IBMDeveloperSkillsNetwork-DA0101EN-SkillsNetwork_jupyterlite_files_DA0101EN-Review-Introduction.jupyterlite.ipynb

{
  "metadata": {
    "anaconda-cloud": {},
    "kernelspec": {
      "name": "python",
      "display_name": "Pyolite",
      "language": "python"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "python",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8"
    }
  },
  "nbformat_minor": 4,
  "nbformat": 4,
  "cells": [
    {
      "cell_type": "markdown",
      "source": "<center>\n    <img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DA0101EN-SkillsNetwork/labs/Module%201/images/IDSNlogo.png\" width=\"300\" alt=\"cognitiveclass.ai logo\"  />\n</center>\n\n# Introduction  Notebook\n\nEstimated time needed: **10** minutes\n\n## Objectives\n\nAfter completing this lab you will be able to:\n\n*   Acquire data in various ways\n*   Obtain insights from data with Pandas library\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<h2>Table of Contents</h2>\n\n<div class=\"alert alert-block alert-info\" style=\"margin-top: 20px\">\n<ol>\n    <li><a href=\"https://#data_acquisition\">Data Acquisition</a>\n    <li><a href=\"https://#basic_insight\">Basic Insight of Dataset</a></li>\n</ol>\n\n</div>\n<hr>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<h1 id=\"data_acquisition\">Data Acquisition</h1>\n<p>\nThere are various formats for a dataset: .csv, .json, .xlsx  etc. The dataset can be stored in different places, on your local machine or sometimes online.<br>\n\nIn this section, you will learn how to load a dataset into our Jupyter Notebook.<br>\n\nIn our case, the Automobile Dataset is an online source, and it is in a CSV (comma separated value) format. Let's use this dataset as an example to practice data reading.\n\n<ul>\n    <li>Data source: <a href=\"https://archive.ics.uci.edu/ml/machine-learning-databases/autos/imports-85.data?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDA0101ENSkillsNetwork20235326-2021-01-01\" target=\"_blank\">https://archive.ics.uci.edu/ml/machine-learning-databases/autos/imports-85.data</a></li>\n    <li>Data type: csv</li>\n</ul>\nThe Pandas Library is a useful tool that enables us to read various datasets into a dataframe; our Jupyter notebook platforms have a built-in <b>Pandas Library</b> so that all we need to do is import Pandas without installing.\n</p>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "you are running the lab in your  browser, so we will install the libraries using `piplite`\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "#you are running the lab in your  browser, so we will install the libraries using ``piplite``\nimport piplite\nimport micropip\nawait piplite.install(['pandas'])\nawait piplite.install(['matplotlib'])\nawait piplite.install(['scipy'])\nawait piplite.install(['seaborn'])\nawait micropip.install(['ipywidgets'],keep_going=True)\nawait micropip.install(['tqdm'],keep_going=True)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "If you run the lab locally using Anaconda, you can load the correct library and versions by uncommenting the following:\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "#install specific version of libraries used in  lab\n#! mamba install pandas==1.3.3  -y\n#! mamba install numpy=1.21.2 -y",
      "metadata": {
        "trusted": true
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": "# import pandas library\nimport pandas as pd\nimport numpy as np",
      "metadata": {
        "trusted": true
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "This function will download the dataset into your browser\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "#This function will download the dataset into your browser \n\nfrom pyodide.http import pyfetch\n\nasync def download(url, filename):\n    response = await pyfetch(url)\n    if response.status == 200:\n        with open(filename, \"wb\") as f:\n            f.write(await response.bytes())",
      "metadata": {
        "trusted": true
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "<h2>Read Data</h2>\n<p>\nWe use <code>pandas.read_csv()</code> function to read the csv file. In the brackets, we put the file path along with a quotation mark so that pandas will read the file into a dataframe from that address. The file path can be either an URL or your local file address.<br>\n\nBecause the data does not include headers, we can add an argument <code>headers = None</code> inside the <code>read_csv()</code> method so that pandas will not automatically set the first row as a header.<br>\n\nYou can also assign the dataset to any variable you create.\n\n</p>\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "path = \"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DA0101EN-SkillsNetwork/labs/Data%20files/auto.csv\"",
      "metadata": {
        "trusted": true
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "you will need to download the dataset; if you are running locally, please comment out the following\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "#you will need to download the dataset; if you are running locally, please comment out the following \nawait download(path, \"auto.csv\")\npath=\"auto.csv\"",
      "metadata": {
        "trusted": true
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "This dataset was hosted on IBM Cloud object. Click <a href=\"https://cocl.us/DA101EN_object_storage?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDA0101ENSkillsNetwork20235326-2021-01-01\">HERE</a> for free storage.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "# Import pandas library\nimport pandas as pd\n\n# Read the online file by the URL provides above, and assign it to variable \"df\"\n\ndf = pd.read_csv(path, header=None)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "After reading the dataset, we can use the <code>dataframe.head(n)</code> method to check the top n rows of the dataframe, where n is an integer. Contrary to <code>dataframe.head(n)</code>, <code>dataframe.tail(n)</code> will show you the bottom n rows of the dataframe.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "# show the first 5 rows using dataframe.head() method\nprint(\"The first 5 rows of the dataframe\") \ndf.head(5)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 9,
      "outputs": [
        {
          "name": "stdout",
          "text": "The first 5 rows of the dataframe\n",
          "output_type": "stream"
        },
        {
          "execution_count": 9,
          "output_type": "execute_result",
          "data": {
            "text/plain": "   0    1            2    3    4     5            6    7      8     9   ...  \\\n0   3    ?  alfa-romero  gas  std   two  convertible  rwd  front  88.6  ...   \n1   3    ?  alfa-romero  gas  std   two  convertible  rwd  front  88.6  ...   \n2   1    ?  alfa-romero  gas  std   two    hatchback  rwd  front  94.5  ...   \n3   2  164         audi  gas  std  four        sedan  fwd  front  99.8  ...   \n4   2  164         audi  gas  std  four        sedan  4wd  front  99.4  ...   \n\n    16    17    18    19    20   21    22  23  24     25  \n0  130  mpfi  3.47  2.68   9.0  111  5000  21  27  13495  \n1  130  mpfi  3.47  2.68   9.0  111  5000  21  27  16500  \n2  152  mpfi  2.68  3.47   9.0  154  5000  19  26  16500  \n3  109  mpfi  3.19  3.40  10.0  102  5500  24  30  13950  \n4  136  mpfi  3.19  3.40   8.0  115  5500  18  22  17450  \n\n[5 rows x 26 columns]",
            "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>0</th>\n      <th>1</th>\n      <th>2</th>\n      <th>3</th>\n      <th>4</th>\n      <th>5</th>\n      <th>6</th>\n      <th>7</th>\n      <th>8</th>\n      <th>9</th>\n      <th>...</th>\n      <th>16</th>\n      <th>17</th>\n      <th>18</th>\n      <th>19</th>\n      <th>20</th>\n      <th>21</th>\n      <th>22</th>\n      <th>23</th>\n      <th>24</th>\n      <th>25</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>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "<div class=\"alert alert-danger alertdanger\" style=\"margin-top: 20px\">\n<h1> Question #1: </h1>\n<b>Check the bottom 10 rows of data frame \"df\".</b>\n</div>\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "# Write your code below and press Shift+Enter to execute \ndf.tail(10)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 10,
      "outputs": [
        {
          "execution_count": 10,
          "output_type": "execute_result",
          "data": {
            "text/plain": "     0    1      2       3      4     5      6    7      8      9   ...   16  \\\n195  -1   74  volvo     gas    std  four  wagon  rwd  front  104.3  ...  141   \n196  -2  103  volvo     gas    std  four  sedan  rwd  front  104.3  ...  141   \n197  -1   74  volvo     gas    std  four  wagon  rwd  front  104.3  ...  141   \n198  -2  103  volvo     gas  turbo  four  sedan  rwd  front  104.3  ...  130   \n199  -1   74  volvo     gas  turbo  four  wagon  rwd  front  104.3  ...  130   \n200  -1   95  volvo     gas    std  four  sedan  rwd  front  109.1  ...  141   \n201  -1   95  volvo     gas  turbo  four  sedan  rwd  front  109.1  ...  141   \n202  -1   95  volvo     gas    std  four  sedan  rwd  front  109.1  ...  173   \n203  -1   95  volvo  diesel  turbo  four  sedan  rwd  front  109.1  ...  145   \n204  -1   95  volvo     gas  turbo  four  sedan  rwd  front  109.1  ...  141   \n\n       17    18    19    20   21    22  23  24     25  \n195  mpfi  3.78  3.15   9.5  114  5400  23  28  13415  \n196  mpfi  3.78  3.15   9.5  114  5400  24  28  15985  \n197  mpfi  3.78  3.15   9.5  114  5400  24  28  16515  \n198  mpfi  3.62  3.15   7.5  162  5100  17  22  18420  \n199  mpfi  3.62  3.15   7.5  162  5100  17  22  18950  \n200  mpfi  3.78  3.15   9.5  114  5400  23  28  16845  \n201  mpfi  3.78  3.15   8.7  160  5300  19  25  19045  \n202  mpfi  3.58  2.87   8.8  134  5500  18  23  21485  \n203   idi  3.01  3.40  23.0  106  4800  26  27  22470  \n204  mpfi  3.78  3.15   9.5  114  5400  19  25  22625  \n\n[10 rows x 26 columns]",
            "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>0</th>\n      <th>1</th>\n      <th>2</th>\n      <th>3</th>\n      <th>4</th>\n      <th>5</th>\n      <th>6</th>\n      <th>7</th>\n      <th>8</th>\n      <th>9</th>\n      <th>...</th>\n      <th>16</th>\n      <th>17</th>\n      <th>18</th>\n      <th>19</th>\n      <th>20</th>\n      <th>21</th>\n      <th>22</th>\n      <th>23</th>\n      <th>24</th>\n      <th>25</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>195</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>23</td>\n      <td>28</td>\n      <td>13415</td>\n    </tr>\n    <tr>\n      <th>196</th>\n      <td>-2</td>\n      <td>103</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>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>15985</td>\n    </tr>\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>10 rows × 26 columns</p>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "<details><summary>Click here for the solution</summary>\n\n```python\nprint(\"The last 10 rows of the dataframe\\n\")\ndf.tail(10)\n```\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<h3>Add Headers</h3>\n<p>\nTake a look at our dataset. Pandas automatically set the header with an integer starting from 0.\n</p>\n<p>\nTo better describe our data, we can introduce a header. This information is available at:  <a href=\"https://archive.ics.uci.edu/ml/datasets/Automobile?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDA0101ENSkillsNetwork20235326-2021-01-01\" target=\"_blank\">https://archive.ics.uci.edu/ml/datasets/Automobile</a>.\n</p>\n<p>\nThus, we have to add headers manually.\n</p>\n<p>\nFirst, we create a list \"headers\" that include all column names in order.\nThen, we use <code>dataframe.columns = headers</code> to replace the headers with the list we created.\n</p>\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "import pandas as pd\n\n# create headers list\nheaders = [\"symboling\",\"normalized-losses\",\"make\",\"fuel-type\",\"aspiration\", \"num-of-doors\",\"body-style\",\n         \"drive-wheels\",\"engine-location\",\"wheel-base\", \"length\",\"width\",\"height\",\"curb-weight\",\"engine-type\",\n         \"num-of-cylinders\", \"engine-size\",\"fuel-system\",\"bore\",\"stroke\",\"compression-ratio\",\"horsepower\",\n         \"peak-rpm\",\"city-mpg\",\"highway-mpg\",\"price\"]\n\n# read in data\ndf = pd.read_csv(\"auto.csv\")\n\n# replace column names with headers list\ndf.columns = headers\n\n# print first few rows to check\nprint(df.head())\n",
      "metadata": {
        "trusted": true
      },
      "execution_count": 13,
      "outputs": [
        {
          "name": "stdout",
          "text": "   symboling normalized-losses         make fuel-type aspiration num-of-doors  \\\n0          3                 ?  alfa-romero       gas        std          two   \n1          1                 ?  alfa-romero       gas        std          two   \n2          2               164         audi       gas        std         four   \n3          2               164         audi       gas        std         four   \n4          2                 ?         audi       gas        std          two   \n\n    body-style drive-wheels engine-location  wheel-base  ...  engine-size  \\\n0  convertible          rwd           front        88.6  ...          130   \n1    hatchback          rwd           front        94.5  ...          152   \n2        sedan          fwd           front        99.8  ...          109   \n3        sedan          4wd           front        99.4  ...          136   \n4        sedan          fwd           front        99.8  ...          136   \n\n   fuel-system  bore  stroke compression-ratio horsepower  peak-rpm city-mpg  \\\n0         mpfi  3.47    2.68               9.0        111      5000       21   \n1         mpfi  2.68    3.47               9.0        154      5000       19   \n2         mpfi  3.19    3.40              10.0        102      5500       24   \n3         mpfi  3.19    3.40               8.0        115      5500       18   \n4         mpfi  3.19    3.40               8.5        110      5500       19   \n\n  highway-mpg  price  \n0          27  16500  \n1          26  16500  \n2          30  13950  \n3          22  17450  \n4          25  15250  \n\n[5 rows x 26 columns]\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "We replace headers and recheck our dataframe:\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "df.columns = headers\ndf.head(10)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 14,
      "outputs": [
        {
          "execution_count": 14,
          "output_type": "execute_result",
          "data": {
            "text/plain": "   symboling normalized-losses         make fuel-type aspiration num-of-doors  \\\n0          3                 ?  alfa-romero       gas        std          two   \n1          1                 ?  alfa-romero       gas        std          two   \n2          2               164         audi       gas        std         four   \n3          2               164         audi       gas        std         four   \n4          2                 ?         audi       gas        std          two   \n5          1               158         audi       gas        std         four   \n6          1                 ?         audi       gas        std         four   \n7          1               158         audi       gas      turbo         four   \n8          0                 ?         audi       gas      turbo          two   \n9          2               192          bmw       gas        std          two   \n\n    body-style drive-wheels engine-location  wheel-base  ...  engine-size  \\\n0  convertible          rwd           front        88.6  ...          130   \n1    hatchback          rwd           front        94.5  ...          152   \n2        sedan          fwd           front        99.8  ...          109   \n3        sedan          4wd           front        99.4  ...          136   \n4        sedan          fwd           front        99.8  ...          136   \n5        sedan          fwd           front       105.8  ...          136   \n6        wagon          fwd           front       105.8  ...          136   \n7        sedan          fwd           front       105.8  ...          131   \n8    hatchback          4wd           front        99.5  ...          131   \n9        sedan          rwd           front       101.2  ...          108   \n\n   fuel-system  bore  stroke compression-ratio horsepower  peak-rpm city-mpg  \\\n0         mpfi  3.47    2.68               9.0        111      5000       21   \n1         mpfi  2.68    3.47               9.0        154      5000       19   \n2         mpfi  3.19    3.40              10.0        102      5500       24   \n3         mpfi  3.19    3.40               8.0        115      5500       18   \n4         mpfi  3.19    3.40               8.5        110      5500       19   \n5         mpfi  3.19    3.40               8.5        110      5500       19   \n6         mpfi  3.19    3.40               8.5        110      5500       19   \n7         mpfi  3.13    3.40               8.3        140      5500       17   \n8         mpfi  3.13    3.40               7.0        160      5500       16   \n9         mpfi  3.50    2.80               8.8        101      5800       23   \n\n  highway-mpg  price  \n0          27  16500  \n1          26  16500  \n2          30  13950  \n3          22  17450  \n4          25  15250  \n5          25  17710  \n6          25  18920  \n7          20  23875  \n8          22      ?  \n9          29  16430  \n\n[10 rows x 26 columns]",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>symboling</th>\n      <th>normalized-losses</th>\n      <th>make</th>\n      <th>fuel-type</th>\n      <th>aspiration</th>\n      <th>num-of-doors</th>\n      <th>body-style</th>\n      <th>drive-wheels</th>\n      <th>engine-location</th>\n      <th>wheel-base</th>\n      <th>...</th>\n      <th>engine-size</th>\n      <th>fuel-system</th>\n      <th>bore</th>\n      <th>stroke</th>\n      <th>compression-ratio</th>\n      <th>horsepower</th>\n      <th>peak-rpm</th>\n      <th>city-mpg</th>\n      <th>highway-mpg</th>\n      <th>price</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>3</td>\n      <td>?</td>\n      <td>alfa-romero</td>\n      <td>gas</td>\n      <td>std</td>\n      <td>two</td>\n      <td>convertible</td>\n      <td>rwd</td>\n      <td>front</td>\n      <td>88.6</td>\n      <td>...</td>\n      <td>130</td>\n      <td>mpfi</td>\n      <td>3.47</td>\n      <td>2.68</td>\n      <td>9.0</td>\n      <td>111</td>\n      <td>5000</td>\n      <td>21</td>\n      <td>27</td>\n      <td>16500</td>\n    </tr>\n    <tr>\n      <th>1</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>2</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>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>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    <tr>\n      <th>4</th>\n      <td>2</td>\n      <td>?</td>\n      <td>audi</td>\n      <td>gas</td>\n      <td>std</td>\n      <td>two</td>\n      <td>sedan</td>\n      <td>fwd</td>\n      <td>front</td>\n      <td>99.8</td>\n      <td>...</td>\n      <td>136</td>\n      <td>mpfi</td>\n      <td>3.19</td>\n      <td>3.40</td>\n      <td>8.5</td>\n      <td>110</td>\n      <td>5500</td>\n      <td>19</td>\n      <td>25</td>\n      <td>15250</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>1</td>\n      <td>158</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>105.8</td>\n      <td>...</td>\n      <td>136</td>\n      <td>mpfi</td>\n      <td>3.19</td>\n      <td>3.40</td>\n      <td>8.5</td>\n      <td>110</td>\n      <td>5500</td>\n      <td>19</td>\n      <td>25</td>\n      <td>17710</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>1</td>\n      <td>?</td>\n      <td>audi</td>\n      <td>gas</td>\n      <td>std</td>\n      <td>four</td>\n      <td>wagon</td>\n      <td>fwd</td>\n      <td>front</td>\n      <td>105.8</td>\n      <td>...</td>\n      <td>136</td>\n      <td>mpfi</td>\n      <td>3.19</td>\n      <td>3.40</td>\n      <td>8.5</td>\n      <td>110</td>\n      <td>5500</td>\n      <td>19</td>\n      <td>25</td>\n      <td>18920</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>1</td>\n      <td>158</td>\n      <td>audi</td>\n      <td>gas</td>\n      <td>turbo</td>\n      <td>four</td>\n      <td>sedan</td>\n      <td>fwd</td>\n      <td>front</td>\n      <td>105.8</td>\n      <td>...</td>\n      <td>131</td>\n      <td>mpfi</td>\n      <td>3.13</td>\n      <td>3.40</td>\n      <td>8.3</td>\n      <td>140</td>\n      <td>5500</td>\n      <td>17</td>\n      <td>20</td>\n      <td>23875</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>0</td>\n      <td>?</td>\n      <td>audi</td>\n      <td>gas</td>\n      <td>turbo</td>\n      <td>two</td>\n      <td>hatchback</td>\n      <td>4wd</td>\n      <td>front</td>\n      <td>99.5</td>\n      <td>...</td>\n      <td>131</td>\n      <td>mpfi</td>\n      <td>3.13</td>\n      <td>3.40</td>\n      <td>7.0</td>\n      <td>160</td>\n      <td>5500</td>\n      <td>16</td>\n      <td>22</td>\n      <td>?</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>2</td>\n      <td>192</td>\n      <td>bmw</td>\n      <td>gas</td>\n      <td>std</td>\n      <td>two</td>\n      <td>sedan</td>\n      <td>rwd</td>\n      <td>front</td>\n      <td>101.2</td>\n      <td>...</td>\n      <td>108</td>\n      <td>mpfi</td>\n      <td>3.50</td>\n      <td>2.80</td>\n      <td>8.8</td>\n      <td>101</td>\n      <td>5800</td>\n      <td>23</td>\n      <td>29</td>\n      <td>16430</td>\n    </tr>\n  </tbody>\n</table>\n<p>10 rows × 26 columns</p>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "We need to replace the \"?\" symbol with NaN so the dropna() can remove the missing values:\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "df1=df.replace('?',np.NaN)\n",
      "metadata": {
        "trusted": true
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "We can drop missing values along the column \"price\" as follows:\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "df=df1.dropna(subset=[\"price\"], axis=0)\ndf.head(20)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 16,
      "outputs": [
        {
          "execution_count": 16,
          "output_type": "execute_result",
          "data": {
            "text/plain": "    symboling normalized-losses         make fuel-type aspiration  \\\n0           3               NaN  alfa-romero       gas        std   \n1           1               NaN  alfa-romero       gas        std   \n2           2               164         audi       gas        std   \n3           2               164         audi       gas        std   \n4           2               NaN         audi       gas        std   \n5           1               158         audi       gas        std   \n6           1               NaN         audi       gas        std   \n7           1               158         audi       gas      turbo   \n9           2               192          bmw       gas        std   \n10          0               192          bmw       gas        std   \n11          0               188          bmw       gas        std   \n12          0               188          bmw       gas        std   \n13          1               NaN          bmw       gas        std   \n14          0               NaN          bmw       gas        std   \n15          0               NaN          bmw       gas        std   \n16          0               NaN          bmw       gas        std   \n17          2               121    chevrolet       gas        std   \n18          1                98    chevrolet       gas        std   \n19          0                81    chevrolet       gas        std   \n20          1               118        dodge       gas        std   \n\n   num-of-doors   body-style drive-wheels engine-location  wheel-base  ...  \\\n0           two  convertible          rwd           front        88.6  ...   \n1           two    hatchback          rwd           front        94.5  ...   \n2          four        sedan          fwd           front        99.8  ...   \n3          four        sedan          4wd           front        99.4  ...   \n4           two        sedan          fwd           front        99.8  ...   \n5          four        sedan          fwd           front       105.8  ...   \n6          four        wagon          fwd           front       105.8  ...   \n7          four        sedan          fwd           front       105.8  ...   \n9           two        sedan          rwd           front       101.2  ...   \n10         four        sedan          rwd           front       101.2  ...   \n11          two        sedan          rwd           front       101.2  ...   \n12         four        sedan          rwd           front       101.2  ...   \n13         four        sedan          rwd           front       103.5  ...   \n14         four        sedan          rwd           front       103.5  ...   \n15          two        sedan          rwd           front       103.5  ...   \n16         four        sedan          rwd           front       110.0  ...   \n17          two    hatchback          fwd           front        88.4  ...   \n18          two    hatchback          fwd           front        94.5  ...   \n19         four        sedan          fwd           front        94.5  ...   \n20          two    hatchback          fwd           front        93.7  ...   \n\n    engine-size  fuel-system  bore  stroke compression-ratio horsepower  \\\n0           130         mpfi  3.47    2.68              9.00        111   \n1           152         mpfi  2.68    3.47              9.00        154   \n2           109         mpfi  3.19    3.40             10.00        102   \n3           136         mpfi  3.19    3.40              8.00        115   \n4           136         mpfi  3.19    3.40              8.50        110   \n5           136         mpfi  3.19    3.40              8.50        110   \n6           136         mpfi  3.19    3.40              8.50        110   \n7           131         mpfi  3.13    3.40              8.30        140   \n9           108         mpfi  3.50    2.80              8.80        101   \n10          108         mpfi  3.50    2.80              8.80        101   \n11          164         mpfi  3.31    3.19              9.00        121   \n12          164         mpfi  3.31    3.19              9.00        121   \n13          164         mpfi  3.31    3.19              9.00        121   \n14          209         mpfi  3.62    3.39              8.00        182   \n15          209         mpfi  3.62    3.39              8.00        182   \n16          209         mpfi  3.62    3.39              8.00        182   \n17           61         2bbl  2.91    3.03              9.50         48   \n18           90         2bbl  3.03    3.11              9.60         70   \n19           90         2bbl  3.03    3.11              9.60         70   \n20           90         2bbl  2.97    3.23              9.41         68   \n\n    peak-rpm city-mpg highway-mpg  price  \n0       5000       21          27  16500  \n1       5000       19          26  16500  \n2       5500       24          30  13950  \n3       5500       18          22  17450  \n4       5500       19          25  15250  \n5       5500       19          25  17710  \n6       5500       19          25  18920  \n7       5500       17          20  23875  \n9       5800       23          29  16430  \n10      5800       23          29  16925  \n11      4250       21          28  20970  \n12      4250       21          28  21105  \n13      4250       20          25  24565  \n14      5400       16          22  30760  \n15      5400       16          22  41315  \n16      5400       15          20  36880  \n17      5100       47          53   5151  \n18      5400       38          43   6295  \n19      5400       38          43   6575  \n20      5500       37          41   5572  \n\n[20 rows x 26 columns]",
            "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>9.00</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>1</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>9.00</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>2</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>10.00</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>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>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>8.00</td>\n      <td>115</td>\n      <td>5500</td>\n      <td>18</td>\n      <td>22</td>\n      <td>17450</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2</td>\n      <td>NaN</td>\n      <td>audi</td>\n      <td>gas</td>\n      <td>std</td>\n      <td>two</td>\n      <td>sedan</td>\n      <td>fwd</td>\n      <td>front</td>\n      <td>99.8</td>\n      <td>...</td>\n      <td>136</td>\n      <td>mpfi</td>\n      <td>3.19</td>\n      <td>3.40</td>\n      <td>8.50</td>\n      <td>110</td>\n      <td>5500</td>\n      <td>19</td>\n      <td>25</td>\n      <td>15250</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>1</td>\n      <td>158</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>105.8</td>\n      <td>...</td>\n      <td>136</td>\n      <td>mpfi</td>\n      <td>3.19</td>\n      <td>3.40</td>\n      <td>8.50</td>\n      <td>110</td>\n      <td>5500</td>\n      <td>19</td>\n      <td>25</td>\n      <td>17710</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>1</td>\n      <td>NaN</td>\n      <td>audi</td>\n      <td>gas</td>\n      <td>std</td>\n      <td>four</td>\n      <td>wagon</td>\n      <td>fwd</td>\n      <td>front</td>\n      <td>105.8</td>\n      <td>...</td>\n      <td>136</td>\n      <td>mpfi</td>\n      <td>3.19</td>\n      <td>3.40</td>\n      <td>8.50</td>\n      <td>110</td>\n      <td>5500</td>\n      <td>19</td>\n      <td>25</td>\n      <td>18920</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>1</td>\n      <td>158</td>\n      <td>audi</td>\n      <td>gas</td>\n      <td>turbo</td>\n      <td>four</td>\n      <td>sedan</td>\n      <td>fwd</td>\n      <td>front</td>\n      <td>105.8</td>\n      <td>...</td>\n      <td>131</td>\n      <td>mpfi</td>\n      <td>3.13</td>\n      <td>3.40</td>\n      <td>8.30</td>\n      <td>140</td>\n      <td>5500</td>\n      <td>17</td>\n      <td>20</td>\n      <td>23875</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>2</td>\n      <td>192</td>\n      <td>bmw</td>\n      <td>gas</td>\n      <td>std</td>\n      <td>two</td>\n      <td>sedan</td>\n      <td>rwd</td>\n      <td>front</td>\n      <td>101.2</td>\n      <td>...</td>\n      <td>108</td>\n      <td>mpfi</td>\n      <td>3.50</td>\n      <td>2.80</td>\n      <td>8.80</td>\n      <td>101</td>\n      <td>5800</td>\n      <td>23</td>\n      <td>29</td>\n      <td>16430</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>0</td>\n      <td>192</td>\n      <td>bmw</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>101.2</td>\n      <td>...</td>\n      <td>108</td>\n      <td>mpfi</td>\n      <td>3.50</td>\n      <td>2.80</td>\n      <td>8.80</td>\n      <td>101</td>\n      <td>5800</td>\n      <td>23</td>\n      <td>29</td>\n      <td>16925</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>0</td>\n      <td>188</td>\n      <td>bmw</td>\n      <td>gas</td>\n      <td>std</td>\n      <td>two</td>\n      <td>sedan</td>\n      <td>rwd</td>\n      <td>front</td>\n      <td>101.2</td>\n      <td>...</td>\n      <td>164</td>\n      <td>mpfi</td>\n      <td>3.31</td>\n      <td>3.19</td>\n      <td>9.00</td>\n      <td>121</td>\n      <td>4250</td>\n      <td>21</td>\n      <td>28</td>\n      <td>20970</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>0</td>\n      <td>188</td>\n      <td>bmw</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>101.2</td>\n      <td>...</td>\n      <td>164</td>\n      <td>mpfi</td>\n      <td>3.31</td>\n      <td>3.19</td>\n      <td>9.00</td>\n      <td>121</td>\n      <td>4250</td>\n      <td>21</td>\n      <td>28</td>\n      <td>21105</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>1</td>\n      <td>NaN</td>\n      <td>bmw</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>103.5</td>\n      <td>...</td>\n      <td>164</td>\n      <td>mpfi</td>\n      <td>3.31</td>\n      <td>3.19</td>\n      <td>9.00</td>\n      <td>121</td>\n      <td>4250</td>\n      <td>20</td>\n      <td>25</td>\n      <td>24565</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>0</td>\n      <td>NaN</td>\n      <td>bmw</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>103.5</td>\n      <td>...</td>\n      <td>209</td>\n      <td>mpfi</td>\n      <td>3.62</td>\n      <td>3.39</td>\n      <td>8.00</td>\n      <td>182</td>\n      <td>5400</td>\n      <td>16</td>\n      <td>22</td>\n      <td>30760</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>0</td>\n      <td>NaN</td>\n      <td>bmw</td>\n      <td>gas</td>\n      <td>std</td>\n      <td>two</td>\n      <td>sedan</td>\n      <td>rwd</td>\n      <td>front</td>\n      <td>103.5</td>\n      <td>...</td>\n      <td>209</td>\n      <td>mpfi</td>\n      <td>3.62</td>\n      <td>3.39</td>\n      <td>8.00</td>\n      <td>182</td>\n      <td>5400</td>\n      <td>16</td>\n      <td>22</td>\n      <td>41315</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>0</td>\n      <td>NaN</td>\n      <td>bmw</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>110.0</td>\n      <td>...</td>\n      <td>209</td>\n      <td>mpfi</td>\n      <td>3.62</td>\n      <td>3.39</td>\n      <td>8.00</td>\n      <td>182</td>\n      <td>5400</td>\n      <td>15</td>\n      <td>20</td>\n      <td>36880</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>2</td>\n      <td>121</td>\n      <td>chevrolet</td>\n      <td>gas</td>\n      <td>std</td>\n      <td>two</td>\n      <td>hatchback</td>\n      <td>fwd</td>\n      <td>front</td>\n      <td>88.4</td>\n      <td>...</td>\n      <td>61</td>\n      <td>2bbl</td>\n      <td>2.91</td>\n      <td>3.03</td>\n      <td>9.50</td>\n      <td>48</td>\n      <td>5100</td>\n      <td>47</td>\n      <td>53</td>\n      <td>5151</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>1</td>\n      <td>98</td>\n      <td>chevrolet</td>\n      <td>gas</td>\n      <td>std</td>\n      <td>two</td>\n      <td>hatchback</td>\n      <td>fwd</td>\n      <td>front</td>\n      <td>94.5</td>\n      <td>...</td>\n      <td>90</td>\n      <td>2bbl</td>\n      <td>3.03</td>\n      <td>3.11</td>\n      <td>9.60</td>\n      <td>70</td>\n      <td>5400</td>\n      <td>38</td>\n      <td>43</td>\n      <td>6295</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>0</td>\n      <td>81</td>\n      <td>chevrolet</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>94.5</td>\n      <td>...</td>\n      <td>90</td>\n      <td>2bbl</td>\n      <td>3.03</td>\n      <td>3.11</td>\n      <td>9.60</td>\n      <td>70</td>\n      <td>5400</td>\n      <td>38</td>\n      <td>43</td>\n      <td>6575</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>1</td>\n      <td>118</td>\n      <td>dodge</td>\n      <td>gas</td>\n      <td>std</td>\n      <td>two</td>\n      <td>hatchback</td>\n      <td>fwd</td>\n      <td>front</td>\n      <td>93.7</td>\n      <td>...</td>\n      <td>90</td>\n      <td>2bbl</td>\n      <td>2.97</td>\n      <td>3.23</td>\n      <td>9.41</td>\n      <td>68</td>\n      <td>5500</td>\n      <td>37</td>\n      <td>41</td>\n      <td>5572</td>\n    </tr>\n  </tbody>\n</table>\n<p>20 rows × 26 columns</p>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "Now, we have successfully read the raw dataset and added the correct headers into the dataframe.\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": " <div class=\"alert alert-danger alertdanger\" style=\"margin-top: 20px\">\n<h1> Question #2: </h1>\n<b>Find the name of the columns of the dataframe.</b>\n</div>\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "# Write your code below and press Shift+Enter to execute \nprint(df.columns)\n",
      "metadata": {
        "trusted": true
      },
      "execution_count": 17,
      "outputs": [
        {
          "name": "stdout",
          "text": "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'],\n      dtype='object')\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "<details><summary>Click here for the solution</summary>\n\n```python\nprint(df.columns)\n```\n\n</details>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<h2>Save Dataset</h2>\n<p>\nCorrespondingly, Pandas enables us to save the dataset to csv. By using the <code>dataframe.to_csv()</code> method, you can add the file path and name along with quotation marks in the brackets.\n</p>\n<p>\nFor example, if you would save the dataframe <b>df</b> as <b>automobile.csv</b> to your local machine, you may use the syntax below, where <code>index = False</code> means the row names will not be written.\n</p>\n",
      "metadata": {}
    },
    {
      "cell_type": "raw",
      "source": "df.to_csv(\"automobile.csv\", index=False)",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "We can also read and save other file formats. We can use similar functions like **`pd.read_csv()`** and **`df.to_csv()`** for other data formats. The functions are listed in the following table:\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<h2>Read/Save Other Data Formats</h2>\n\n| Data Formate |        Read       |            Save |\n| ------------ | :---------------: | --------------: |\n| csv          |  `pd.read_csv()`  |   `df.to_csv()` |\n| json         |  `pd.read_json()` |  `df.to_json()` |\n| excel        | `pd.read_excel()` | `df.to_excel()` |\n| hdf          |  `pd.read_hdf()`  |   `df.to_hdf()` |\n| sql          |  `pd.read_sql()`  |   `df.to_sql()` |\n| ...          |        ...        |             ... |\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<h1 id=\"basic_insight\">Basic Insight of Dataset</h1>\n<p>\nAfter reading data into Pandas dataframe, it is time for us to explore the dataset.<br>\n\nThere are several ways to obtain essential insights of the data to help us better understand our dataset.\n\n</p>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<h2>Data Types</h2>\n<p>\nData has a variety of types.<br>\n\nThe main types stored in Pandas dataframes are <b>object</b>, <b>float</b>, <b>int</b>, <b>bool</b> and <b>datetime64</b>. In order to better learn about each attribute, it is always good for us to know the data type of each column. In Pandas:\n\n</p>\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "df.dtypes\n",
      "metadata": {
        "trusted": true
      },
      "execution_count": 18,
      "outputs": [
        {
          "execution_count": 18,
          "output_type": "execute_result",
          "data": {
            "text/plain": "symboling              int64\nnormalized-losses     object\nmake                  object\nfuel-type             object\naspiration            object\nnum-of-doors          object\nbody-style            object\ndrive-wheels          object\nengine-location       object\nwheel-base           float64\nlength               float64\nwidth                float64\nheight               float64\ncurb-weight            int64\nengine-type           object\nnum-of-cylinders      object\nengine-size            int64\nfuel-system           object\nbore                  object\nstroke                object\ncompression-ratio    float64\nhorsepower            object\npeak-rpm              object\ncity-mpg               int64\nhighway-mpg            int64\nprice                 object\ndtype: object"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "A series with the data type of each column is returned.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "# check the data type of data frame \"df\" by .dtypes\nprint(df.dtypes)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 19,
      "outputs": [
        {
          "name": "stdout",
          "text": "symboling              int64\nnormalized-losses     object\nmake                  object\nfuel-type             object\naspiration            object\nnum-of-doors          object\nbody-style            object\ndrive-wheels          object\nengine-location       object\nwheel-base           float64\nlength               float64\nwidth                float64\nheight               float64\ncurb-weight            int64\nengine-type           object\nnum-of-cylinders      object\nengine-size            int64\nfuel-system           object\nbore                  object\nstroke                object\ncompression-ratio    float64\nhorsepower            object\npeak-rpm              object\ncity-mpg               int64\nhighway-mpg            int64\nprice                 object\ndtype: object\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "<p>\nAs shown above, it is clear to see that the data type of \"symboling\" and \"curb-weight\" are <code>int64</code>, \"normalized-losses\" is <code>object</code>, and \"wheel-base\" is <code>float64</code>, etc.\n</p>\n<p>\nThese data types can be changed; we will learn how to accomplish this in a later module.\n</p>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<h2>Describe</h2>\nIf we would like to get a statistical summary of each column e.g. count, column mean value, column standard deviation, etc., we use the describe method:\n",
      "metadata": {}
    },
    {
      "cell_type": "raw",
      "source": "dataframe.describe()",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "This method will provide various summary statistics, excluding <code>NaN</code> (Not a Number) values.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "df.describe()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 20,
      "outputs": [
        {
          "execution_count": 20,
          "output_type": "execute_result",
          "data": {
            "text/plain": "        symboling  wheel-base      length       width      height  \\\ncount  200.000000  200.000000  200.000000  200.000000  200.000000   \nmean     0.830000   98.848000  174.228000   65.898000   53.791500   \nstd      1.248557    6.038261   12.347132    2.102904    2.428449   \nmin     -2.000000   86.600000  141.100000   60.300000   47.800000   \n25%      0.000000   94.500000  166.675000   64.175000   52.000000   \n50%      1.000000   97.000000  173.200000   65.500000   54.100000   \n75%      2.000000  102.400000  183.500000   66.675000   55.525000   \nmax      3.000000  120.900000  208.100000   72.000000   59.800000   \n\n       curb-weight  engine-size  compression-ratio    city-mpg  highway-mpg  \ncount   200.000000   200.000000         200.000000  200.000000   200.000000  \nmean   2555.705000   126.860000          10.170100   25.200000    30.705000  \nstd     518.594552    41.650501           4.014163    6.432487     6.827227  \nmin    1488.000000    61.000000           7.000000   13.000000    16.000000  \n25%    2163.000000    97.750000           8.575000   19.000000    25.000000  \n50%    2414.000000   119.500000           9.000000   24.000000    30.000000  \n75%    2928.250000   142.000000           9.400000   30.000000    34.000000  \nmax    4066.000000   326.000000          23.000000   49.000000    54.000000  ",
            "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>wheel-base</th>\n      <th>length</th>\n      <th>width</th>\n      <th>height</th>\n      <th>curb-weight</th>\n      <th>engine-size</th>\n      <th>compression-ratio</th>\n      <th>city-mpg</th>\n      <th>highway-mpg</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>200.000000</td>\n      <td>200.000000</td>\n      <td>200.000000</td>\n      <td>200.000000</td>\n      <td>200.000000</td>\n      <td>200.000000</td>\n      <td>200.000000</td>\n      <td>200.000000</td>\n      <td>200.000000</td>\n      <td>200.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>0.830000</td>\n      <td>98.848000</td>\n      <td>174.228000</td>\n      <td>65.898000</td>\n      <td>53.791500</td>\n      <td>2555.705000</td>\n      <td>126.860000</td>\n      <td>10.170100</td>\n      <td>25.200000</td>\n      <td>30.705000</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>1.248557</td>\n      <td>6.038261</td>\n      <td>12.347132</td>\n      <td>2.102904</td>\n      <td>2.428449</td>\n      <td>518.594552</td>\n      <td>41.650501</td>\n      <td>4.014163</td>\n      <td>6.432487</td>\n      <td>6.827227</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>-2.000000</td>\n      <td>86.600000</td>\n      <td>141.100000</td>\n      <td>60.300000</td>\n      <td>47.800000</td>\n      <td>1488.000000</td>\n      <td>61.000000</td>\n      <td>7.000000</td>\n      <td>13.000000</td>\n      <td>16.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>0.000000</td>\n      <td>94.500000</td>\n      <td>166.675000</td>\n      <td>64.175000</td>\n      <td>52.000000</td>\n      <td>2163.000000</td>\n      <td>97.750000</td>\n      <td>8.575000</td>\n      <td>19.000000</td>\n      <td>25.000000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>1.000000</td>\n      <td>97.000000</td>\n      <td>173.200000</td>\n      <td>65.500000</td>\n      <td>54.100000</td>\n      <td>2414.000000</td>\n      <td>119.500000</td>\n      <td>9.000000</td>\n      <td>24.000000</td>\n      <td>30.000000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>2.000000</td>\n      <td>102.400000</td>\n      <td>183.500000</td>\n      <td>66.675000</td>\n      <td>55.525000</td>\n      <td>2928.250000</td>\n      <td>142.000000</td>\n      <td>9.400000</td>\n      <td>30.000000</td>\n      <td>34.000000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>3.000000</td>\n      <td>120.900000</td>\n      <td>208.100000</td>\n      <td>72.000000</td>\n      <td>59.800000</td>\n      <td>4066.000000</td>\n      <td>326.000000</td>\n      <td>23.000000</td>\n      <td>49.000000</td>\n      <td>54.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "<p>\nThis shows the statistical summary of all numeric-typed (int, float) columns.<br>\n\nFor example, the attribute \"symboling\" has 205 counts, the mean value of this column is 0.83, the standard deviation is 1.25, the minimum value is -2, 25th percentile is 0, 50th percentile is 1, 75th percentile is 2, and the maximum value is 3. <br>\n\nHowever, what if we would also like to check all the columns including those that are of type object? <br><br>\n\nYou can add an argument <code>include = \"all\"</code> inside the bracket. Let's try it again.\n\n</p>\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "# describe all the columns in \"df\" \ndf.describe(include = \"all\")",
      "metadata": {
        "trusted": true
      },
      "execution_count": 21,
      "outputs": [
        {
          "execution_count": 21,
          "output_type": "execute_result",
          "data": {
            "text/plain": "         symboling normalized-losses    make fuel-type aspiration  \\\ncount   200.000000               164     200       200        200   \nunique         NaN                51      22         2          2   \ntop            NaN               161  toyota       gas        std   \nfreq           NaN                11      32       180        164   \nmean      0.830000               NaN     NaN       NaN        NaN   \nstd       1.248557               NaN     NaN       NaN        NaN   \nmin      -2.000000               NaN     NaN       NaN        NaN   \n25%       0.000000               NaN     NaN       NaN        NaN   \n50%       1.000000               NaN     NaN       NaN        NaN   \n75%       2.000000               NaN     NaN       NaN        NaN   \nmax       3.000000               NaN     NaN       NaN        NaN   \n\n       num-of-doors body-style drive-wheels engine-location  wheel-base  ...  \\\ncount           198        200          200             200  200.000000  ...   \nunique            2          5            3               2         NaN  ...   \ntop            four      sedan          fwd           front         NaN  ...   \nfreq            113         94          118             197         NaN  ...   \nmean            NaN        NaN          NaN             NaN   98.848000  ...   \nstd             NaN        NaN          NaN             NaN    6.038261  ...   \nmin             NaN        NaN          NaN             NaN   86.600000  ...   \n25%             NaN        NaN          NaN             NaN   94.500000  ...   \n50%             NaN        NaN          NaN             NaN   97.000000  ...   \n75%             NaN        NaN          NaN             NaN  102.400000  ...   \nmax             NaN        NaN          NaN             NaN  120.900000  ...   \n\n        engine-size  fuel-system  bore  stroke compression-ratio horsepower  \\\ncount    200.000000          200   196     196        200.000000        198   \nunique          NaN            8    38      36               NaN         58   \ntop             NaN         mpfi  3.62    3.40               NaN         68   \nfreq            NaN           91    23      19               NaN         19   \nmean     126.860000          NaN   NaN     NaN         10.170100        NaN   \nstd       41.650501          NaN   NaN     NaN          4.014163        NaN   \nmin       61.000000          NaN   NaN     NaN          7.000000        NaN   \n25%       97.750000          NaN   NaN     NaN          8.575000        NaN   \n50%      119.500000          NaN   NaN     NaN          9.000000        NaN   \n75%      142.000000          NaN   NaN     NaN          9.400000        NaN   \nmax      326.000000          NaN   NaN     NaN         23.000000        NaN   \n\n        peak-rpm    city-mpg highway-mpg  price  \ncount        198  200.000000  200.000000    200  \nunique        22         NaN         NaN    185  \ntop         5500         NaN         NaN  16500  \nfreq          36         NaN         NaN      2  \nmean         NaN   25.200000   30.705000    NaN  \nstd          NaN    6.432487    6.827227    NaN  \nmin          NaN   13.000000   16.000000    NaN  \n25%          NaN   19.000000   25.000000    NaN  \n50%          NaN   24.000000   30.000000    NaN  \n75%          NaN   30.000000   34.000000    NaN  \nmax          NaN   49.000000   54.000000    NaN  \n\n[11 rows x 26 columns]",
            "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>count</th>\n      <td>200.000000</td>\n      <td>164</td>\n      <td>200</td>\n      <td>200</td>\n      <td>200</td>\n      <td>198</td>\n      <td>200</td>\n      <td>200</td>\n      <td>200</td>\n      <td>200.000000</td>\n      <td>...</td>\n      <td>200.000000</td>\n      <td>200</td>\n      <td>196</td>\n      <td>196</td>\n      <td>200.000000</td>\n      <td>198</td>\n      <td>198</td>\n      <td>200.000000</td>\n      <td>200.000000</td>\n      <td>200</td>\n    </tr>\n    <tr>\n      <th>unique</th>\n      <td>NaN</td>\n      <td>51</td>\n      <td>22</td>\n      <td>2</td>\n      <td>2</td>\n      <td>2</td>\n      <td>5</td>\n      <td>3</td>\n      <td>2</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>8</td>\n      <td>38</td>\n      <td>36</td>\n      <td>NaN</td>\n      <td>58</td>\n      <td>22</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>185</td>\n    </tr>\n    <tr>\n      <th>top</th>\n      <td>NaN</td>\n      <td>161</td>\n      <td>toyota</td>\n      <td>gas</td>\n      <td>std</td>\n      <td>four</td>\n      <td>sedan</td>\n      <td>fwd</td>\n      <td>front</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>mpfi</td>\n      <td>3.62</td>\n      <td>3.40</td>\n      <td>NaN</td>\n      <td>68</td>\n      <td>5500</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>16500</td>\n    </tr>\n    <tr>\n      <th>freq</th>\n      <td>NaN</td>\n      <td>11</td>\n      <td>32</td>\n      <td>180</td>\n      <td>164</td>\n      <td>113</td>\n      <td>94</td>\n      <td>118</td>\n      <td>197</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>91</td>\n      <td>23</td>\n      <td>19</td>\n      <td>NaN</td>\n      <td>19</td>\n      <td>36</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>0.830000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>98.848000</td>\n      <td>...</td>\n      <td>126.860000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>10.170100</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>25.200000</td>\n      <td>30.705000</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>1.248557</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>6.038261</td>\n      <td>...</td>\n      <td>41.650501</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>4.014163</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>6.432487</td>\n      <td>6.827227</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>-2.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>86.600000</td>\n      <td>...</td>\n      <td>61.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>7.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>13.000000</td>\n      <td>16.000000</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>94.500000</td>\n      <td>...</td>\n      <td>97.750000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>8.575000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>19.000000</td>\n      <td>25.000000</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>1.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>97.000000</td>\n      <td>...</td>\n      <td>119.500000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>9.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>24.000000</td>\n      <td>30.000000</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>2.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>102.400000</td>\n      <td>...</td>\n      <td>142.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>9.400000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>30.000000</td>\n      <td>34.000000</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>3.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>120.900000</td>\n      <td>...</td>\n      <td>326.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>23.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>49.000000</td>\n      <td>54.000000</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n<p>11 rows × 26 columns</p>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "<p>\nNow it provides the statistical summary of all the columns, including object-typed attributes.<br>\n\nWe can now see how many unique values there, which one is the top value and the frequency of top value in the object-typed columns.<br>\n\nSome values in the table above show as \"NaN\". This is because those numbers are not available regarding a particular column type.<br>\n\n</p>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<div class=\"alert alert-danger alertdanger\" style=\"margin-top: 20px\">\n<h1> Question #3: </h1>\n\n<p>\nYou can select the columns of a dataframe by indicating the name of each column. For example, you can select the three columns as follows:\n</p>\n<p>\n    <code>dataframe[[' column 1 ',column 2', 'column 3']]</code>\n</p>\n<p>\nWhere \"column\" is the name of the column, you can apply the method  \".describe()\" to get the statistics of those columns as follows:\n</p>\n<p>\n    <code>dataframe[[' column 1 ',column 2', 'column 3'] ].describe()</code>\n</p>\n\nApply the  method to \".describe()\" to the columns 'length' and 'compression-ratio'.\n\n</div>\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "# Write your code below and press Shift+Enter to execute \ndf[['length', 'compression-ratio']].describe()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 22,
      "outputs": [
        {
          "execution_count": 22,
          "output_type": "execute_result",
          "data": {
            "text/plain": "           length  compression-ratio\ncount  200.000000         200.000000\nmean   174.228000          10.170100\nstd     12.347132           4.014163\nmin    141.100000           7.000000\n25%    166.675000           8.575000\n50%    173.200000           9.000000\n75%    183.500000           9.400000\nmax    208.100000          23.000000",
            "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>compression-ratio</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>200.000000</td>\n      <td>200.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>174.228000</td>\n      <td>10.170100</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>12.347132</td>\n      <td>4.014163</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>141.100000</td>\n      <td>7.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>166.675000</td>\n      <td>8.575000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>173.200000</td>\n      <td>9.000000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>183.500000</td>\n      <td>9.400000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>208.100000</td>\n      <td>23.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "<details><summary>Click here for the solution</summary>\n\n```python\ndf[['length', 'compression-ratio']].describe()\n```\n\n</details>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<h2>Info</h2>\nAnother method you can use to check your dataset is:\n",
      "metadata": {}
    },
    {
      "cell_type": "raw",
      "source": "dataframe.info()",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "It provides a concise summary of your DataFrame.\n\nThis method prints information about a DataFrame including the index dtype and columns, non-null values and memory usage.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "# look at the info of \"df\"\ndf.info()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 23,
      "outputs": [
        {
          "name": "stdout",
          "text": "<class 'pandas.core.frame.DataFrame'>\nInt64Index: 200 entries, 0 to 203\nData columns (total 26 columns):\n #   Column             Non-Null Count  Dtype  \n---  ------             --------------  -----  \n 0   symboling          200 non-null    int64  \n 1   normalized-losses  164 non-null    object \n 2   make               200 non-null    object \n 3   fuel-type          200 non-null    object \n 4   aspiration         200 non-null    object \n 5   num-of-doors       198 non-null    object \n 6   body-style         200 non-null    object \n 7   drive-wheels       200 non-null    object \n 8   engine-location    200 non-null    object \n 9   wheel-base         200 non-null    float64\n 10  length             200 non-null    float64\n 11  width              200 non-null    float64\n 12  height             200 non-null    float64\n 13  curb-weight        200 non-null    int64  \n 14  engine-type        200 non-null    object \n 15  num-of-cylinders   200 non-null    object \n 16  engine-size        200 non-null    int64  \n 17  fuel-system        200 non-null    object \n 18  bore               196 non-null    object \n 19  stroke             196 non-null    object \n 20  compression-ratio  200 non-null    float64\n 21  horsepower         198 non-null    object \n 22  peak-rpm           198 non-null    object \n 23  city-mpg           200 non-null    int64  \n 24  highway-mpg        200 non-null    int64  \n 25  price              200 non-null    object \ndtypes: float64(5), int64(5), object(16)\nmemory usage: 29.7+ KB\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "<h1>Excellent! You have just completed the  Introduction Notebook!</h1>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "### Thank you for completing this lab!\n\n## Author\n\n<a href=\"https://www.linkedin.com/in/joseph-s-50398b136/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDA0101ENSkillsNetwork20235326-2021-01-01\" target=\"_blank\">Joseph Santarcangelo</a>\n\n### Other Contributors\n\n<a href=\"https://www.linkedin.com/in/mahdi-noorian-58219234/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDA0101ENSkillsNetwork20235326-2021-01-01\" target=\"_blank\">Mahdi Noorian PhD</a>\n\nBahare Talayian\n\nEric Xiao\n\nSteven Dong\n\nParizad\n\nHima Vasudevan\n\n<a href=\"https://www.linkedin.com/in/fiorellawever/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDA0101ENSkillsNetwork20235326-2021-01-01\" target=\"_blank\">Fiorella Wenver</a>\n\n<a href=\"https:// https://www.linkedin.com/in/yi-leng-yao-84451275/ \" target=\"_blank\" >Yi Yao</a>.\n\n## Change Log\n\n| Date (YYYY-MM-DD) | Version | Changed By | Change Description                                         |\n| ----------------- | ------- | ---------- | ---------------------------------------------------------- |\n| 2023-08-05        | 2.5     | Nafisa     | Solve the 3 queries for the Questions and Moved lab to course repo in Github |\n| 2022-08-23        | 2.4     | Malika     | Import micropip added and parameter for ipwidgets and tqdm |\n| 2020-10-30        | 2.3     | Lakshmi    | Changed URL of the csv                                     |\n| 2020-09-22        | 2.2     | Nayef      | Added replace() method to remove '?'                       |\n| 2020-09-09        | 2.1     | Lakshmi    | Made changes in info method of dataframe                   |\n| 2020-08-27        | 2.0     | Lavanya    | Moved lab to course repo in GitLab                         |\n\n<hr>\n\n## <h3 align=\"center\"> © IBM Corporation 2020. All rights reserved. <h3/>\n",
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    },
    {
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      "source": "",
      "metadata": {},
      "execution_count": null,
      "outputs": []
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  ]
}