              "      <td>30</td>\n",
              "      <td>13950.0</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>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>176.6</td>\n",
              "      <td>66.4</td>\n",
              "      <td>54.3</td>\n",
              "      <td>2824</td>\n",
              "      <td>ohc</td>\n",
              "      <td>five</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>115.0</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",
              "</div>"
            ],
            "text/plain": [
              "   symboling  normalized-losses         make  ... city-mpg highway-mpg    price\n",
              "0          3                122  alfa-romero  ...       21          27  13495.0\n",
              "1          3                122  alfa-romero  ...       21          27  16500.0\n",
              "2          1                122  alfa-romero  ...       19          26  16500.0\n",
              "3          2                164         audi  ...       24          30  13950.0\n",
              "4          2                164         audi  ...       18          22  17450.0\n",
              "\n",
              "[5 rows x 26 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P9JNT42tEVO3",
        "colab_type": "text"
      },
      "source": [
        "<b><h2 id=\"data_standardization\">Data Standardization</h2></b>\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 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>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "USI3axGFEepK",
        "colab_type": "text"
      },
      "source": [
        "The formula for unit conversion is\n",
        "\n",
        "L/100km = 235 / mpg\n",
        "\n",
        "We can do many mathematical operations directly in Pandas."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aJBvJbrKcyv1",
        "colab_type": "text"
      },
      "source": [
        "1 Gallon = 3.785 Liter\n",
        "\n",
        "1 Mile = 1.609 Kilometer\n",
        "\n",
        "L/1 km = 3.785/1,609 = 2.35 mpg\n",
        "\n",
        "L/100km = 2.35*100 = 235"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9CZVAFaTEmrP",
        "colab_type": "text"
      },
      "source": [
        "Transform mpg to L/100km in the column of \"highway-mpg\", and change the name of column to \"highway-L/100km\"."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zHvH_EPqEp9V",
        "colab_type": "code",
        "outputId": "3c48f38d-2b51-43be-9d27-a1cf4fad4868",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 309
        }
      },
      "source": [
        "# Convert mpg to L/100km by mathematical operation (235 divided by mpg)\n",
        "df['highway-L/100km'] = (235/df[\"highway-mpg\"])\n",
        "df.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>length</th>\n",
              "      <th>width</th>\n",
              "      <th>height</th>\n",
              "      <th>curb-weight</th>\n",
              "      <th>engine-type</th>\n",
              "      <th>num-of-cylinders</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",
              "      <th>highway-L/100km</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3</td>\n",
              "      <td>122</td>\n",
              "      <td>alfa-romero</td>\n",
              "      <td>gas</td>\n",
              "      <td>std</td>\n",
              "      <td>two</td>\n",
              "      <td>convertible</td>\n",
              "      <td>rwd</td>\n",
              "      <td>front</td>\n",
              "      <td>88.6</td>\n",
              "      <td>168.8</td>\n",
              "      <td>64.1</td>\n",
              "      <td>48.8</td>\n",
              "      <td>2548</td>\n",
              "      <td>dohc</td>\n",
              "      <td>four</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>111.0</td>\n",
              "      <td>5000.0</td>\n",
              "      <td>21</td>\n",
              "      <td>27</td>\n",
              "      <td>13495.0</td>\n",
              "      <td>8.703704</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3</td>\n",
              "      <td>122</td>\n",
              "      <td>alfa-romero</td>\n",
              "      <td>gas</td>\n",
              "      <td>std</td>\n",
              "      <td>two</td>\n",
              "      <td>convertible</td>\n",
              "      <td>rwd</td>\n",
              "      <td>front</td>\n",
              "      <td>88.6</td>\n",
              "      <td>168.8</td>\n",
              "      <td>64.1</td>\n",
              "      <td>48.8</td>\n",
              "      <td>2548</td>\n",
              "      <td>dohc</td>\n",
              "      <td>four</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>111.0</td>\n",
              "      <td>5000.0</td>\n",
              "      <td>21</td>\n",
              "      <td>27</td>\n",
              "      <td>16500.0</td>\n",
              "      <td>8.703704</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>122</td>\n",
              "      <td>alfa-romero</td>\n",
              "      <td>gas</td>\n",
              "      <td>std</td>\n",
              "      <td>two</td>\n",
              "      <td>hatchback</td>\n",
              "      <td>rwd</td>\n",
              "      <td>front</td>\n",
              "      <td>94.5</td>\n",
              "      <td>171.2</td>\n",
              "      <td>65.5</td>\n",
              "      <td>52.4</td>\n",
              "      <td>2823</td>\n",
              "      <td>ohcv</td>\n",
              "      <td>six</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>154.0</td>\n",
              "      <td>5000.0</td>\n",
              "      <td>19</td>\n",
              "      <td>26</td>\n",
              "      <td>16500.0</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>176.6</td>\n",
              "      <td>66.2</td>\n",
              "      <td>54.3</td>\n",
              "      <td>2337</td>\n",
              "      <td>ohc</td>\n",
              "      <td>four</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>102.0</td>\n",
              "      <td>5500.0</td>\n",
              "      <td>24</td>\n",
              "      <td>30</td>\n",
              "      <td>13950.0</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>176.6</td>\n",
              "      <td>66.4</td>\n",
              "      <td>54.3</td>\n",
              "      <td>2824</td>\n",
              "      <td>ohc</td>\n",
              "      <td>five</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>115.0</td>\n",
              "      <td>5500.0</td>\n",
              "      <td>18</td>\n",
              "      <td>22</td>\n",
              "      <td>17450.0</td>\n",
              "      <td>10.681818</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   symboling  normalized-losses  ...    price highway-L/100km\n",
              "0          3                122  ...  13495.0        8.703704\n",
              "1          3                122  ...  16500.0        8.703704\n",
              "2          1                122  ...  16500.0        9.038462\n",
              "3          2                164  ...  13950.0        7.833333\n",
              "4          2                164  ...  17450.0       10.681818\n",
              "\n",
              "[5 rows x 27 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M5sN5utLH729",
        "colab_type": "code",
        "outputId": "50501ce5-f771-486b-e7e8-363045201b09",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 309
        }
      },
      "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()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>length</th>\n",
              "      <th>width</th>\n",
              "      <th>height</th>\n",
              "      <th>curb-weight</th>\n",
              "      <th>engine-type</th>\n",
              "      <th>num-of-cylinders</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",
              "      <th>highway-L/100km</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>168.8</td>\n",
              "      <td>64.1</td>\n",
              "      <td>48.8</td>\n",
              "      <td>2548</td>\n",
              "      <td>dohc</td>\n",
              "      <td>four</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>111.0</td>\n",
              "      <td>5000.0</td>\n",
              "      <td>21</td>\n",
              "      <td>27</td>\n",
              "      <td>13495.0</td>\n",
              "      <td>8.703704</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>168.8</td>\n",
              "      <td>64.1</td>\n",
              "      <td>48.8</td>\n",
              "      <td>2548</td>\n",
              "      <td>dohc</td>\n",
              "      <td>four</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>111.0</td>\n",
              "      <td>5000.0</td>\n",
              "      <td>21</td>\n",
              "      <td>27</td>\n",
              "      <td>16500.0</td>\n",
              "      <td>8.703704</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>171.2</td>\n",
              "      <td>65.5</td>\n",
              "      <td>52.4</td>\n",
              "      <td>2823</td>\n",
              "      <td>ohcv</td>\n",
              "      <td>six</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>154.0</td>\n",
              "      <td>5000.0</td>\n",
              "      <td>19</td>\n",
              "      <td>26</td>\n",
              "      <td>16500.0</td>\n",
              "      <td>9.038462</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>176.6</td>\n",
              "      <td>66.2</td>\n",
              "      <td>54.3</td>\n",
              "      <td>2337</td>\n",
              "      <td>ohc</td>\n",
              "      <td>four</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>102.0</td>\n",
              "      <td>5500.0</td>\n",
              "      <td>24</td>\n",
              "      <td>30</td>\n",
              "      <td>13950.0</td>\n",
              "      <td>7.833333</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>176.6</td>\n",
              "      <td>66.4</td>\n",
              "      <td>54.3</td>\n",
              "      <td>2824</td>\n",
              "      <td>ohc</td>\n",
              "      <td>five</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>115.0</td>\n",
              "      <td>5500.0</td>\n",
              "      <td>18</td>\n",
              "      <td>22</td>\n",
              "      <td>17450.0</td>\n",
              "      <td>10.681818</td>\n",
              "      <td>13.055556</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   symboling  normalized-losses  ... highway-L/100km city-L/100km\n",
              "0          3                122  ...        8.703704    11.190476\n",
              "1          3                122  ...        8.703704    11.190476\n",
              "2          1                122  ...        9.038462    12.368421\n",
              "3          2                164  ...        7.833333     9.791667\n",
              "4          2                164  ...       10.681818    13.055556\n",
              "\n",
              "[5 rows x 28 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EkNItq2XEuAk",
        "colab_type": "text"
      },
      "source": [
        "<b><h2 id=\"Data_Normalization\">Data Normalization</h2></b>\n",
        "<p>\n",
        "\n",
        "<b>Why normalization?</b>\n",
        "\n",
        "Normalization is the process of transforming values of several variables into a similar range. Typical normalizations include scaling the variable so the variable average is 0, scaling the variable so the variance is 1, or scaling variable so the variable values range from 0 to 1\n",
        "\n",
        "<b>Example</b>\n",
        "\n",
        "To demonstrate normalization, let's say we want to scale the columns \"length\", \"width\" and \"height\"\n",
        "\n",
        "Target:would like to Normalize those variables so their value ranges from 0 to 1.\n",
        "\n",
        "Approach: replace original value by (original value)/(maximum value)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KUy65rmUEyjQ",
        "colab_type": "code",
        "colab": {}
      },
      "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()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tkRRafgfE7HO",
        "colab_type": "text"
      },
      "source": [
        "Normalize the column \"height\""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PBvhkPCgE8Kd",
        "colab_type": "code",
        "outputId": "35f92d54-8d97-4308-bbf4-cadba7d484b3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "# show the scaled columns\n",
        "df[[\"length\",\"width\",\"height\"]].head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "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"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ycY8t3cvFBZu",
        "colab_type": "text"
      },
      "source": [
        "Here we can see, we've normalized \"length\", \"width\" and \"height\" in the range of [0,1]."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rL8dIdGCzgI5",
        "colab_type": "text"
      },
      "source": [
        "<b><h2 id=\"binning\">Binning</h2></b>\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 262, it has 59 unique values. What if we only care about the price difference between cars with high horsepower, medium horsepower, and little horsepower (3 types)? Can we rearrange them into three ‘bins' to simplify analysis? </p>\n",
        "\n",
        "<p>We will use the Pandas method 'cut' to segment the 'horsepower' column into 3 bins </p>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tk39gi8m0pSc",
        "colab_type": "text"
      },
      "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": {
        "id": "-zCo8seI0uEF",
        "colab_type": "text"
      },
      "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",
      "metadata": {
        "id": "d42s2GhVz5zh",
        "colab_type": "code",
        "outputId": "c2e0a3a0-4c10-414d-aa60-f5be141db431",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "import numpy as np\n",
        "\n",
        "bins = np.linspace(min(df[\"horsepower\"]), max(df[\"horsepower\"]), 4)\n",
        "bins"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 48.        , 119.33333333, 190.66666667, 262.        ])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yOlg_GAp006v",
        "colab_type": "text"
      },
      "source": [
        " We set group  names:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RoUZnh44zfHi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "group_names = ['Low', 'Medium', 'High']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QJYS3SV707Yy",
        "colab_type": "text"
      },
      "source": [
        " We apply the function \"cut\" the determine what each value of \"df['horsepower']\" belongs to. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "psKy9pkKzwLv",
        "colab_type": "code",
        "outputId": "81d99520-ca3d-4ca4-8213-dcd66afe261c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 669
        }
      },
      "source": [
        "df['horsepower-binned'] = pd.cut(df['horsepower'], bins, labels=group_names, include_lowest=True)\n",
        "df[['horsepower','horsepower-binned']].head(20)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>111.0</td>\n",
              "      <td>Low</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>111.0</td>\n",
              "      <td>Low</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>154.0</td>\n",
              "      <td>Medium</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>102.0</td>\n",
              "      <td>Low</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>115.0</td>\n",
              "      <td>Low</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>110.0</td>\n",
              "      <td>Low</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>110.0</td>\n",
              "      <td>Low</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>110.0</td>\n",
              "      <td>Low</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>140.0</td>\n",
              "      <td>Medium</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>101.0</td>\n",
              "      <td>Low</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>101.0</td>\n",
              "      <td>Low</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>121.0</td>\n",
              "      <td>Medium</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>121.0</td>\n",
              "      <td>Medium</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>121.0</td>\n",
              "      <td>Medium</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>182.0</td>\n",
              "      <td>Medium</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>182.0</td>\n",
              "      <td>Medium</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>182.0</td>\n",
              "      <td>Medium</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>48.0</td>\n",
              "      <td>Low</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>70.0</td>\n",
              "      <td>Low</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>70.0</td>\n",
              "      <td>Low</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    horsepower horsepower-binned\n",
              "0        111.0               Low\n",
              "1        111.0               Low\n",
              "2        154.0            Medium\n",
              "3        102.0               Low\n",
              "4        115.0               Low\n",
              "5        110.0               Low\n",
              "6        110.0               Low\n",
              "7        110.0               Low\n",
              "8        140.0            Medium\n",
              "9        101.0               Low\n",
              "10       101.0               Low\n",
              "11       121.0            Medium\n",
              "12       121.0            Medium\n",
              "13       121.0            Medium\n",
              "14       182.0            Medium\n",
              "15       182.0            Medium\n",
              "16       182.0            Medium\n",
              "17        48.0               Low\n",
              "18        70.0               Low\n",
              "19        70.0               Low"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eowHovysGQBb",
        "colab_type": "text"
      },
      "source": [
        "Lets see the number of vehicles in each bin."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-3hRjc_8GTVj",
        "colab_type": "code",
        "outputId": "5ed39cde-f65c-4fe4-a02d-7868dd59047d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "source": [
        "df[\"horsepower-binned\"].value_counts()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Low       153\n",
              "Medium     43\n",
              "High        5\n",
              "Name: horsepower-binned, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JeFFu3VrGWgb",
        "colab_type": "text"
      },
      "source": [
        "Lets plot the distribution of each bin."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5V0CWujwGaPI",
        "colab_type": "code",
        "outputId": "f63e7ec6-981f-4db7-d27c-2acc57d1544b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        }
      },
      "source": [
        "%matplotlib inline\n",
        "import matplotlib as plt\n",
        "from matplotlib import pyplot\n",
        "pyplot.bar(group_names, df[\"horsepower-binned\"].value_counts())\n",
        "\n",
        "# set x/y labels and plot title\n",
        "plt.pyplot.xlabel(\"horsepower\")\n",
        "plt.pyplot.ylabel(\"count\")\n",
        "plt.pyplot.title(\"horsepower bins\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'horsepower bins')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEWCAYAAACJ0YulAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nktQzFCRJPUNBktQzFCRJvf8PTzOBnu1CYXUAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rYh-iRY7Ge4e",
        "colab_type": "text"
      },
      "source": [
        "Check the dataframe above carefully, you will find the last column provides the bins for \"horsepower\" with 3 categories (\"Low\",\"Medium\" and \"High\")."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K3xybLHdGuPW",
        "colab_type": "text"
      },
      "source": [
        "<b><h2 id=\"indicator\">Indicator variable (or dummy variable)</h2></b>\n",
        "<b>What is an indicator variable?</b>\n",
        "<p>\n",
        "    An indicator variable (or dummy variable) is a numerical variable used to label categories. They are called 'dummies' because the numbers themselves don't have inherent meaning. \n",
        "</p>\n",
        "\n",
        "<b>Why we use indicator variables?</b>\n",
        "<p>\n",
        "    So we can use categorical variables for regression analysis in the later modules.\n",
        "</p>\n",
        "<b>Example</b>\n",
        "<p>\n",
        "    We see the column \"fuel-type\" has two unique values, \"gas\" or \"diesel\". Regression doesn't understand words, only numbers. To use this attribute in regression analysis, we convert \"fuel-type\" into indicator variables.\n",
        "</p>\n",
        "\n",
        "<p>\n",
        "    We will use the panda's method 'get_dummies' to assign numerical values to different categories of fuel type. \n",
        "</p>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gz6ueHBFGy9b",
        "colab_type": "code",
        "outputId": "049fa4ae-40ce-4b5c-f570-00557c6ddbd6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153
        }
      },
      "source": [
        "df.columns"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "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', 'highway-L/100km', 'city-L/100km',\n",
              "       'horsepower-binned'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q6DeGOzbG2JI",
        "colab_type": "text"
      },
      "source": [
        "get indicator variables and assign it to data frame \"dummy_variable_1\""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WLzFfkNuG74n",
        "colab_type": "code",
        "outputId": "4eaa97f3-fd69-43bb-97d5-5715da8627d2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "dummy_variable_1 = pd.get_dummies(df[\"fuel-type\"])\n",
        "dummy_variable_1.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "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"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RTLXl6mrSxju",
        "colab_type": "code",
        "outputId": "52fde6f1-e444-43ae-cc1a-c618a2693389",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "df[\"fuel-type\"].value_counts()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "gas       181\n",
              "diesel     20\n",
              "Name: fuel-type, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j0nZd1SoSoFy",
        "colab_type": "code",
        "outputId": "c8d1120d-af5c-41b4-ad1f-3435abead297",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "dummy_variable_1.rename(columns={'gas':'fuel-type-gas', 'diesel':'fuel-type-diesel'}, inplace=True)\n",
        "dummy_variable_1.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>fuel-type-diesel</th>\n",
              "      <th>fuel-type-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": [
              "   fuel-type-diesel  fuel-type-gas\n",
              "0                 0              1\n",
              "1                 0              1\n",
              "2                 0              1\n",
              "3                 0              1\n",
              "4                 0              1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FYWH-Y9RHG_a",
        "colab_type": "text"
      },
      "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",
      "metadata": {
        "id": "c92u3GZuHKaX",
        "colab_type": "code",
        "colab": {}
      },
      "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)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XwhOXLNwHNE2",
        "colab_type": "code",
        "outputId": "fcf8f561-cd1f-48ef-c7f4-fa4e9eb8c3fd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 309
        }
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>symboling</th>\n",
              "      <th>normalized-losses</th>\n",
              "      <th>make</th>\n",
              "      <th>aspiration</th>\n",
              "      <th>num-of-doors</th>\n",
              "      <th>body-style</th>\n",
              "      <th>drive-wheels</th>\n",
              "      <th>engine-location</th>\n",
              "      <th>wheel-base</th>\n",
              "      <th>length</th>\n",
              "      <th>width</th>\n",
              "      <th>height</th>\n",
              "      <th>curb-weight</th>\n",
              "      <th>engine-type</th>\n",
              "      <th>num-of-cylinders</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",
              "      <th>highway-L/100km</th>\n",
              "      <th>city-L/100km</th>\n",
              "      <th>horsepower-binned</th>\n",
              "      <th>fuel-type-diesel</th>\n",
              "      <th>fuel-type-gas</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3</td>\n",
              "      <td>122</td>\n",
              "      <td>alfa-romero</td>\n",
              "      <td>std</td>\n",
              "      <td>two</td>\n",
              "      <td>convertible</td>\n",
              "      <td>rwd</td>\n",
              "      <td>front</td>\n",
              "      <td>88.6</td>\n",
              "      <td>0.811148</td>\n",
              "      <td>0.890278</td>\n",
              "      <td>0.816054</td>\n",
              "      <td>2548</td>\n",
              "      <td>dohc</td>\n",
              "      <td>four</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>111.0</td>\n",
              "      <td>5000.0</td>\n",
              "      <td>21</td>\n",
              "      <td>27</td>\n",
              "      <td>13495.0</td>\n",
              "      <td>8.703704</td>\n",
              "      <td>11.190476</td>\n",
              "      <td>Low</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3</td>\n",
              "      <td>122</td>\n",
              "      <td>alfa-romero</td>\n",
              "      <td>std</td>\n",
              "      <td>two</td>\n",
              "      <td>convertible</td>\n",
              "      <td>rwd</td>\n",
              "      <td>front</td>\n",
              "      <td>88.6</td>\n",
              "      <td>0.811148</td>\n",
              "      <td>0.890278</td>\n",
              "      <td>0.816054</td>\n",
              "      <td>2548</td>\n",
              "      <td>dohc</td>\n",
              "      <td>four</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>111.0</td>\n",
              "      <td>5000.0</td>\n",
              "      <td>21</td>\n",
              "      <td>27</td>\n",
              "      <td>16500.0</td>\n",
              "      <td>8.703704</td>\n",
              "      <td>11.190476</td>\n",
              "      <td>Low</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>122</td>\n",
              "      <td>alfa-romero</td>\n",
              "      <td>std</td>\n",
              "      <td>two</td>\n",
              "      <td>hatchback</td>\n",
              "      <td>rwd</td>\n",
              "      <td>front</td>\n",
              "      <td>94.5</td>\n",
              "      <td>0.822681</td>\n",
              "      <td>0.909722</td>\n",
              "      <td>0.876254</td>\n",
              "      <td>2823</td>\n",
              "      <td>ohcv</td>\n",
              "      <td>six</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>154.0</td>\n",
              "      <td>5000.0</td>\n",
              "      <td>19</td>\n",
              "      <td>26</td>\n",
              "      <td>16500.0</td>\n",
              "      <td>9.038462</td>\n",
              "      <td>12.368421</td>\n",
              "      <td>Medium</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2</td>\n",
              "      <td>164</td>\n",
              "      <td>audi</td>\n",
              "      <td>std</td>\n",
              "      <td>four</td>\n",
              "      <td>sedan</td>\n",
              "      <td>fwd</td>\n",
              "      <td>front</td>\n",
              "      <td>99.8</td>\n",
              "      <td>0.848630</td>\n",
              "      <td>0.919444</td>\n",
              "      <td>0.908027</td>\n",
              "      <td>2337</td>\n",
              "      <td>ohc</td>\n",
              "      <td>four</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>102.0</td>\n",
              "      <td>5500.0</td>\n",
              "      <td>24</td>\n",
              "      <td>30</td>\n",
              "      <td>13950.0</td>\n",
              "      <td>7.833333</td>\n",
              "      <td>9.791667</td>\n",
              "      <td>Low</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2</td>\n",
              "      <td>164</td>\n",
              "      <td>audi</td>\n",
              "      <td>std</td>\n",
              "      <td>four</td>\n",
              "      <td>sedan</td>\n",
              "      <td>4wd</td>\n",
              "      <td>front</td>\n",
              "      <td>99.4</td>\n",
              "      <td>0.848630</td>\n",
              "      <td>0.922222</td>\n",
              "      <td>0.908027</td>\n",
              "      <td>2824</td>\n",
              "      <td>ohc</td>\n",
              "      <td>five</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>115.0</td>\n",
              "      <td>5500.0</td>\n",
              "      <td>18</td>\n",
              "      <td>22</td>\n",
              "      <td>17450.0</td>\n",
              "      <td>10.681818</td>\n",
              "      <td>13.055556</td>\n",
              "      <td>Low</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   symboling  normalized-losses  ... fuel-type-diesel fuel-type-gas\n",
              "0          3                122  ...                0             1\n",
              "1          3                122  ...                0             1\n",
              "2          1                122  ...                0             1\n",
              "3          2                164  ...                0             1\n",
              "4          2                164  ...                0             1\n",
              "\n",
              "[5 rows x 30 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wChcbxuCHQSH",
        "colab_type": "text"
      },
      "source": [
        "The last two columns are now the indicator variable representation of the fuel-type variable. It's all 0s and 1s now."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bhFm1rxYHT0-",
        "colab_type": "code",
        "outputId": "7a5e6970-c886-4ae0-cdd5-5f8ba3593589",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "# get indicator variables of aspiration and assign it to data frame \"dummy_variable_2\"\n",
        "dummy_variable_2 = pd.get_dummies(df['aspiration'])\n",
        "\n",
        "# change column names for clarity\n",
        "dummy_variable_2.rename(columns={'std':'aspiration-std', 'turbo': 'aspiration-turbo'}, inplace=True)\n",
        "\n",
        "# show first 5 instances of data frame \"dummy_variable_1\"\n",
        "dummy_variable_2.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>aspiration-std</th>\n",
              "      <th>aspiration-turbo</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   aspiration-std  aspiration-turbo\n",
              "0               1                 0\n",
              "1               1                 0\n",
              "2               1                 0\n",
              "3               1                 0\n",
              "4               1                 0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cOW45M_oHcI4",
        "colab_type": "text"
      },
      "source": [
        "<h4>Merge the new dataframe to the original dataframe then drop the column 'aspiration'</h4>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "exabcRe1HXkn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#merge the new dataframe to the original datafram\n",
        "df = pd.concat([df, dummy_variable_2], axis=1)\n",
        "\n",
        "# drop original column \"aspiration\" from \"df\"\n",
        "df.drop(\"aspiration\", axis = 1, inplace=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M0EU-9GzCG1j",
        "colab_type": "code",
        "outputId": "3612678a-a481-4b98-e7f3-32d223dbef72",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 309
        }
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>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>width</th>\n",
              "      <th>height</th>\n",
              "      <th>curb-weight</th>\n",
              "      <th>engine-type</th>\n",
              "      <th>num-of-cylinders</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",
              "      <th>highway-L/100km</th>\n",
              "      <th>city-L/100km</th>\n",
              "      <th>horsepower-binned</th>\n",
              "      <th>fuel-type-diesel</th>\n",
              "      <th>fuel-type-gas</th>\n",
              "      <th>aspiration-std</th>\n",
              "      <th>aspiration-turbo</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3</td>\n",
              "      <td>122</td>\n",
              "      <td>alfa-romero</td>\n",
              "      <td>two</td>\n",
              "      <td>convertible</td>\n",
              "      <td>rwd</td>\n",
              "      <td>front</td>\n",
              "      <td>88.6</td>\n",
              "      <td>0.811148</td>\n",
              "      <td>0.890278</td>\n",
              "      <td>0.816054</td>\n",
              "      <td>2548</td>\n",
              "      <td>dohc</td>\n",
              "      <td>four</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>111.0</td>\n",
              "      <td>5000.0</td>\n",
              "      <td>21</td>\n",
              "      <td>27</td>\n",
              "      <td>13495.0</td>\n",
              "      <td>8.703704</td>\n",
              "      <td>11.190476</td>\n",
              "      <td>Low</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3</td>\n",
              "      <td>122</td>\n",
              "      <td>alfa-romero</td>\n",
              "      <td>two</td>\n",
              "      <td>convertible</td>\n",
              "      <td>rwd</td>\n",
              "      <td>front</td>\n",
              "      <td>88.6</td>\n",
              "      <td>0.811148</td>\n",
              "      <td>0.890278</td>\n",
              "      <td>0.816054</td>\n",
              "      <td>2548</td>\n",
              "      <td>dohc</td>\n",
              "      <td>four</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>111.0</td>\n",
              "      <td>5000.0</td>\n",
              "      <td>21</td>\n",
              "      <td>27</td>\n",
              "      <td>16500.0</td>\n",
              "      <td>8.703704</td>\n",
              "      <td>11.190476</td>\n",
              "      <td>Low</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>122</td>\n",
              "      <td>alfa-romero</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>0.909722</td>\n",
              "      <td>0.876254</td>\n",
              "      <td>2823</td>\n",
              "      <td>ohcv</td>\n",
              "      <td>six</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>154.0</td>\n",
              "      <td>5000.0</td>\n",
              "      <td>19</td>\n",
              "      <td>26</td>\n",
              "      <td>16500.0</td>\n",
              "      <td>9.038462</td>\n",
              "      <td>12.368421</td>\n",
              "      <td>Medium</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</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>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>0.919444</td>\n",
              "      <td>0.908027</td>\n",
              "      <td>2337</td>\n",
              "      <td>ohc</td>\n",
              "      <td>four</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>102.0</td>\n",
              "      <td>5500.0</td>\n",
              "      <td>24</td>\n",
              "      <td>30</td>\n",
              "      <td>13950.0</td>\n",
              "      <td>7.833333</td>\n",
              "      <td>9.791667</td>\n",
              "      <td>Low</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</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>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>0.922222</td>\n",
              "      <td>0.908027</td>\n",
              "      <td>2824</td>\n",
              "      <td>ohc</td>\n",
              "      <td>five</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>115.0</td>\n",
              "      <td>5500.0</td>\n",
              "      <td>18</td>\n",
              "      <td>22</td>\n",
              "      <td>17450.0</td>\n",
              "      <td>10.681818</td>\n",
              "      <td>13.055556</td>\n",
              "      <td>Low</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   symboling  normalized-losses  ... aspiration-std aspiration-turbo\n",
              "0          3                122  ...              1                0\n",
              "1          3                122  ...              1                0\n",
              "2          1                122  ...              1                0\n",
              "3          2                164  ...              1                0\n",
              "4          2                164  ...              1                0\n",
              "\n",
              "[5 rows x 31 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8pBYwMtPHgIT",
        "colab_type": "text"
      },
      "source": [
        "save the new csv"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jxMbQa8SE2Xc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df.to_csv('Auto_mobile.csv', index = False)"
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}