introduction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center>\n",
    "    <img src=\"https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/Logos/organization_logo/organization_logo.png\" width=\"300\" alt=\"cognitiveclass.ai logo\"  />\n",
    "</center>\n",
    "\n",
    "# Introduction  Notebook\n",
    "\n",
    "Estimated time needed: **10** minutes\n",
    "\n",
    "## Objectives\n",
    "\n",
    "After completing this lab you will be able to:\n",
    "\n",
    "-   Acquire data in various ways\n",
    "-   Obtain insights from Data with Pandas library\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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=\"#data_acquisition\">Data Acquisition</a>\n",
    "    <li><a href=\"#basic_insight\">Basic Insight of Dataset</a></li>\n",
    "</ol>\n",
    "\n",
    "</div>\n",
    "<hr>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1 id=\"data_acquisition\">Data Acquisition</h1>\n",
    "<p>\n",
    "There 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",
    "In this section, you will learn how to load a dataset into our Jupyter Notebook.<br>\n",
    "In our case, the Automobile Dataset is an online source, and it is in CSV (comma separated value) format. Let's use this dataset as an example to practice data reading.\n",
    "<ul>\n",
    "    <li>data source: <a href=\"https://archive.ics.uci.edu/ml/machine-learning-databases/autos/imports-85.data\" 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>\n",
    "The Pandas Library is a useful tool that enables us to read various datasets into a data frame; 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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import pandas library\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Read Data</h2>\n",
    "<p>\n",
    "We use <code>pandas.read_csv()</code> function to read the csv file. In the bracket, we put the file path along with a quotation mark, so that pandas will read the file into a data frame from that address. The file path can be either an URL or your local file address.<br>\n",
    "Because 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",
    "You can also assign the dataset to any variable you create.\n",
    "</p>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This dataset was hosted on IBM Cloud object click <a href=\"https://cocl.us/DA101EN_object_storage\">HERE</a> for free storage.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import pandas library\n",
    "import pandas as pd\n",
    "\n",
    "# Read the online file by the URL provides above, and assign it to variable \"df\"\n",
    "other_path = \"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DA0101EN-SkillsNetwork/labs/Data%20files/auto.csv\"\n",
    "df = pd.read_csv(other_path, header=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# show the first 5 rows using dataframe.head() method\n",
    "print(\"The first 5 rows of the dataframe\") \n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Write your code below and press Shift+Enter to execute \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details><summary>Click here for the solution</summary>\n",
    "\n",
    "```python\n",
    "print(\"The last 10 rows of the dataframe\\n\")\n",
    "df.tail(10)\n",
    "```\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Add Headers</h3>\n",
    "<p>\n",
    "Take a look at our dataset; pandas automatically set the header by an integer from 0.\n",
    "</p>\n",
    "<p>\n",
    "To better describe our data we can introduce a header, this information is available at:  <a href=\"https://archive.ics.uci.edu/ml/datasets/Automobile\" target=\"_blank\">https://archive.ics.uci.edu/ml/datasets/Automobile</a>\n",
    "</p>\n",
    "<p>\n",
    "Thus, we have to add headers manually.\n",
    "</p>\n",
    "<p>\n",
    "Firstly, we create a list \"headers\" that include all column names in order.\n",
    "Then, we use <code>dataframe.columns = headers</code> to replace the headers by the list we created.\n",
    "</p>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "headers\n",
      " ['symboling', 'normalized-losses', 'make', 'fuel-type', 'aspiration', 'num-of-doors', 'body-style', 'drive-wheels', 'engine-location', 'wheel-base', 'length', 'width', 'height', 'curb-weight', 'engine-type', 'num-of-cylinders', 'engine-size', 'fuel-system', 'bore', 'stroke', 'compression-ratio', 'horsepower', 'peak-rpm', 'city-mpg', 'highway-mpg', 'price']\n"
     ]
    }
   ],
   "source": [
    "# create headers list\n",
    "headers = [\"symboling\",\"normalized-losses\",\"make\",\"fuel-type\",\"aspiration\", \"num-of-doors\",\"body-style\",\n",
    "         \"drive-wheels\",\"engine-location\",\"wheel-base\", \"length\",\"width\",\"height\",\"curb-weight\",\"engine-type\",\n",
    "         \"num-of-cylinders\", \"engine-size\",\"fuel-system\",\"bore\",\"stroke\",\"compression-ratio\",\"horsepower\",\n",
    "         \"peak-rpm\",\"city-mpg\",\"highway-mpg\",\"price\"]\n",
    "print(\"headers\\n\", headers)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " We replace headers and recheck our data frame\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>symboling</th>\n",
       "      <th>normalized-losses</th>\n",
       "      <th>make</th>\n",
       "      <th>fuel-type</th>\n",
       "      <th>aspiration</th>\n",
       "      <th>num-of-doors</th>\n",
       "      <th>body-style</th>\n",
       "      <th>drive-wheels</th>\n",
       "      <th>engine-location</th>\n",
       "      <th>wheel-base</th>\n",
       "      <th>...</th>\n",
       "      <th>engine-size</th>\n",
       "      <th>fuel-system</th>\n",
       "      <th>bore</th>\n",
       "      <th>stroke</th>\n",
       "      <th>compression-ratio</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>peak-rpm</th>\n",
       "      <th>city-mpg</th>\n",
       "      <th>highway-mpg</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>?</td>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>convertible</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>88.6</td>\n",
       "      <td>...</td>\n",
       "      <td>130</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.47</td>\n",
       "      <td>2.68</td>\n",
       "      <td>9.0</td>\n",
       "      <td>111</td>\n",
       "      <td>5000</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>13495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>?</td>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>convertible</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>88.6</td>\n",
       "      <td>...</td>\n",
       "      <td>130</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.47</td>\n",
       "      <td>2.68</td>\n",
       "      <td>9.0</td>\n",
       "      <td>111</td>\n",
       "      <td>5000</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>16500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>?</td>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>94.5</td>\n",
       "      <td>...</td>\n",
       "      <td>152</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>2.68</td>\n",
       "      <td>3.47</td>\n",
       "      <td>9.0</td>\n",
       "      <td>154</td>\n",
       "      <td>5000</td>\n",
       "      <td>19</td>\n",
       "      <td>26</td>\n",
       "      <td>16500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>164</td>\n",
       "      <td>audi</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>99.8</td>\n",
       "      <td>...</td>\n",
       "      <td>109</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>10.0</td>\n",
       "      <td>102</td>\n",
       "      <td>5500</td>\n",
       "      <td>24</td>\n",
       "      <td>30</td>\n",
       "      <td>13950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>164</td>\n",
       "      <td>audi</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>4wd</td>\n",
       "      <td>front</td>\n",
       "      <td>99.4</td>\n",
       "      <td>...</td>\n",
       "      <td>136</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>8.0</td>\n",
       "      <td>115</td>\n",
       "      <td>5500</td>\n",
       "      <td>18</td>\n",
       "      <td>22</td>\n",
       "      <td>17450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</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>6</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>7</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>8</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>9</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",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   symboling normalized-losses         make fuel-type aspiration num-of-doors  \\\n",
       "0          3                 ?  alfa-romero       gas        std          two   \n",
       "1          3                 ?  alfa-romero       gas        std          two   \n",
       "2          1                 ?  alfa-romero       gas        std          two   \n",
       "3          2               164         audi       gas        std         four   \n",
       "4          2               164         audi       gas        std         four   \n",
       "5          2                 ?         audi       gas        std          two   \n",
       "6          1               158         audi       gas        std         four   \n",
       "7          1                 ?         audi       gas        std         four   \n",
       "8          1               158         audi       gas      turbo         four   \n",
       "9          0                 ?         audi       gas      turbo          two   \n",
       "\n",
       "    body-style drive-wheels engine-location  wheel-base  ...  engine-size  \\\n",
       "0  convertible          rwd           front        88.6  ...          130   \n",
       "1  convertible          rwd           front        88.6  ...          130   \n",
       "2    hatchback          rwd           front        94.5  ...          152   \n",
       "3        sedan          fwd           front        99.8  ...          109   \n",
       "4        sedan          4wd           front        99.4  ...          136   \n",
       "5        sedan          fwd           front        99.8  ...          136   \n",
       "6        sedan          fwd           front       105.8  ...          136   \n",
       "7        wagon          fwd           front       105.8  ...          136   \n",
       "8        sedan          fwd           front       105.8  ...          131   \n",
       "9    hatchback          4wd           front        99.5  ...          131   \n",
       "\n",
       "   fuel-system  bore  stroke compression-ratio horsepower  peak-rpm city-mpg  \\\n",
       "0         mpfi  3.47    2.68               9.0        111      5000       21   \n",
       "1         mpfi  3.47    2.68               9.0        111      5000       21   \n",
       "2         mpfi  2.68    3.47               9.0        154      5000       19   \n",
       "3         mpfi  3.19    3.40              10.0        102      5500       24   \n",
       "4         mpfi  3.19    3.40               8.0        115      5500       18   \n",
       "5         mpfi  3.19    3.40               8.5        110      5500       19   \n",
       "6         mpfi  3.19    3.40               8.5        110      5500       19   \n",
       "7         mpfi  3.19    3.40               8.5        110      5500       19   \n",
       "8         mpfi  3.13    3.40               8.3        140      5500       17   \n",
       "9         mpfi  3.13    3.40               7.0        160      5500       16   \n",
       "\n",
       "  highway-mpg  price  \n",
       "0          27  13495  \n",
       "1          27  16500  \n",
       "2          26  16500  \n",
       "3          30  13950  \n",
       "4          22  17450  \n",
       "5          25  15250  \n",
       "6          25  17710  \n",
       "7          25  18920  \n",
       "8          20  23875  \n",
       "9          22      ?  \n",
       "\n",
       "[10 rows x 26 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns = headers\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "we need to replace the \"?\" symbol with NaN so the dropna() can remove the missing values \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1=df.replace('?',np.NaN)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "we can drop missing values along the column \"price\" as follows  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>symboling</th>\n",
       "      <th>normalized-losses</th>\n",
       "      <th>make</th>\n",
       "      <th>fuel-type</th>\n",
       "      <th>aspiration</th>\n",
       "      <th>num-of-doors</th>\n",
       "      <th>body-style</th>\n",
       "      <th>drive-wheels</th>\n",
       "      <th>engine-location</th>\n",
       "      <th>wheel-base</th>\n",
       "      <th>...</th>\n",
       "      <th>engine-size</th>\n",
       "      <th>fuel-system</th>\n",
       "      <th>bore</th>\n",
       "      <th>stroke</th>\n",
       "      <th>compression-ratio</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>peak-rpm</th>\n",
       "      <th>city-mpg</th>\n",
       "      <th>highway-mpg</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>convertible</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>88.6</td>\n",
       "      <td>...</td>\n",
       "      <td>130</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.47</td>\n",
       "      <td>2.68</td>\n",
       "      <td>9.0</td>\n",
       "      <td>111</td>\n",
       "      <td>5000</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>13495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>convertible</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>88.6</td>\n",
       "      <td>...</td>\n",
       "      <td>130</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.47</td>\n",
       "      <td>2.68</td>\n",
       "      <td>9.0</td>\n",
       "      <td>111</td>\n",
       "      <td>5000</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>16500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>94.5</td>\n",
       "      <td>...</td>\n",
       "      <td>152</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>2.68</td>\n",
       "      <td>3.47</td>\n",
       "      <td>9.0</td>\n",
       "      <td>154</td>\n",
       "      <td>5000</td>\n",
       "      <td>19</td>\n",
       "      <td>26</td>\n",
       "      <td>16500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>164</td>\n",
       "      <td>audi</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>99.8</td>\n",
       "      <td>...</td>\n",
       "      <td>109</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>10.0</td>\n",
       "      <td>102</td>\n",
       "      <td>5500</td>\n",
       "      <td>24</td>\n",
       "      <td>30</td>\n",
       "      <td>13950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>164</td>\n",
       "      <td>audi</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>4wd</td>\n",
       "      <td>front</td>\n",
       "      <td>99.4</td>\n",
       "      <td>...</td>\n",
       "      <td>136</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>8.0</td>\n",
       "      <td>115</td>\n",
       "      <td>5500</td>\n",
       "      <td>18</td>\n",
       "      <td>22</td>\n",
       "      <td>17450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</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>8.5</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>6</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>7</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>8.5</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>8</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>10</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",
       "    <tr>\n",
       "      <th>11</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>8.8</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>12</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>9.0</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>13</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>9.0</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>14</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>9.0</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>15</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>8.0</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>16</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>8.0</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>17</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>8.0</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>18</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>9.5</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>19</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>9.6</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>20</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>9.6</td>\n",
       "      <td>70</td>\n",
       "      <td>5400</td>\n",
       "      <td>38</td>\n",
       "      <td>43</td>\n",
       "      <td>6575</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    symboling normalized-losses         make fuel-type aspiration  \\\n",
       "0           3               NaN  alfa-romero       gas        std   \n",
       "1           3               NaN  alfa-romero       gas        std   \n",
       "2           1               NaN  alfa-romero       gas        std   \n",
       "3           2               164         audi       gas        std   \n",
       "4           2               164         audi       gas        std   \n",
       "5           2               NaN         audi       gas        std   \n",
       "6           1               158         audi       gas        std   \n",
       "7           1               NaN         audi       gas        std   \n",
       "8           1               158         audi       gas      turbo   \n",
       "10          2               192          bmw       gas        std   \n",
       "11          0               192          bmw       gas        std   \n",
       "12          0               188          bmw       gas        std   \n",
       "13          0               188          bmw       gas        std   \n",
       "14          1               NaN          bmw       gas        std   \n",
       "15          0               NaN          bmw       gas        std   \n",
       "16          0               NaN          bmw       gas        std   \n",
       "17          0               NaN          bmw       gas        std   \n",
       "18          2               121    chevrolet       gas        std   \n",
       "19          1                98    chevrolet       gas        std   \n",
       "20          0                81    chevrolet       gas        std   \n",
       "\n",
       "   num-of-doors   body-style drive-wheels engine-location  wheel-base  ...  \\\n",
       "0           two  convertible          rwd           front        88.6  ...   \n",
       "1           two  convertible          rwd           front        88.6  ...   \n",
       "2           two    hatchback          rwd           front        94.5  ...   \n",
       "3          four        sedan          fwd           front        99.8  ...   \n",
       "4          four        sedan          4wd           front        99.4  ...   \n",
       "5           two        sedan          fwd           front        99.8  ...   \n",
       "6          four        sedan          fwd           front       105.8  ...   \n",
       "7          four        wagon          fwd           front       105.8  ...   \n",
       "8          four        sedan          fwd           front       105.8  ...   \n",
       "10          two        sedan          rwd           front       101.2  ...   \n",
       "11         four        sedan          rwd           front       101.2  ...   \n",
       "12          two        sedan          rwd           front       101.2  ...   \n",
       "13         four        sedan          rwd           front       101.2  ...   \n",
       "14         four        sedan          rwd           front       103.5  ...   \n",
       "15         four        sedan          rwd           front       103.5  ...   \n",
       "16          two        sedan          rwd           front       103.5  ...   \n",
       "17         four        sedan          rwd           front       110.0  ...   \n",
       "18          two    hatchback          fwd           front        88.4  ...   \n",
       "19          two    hatchback          fwd           front        94.5  ...   \n",
       "20         four        sedan          fwd           front        94.5  ...   \n",
       "\n",
       "    engine-size  fuel-system  bore  stroke compression-ratio horsepower  \\\n",
       "0           130         mpfi  3.47    2.68               9.0        111   \n",
       "1           130         mpfi  3.47    2.68               9.0        111   \n",
       "2           152         mpfi  2.68    3.47               9.0        154   \n",
       "3           109         mpfi  3.19    3.40              10.0        102   \n",
       "4           136         mpfi  3.19    3.40               8.0        115   \n",
       "5           136         mpfi  3.19    3.40               8.5        110   \n",
       "6           136         mpfi  3.19    3.40               8.5        110   \n",
       "7           136         mpfi  3.19    3.40               8.5        110   \n",
       "8           131         mpfi  3.13    3.40               8.3        140   \n",
       "10          108         mpfi  3.50    2.80               8.8        101   \n",
       "11          108         mpfi  3.50    2.80               8.8        101   \n",
       "12          164         mpfi  3.31    3.19               9.0        121   \n",
       "13          164         mpfi  3.31    3.19               9.0        121   \n",
       "14          164         mpfi  3.31    3.19               9.0        121   \n",
       "15          209         mpfi  3.62    3.39               8.0        182   \n",
       "16          209         mpfi  3.62    3.39               8.0        182   \n",
       "17          209         mpfi  3.62    3.39               8.0        182   \n",
       "18           61         2bbl  2.91    3.03               9.5         48   \n",
       "19           90         2bbl  3.03    3.11               9.6         70   \n",
       "20           90         2bbl  3.03    3.11               9.6         70   \n",
       "\n",
       "    peak-rpm city-mpg highway-mpg  price  \n",
       "0       5000       21          27  13495  \n",
       "1       5000       21          27  16500  \n",
       "2       5000       19          26  16500  \n",
       "3       5500       24          30  13950  \n",
       "4       5500       18          22  17450  \n",
       "5       5500       19          25  15250  \n",
       "6       5500       19          25  17710  \n",
       "7       5500       19          25  18920  \n",
       "8       5500       17          20  23875  \n",
       "10      5800       23          29  16430  \n",
       "11      5800       23          29  16925  \n",
       "12      4250       21          28  20970  \n",
       "13      4250       21          28  21105  \n",
       "14      4250       20          25  24565  \n",
       "15      5400       16          22  30760  \n",
       "16      5400       16          22  41315  \n",
       "17      5400       15          20  36880  \n",
       "18      5100       47          53   5151  \n",
       "19      5400       38          43   6295  \n",
       "20      5400       38          43   6575  \n",
       "\n",
       "[20 rows x 26 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=df1.dropna(subset=[\"price\"], axis=0)\n",
    "df.head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we have successfully read the raw dataset and add the correct headers into the data frame.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Write your code below and press Shift+Enter to execute \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details><summary>Click here for the solution</summary>\n",
    "\n",
    "```python\n",
    "print(df.columns)\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Save Dataset</h2>\n",
    "<p>\n",
    "Correspondingly, 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>\n",
    "    For 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:\n",
    "</p>\n"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "df.to_csv(\"automobile.csv\", index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " We can also read and save other file formats, we can use similar functions to **`pd.read_csv()`** and **`df.to_csv()`** for other data formats, the functions are listed in the following table:\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1 id=\"basic_insight\">Basic Insight of Dataset</h1>\n",
    "<p>\n",
    "After reading data into Pandas dataframe, it is time for us to explore the dataset.<br>\n",
    "There are several ways to obtain essential insights of the data to help us better understand our dataset.\n",
    "</p>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Data Types</h2>\n",
    "<p>\n",
    "Data has a variety of types.<br>\n",
    "The 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",
    "</p>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "symboling              int64\n",
       "normalized-losses     object\n",
       "make                  object\n",
       "fuel-type             object\n",
       "aspiration            object\n",
       "num-of-doors          object\n",
       "body-style            object\n",
       "drive-wheels          object\n",
       "engine-location       object\n",
       "wheel-base           float64\n",
       "length               float64\n",
       "width                float64\n",
       "height               float64\n",
       "curb-weight            int64\n",
       "engine-type           object\n",
       "num-of-cylinders      object\n",
       "engine-size            int64\n",
       "fuel-system           object\n",
       "bore                  object\n",
       "stroke                object\n",
       "compression-ratio    float64\n",
       "horsepower            object\n",
       "peak-rpm              object\n",
       "city-mpg               int64\n",
       "highway-mpg            int64\n",
       "price                 object\n",
       "dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "returns a Series with the data type of each column.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "symboling              int64\n",
      "normalized-losses     object\n",
      "make                  object\n",
      "fuel-type             object\n",
      "aspiration            object\n",
      "num-of-doors          object\n",
      "body-style            object\n",
      "drive-wheels          object\n",
      "engine-location       object\n",
      "wheel-base           float64\n",
      "length               float64\n",
      "width                float64\n",
      "height               float64\n",
      "curb-weight            int64\n",
      "engine-type           object\n",
      "num-of-cylinders      object\n",
      "engine-size            int64\n",
      "fuel-system           object\n",
      "bore                  object\n",
      "stroke                object\n",
      "compression-ratio    float64\n",
      "horsepower            object\n",
      "peak-rpm              object\n",
      "city-mpg               int64\n",
      "highway-mpg            int64\n",
      "price                 object\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "# check the data type of data frame \"df\" by .dtypes\n",
    "print(df.dtypes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>\n",
    "As a result, as 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>\n",
    "These data types can be changed; we will learn how to accomplish this in a later module.\n",
    "</p>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Describe</h2>\n",
    "If we would like to get a statistical summary of each column, such as count, column mean value, column standard deviation, etc. We use the describe method:\n"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "dataframe.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This method will provide various summary statistics, excluding <code>NaN</code> (Not a Number) values.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>symboling</th>\n",
       "      <th>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>201.000000</td>\n",
       "      <td>201.000000</td>\n",
       "      <td>201.000000</td>\n",
       "      <td>201.000000</td>\n",
       "      <td>201.000000</td>\n",
       "      <td>201.000000</td>\n",
       "      <td>201.000000</td>\n",
       "      <td>201.000000</td>\n",
       "      <td>201.000000</td>\n",
       "      <td>201.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.840796</td>\n",
       "      <td>98.797015</td>\n",
       "      <td>174.200995</td>\n",
       "      <td>65.889055</td>\n",
       "      <td>53.766667</td>\n",
       "      <td>2555.666667</td>\n",
       "      <td>126.875622</td>\n",
       "      <td>10.164279</td>\n",
       "      <td>25.179104</td>\n",
       "      <td>30.686567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.254802</td>\n",
       "      <td>6.066366</td>\n",
       "      <td>12.322175</td>\n",
       "      <td>2.101471</td>\n",
       "      <td>2.447822</td>\n",
       "      <td>517.296727</td>\n",
       "      <td>41.546834</td>\n",
       "      <td>4.004965</td>\n",
       "      <td>6.423220</td>\n",
       "      <td>6.815150</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>166.800000</td>\n",
       "      <td>64.100000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>2169.000000</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>8.600000</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>120.000000</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>66.600000</td>\n",
       "      <td>55.500000</td>\n",
       "      <td>2926.000000</td>\n",
       "      <td>141.000000</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>"
      ],
      "text/plain": [
       "        symboling  wheel-base      length       width      height  \\\n",
       "count  201.000000  201.000000  201.000000  201.000000  201.000000   \n",
       "mean     0.840796   98.797015  174.200995   65.889055   53.766667   \n",
       "std      1.254802    6.066366   12.322175    2.101471    2.447822   \n",
       "min     -2.000000   86.600000  141.100000   60.300000   47.800000   \n",
       "25%      0.000000   94.500000  166.800000   64.100000   52.000000   \n",
       "50%      1.000000   97.000000  173.200000   65.500000   54.100000   \n",
       "75%      2.000000  102.400000  183.500000   66.600000   55.500000   \n",
       "max      3.000000  120.900000  208.100000   72.000000   59.800000   \n",
       "\n",
       "       curb-weight  engine-size  compression-ratio    city-mpg  highway-mpg  \n",
       "count   201.000000   201.000000         201.000000  201.000000   201.000000  \n",
       "mean   2555.666667   126.875622          10.164279   25.179104    30.686567  \n",
       "std     517.296727    41.546834           4.004965    6.423220     6.815150  \n",
       "min    1488.000000    61.000000           7.000000   13.000000    16.000000  \n",
       "25%    2169.000000    98.000000           8.600000   19.000000    25.000000  \n",
       "50%    2414.000000   120.000000           9.000000   24.000000    30.000000  \n",
       "75%    2926.000000   141.000000           9.400000   30.000000    34.000000  \n",
       "max    4066.000000   326.000000          23.000000   49.000000    54.000000  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>\n",
    "This shows the statistical summary of all numeric-typed (int, float) columns.<br>\n",
    "For 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.\n",
    "<br>\n",
    "However, what if we would also like to check all the columns including those that are of type object.\n",
    "<br><br>\n",
    "\n",
    "You can add an argument <code>include = \"all\"</code> inside the bracket. Let's try it again.\n",
    "\n",
    "</p>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\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>201.000000</td>\n",
       "      <td>164</td>\n",
       "      <td>201</td>\n",
       "      <td>201</td>\n",
       "      <td>201</td>\n",
       "      <td>199</td>\n",
       "      <td>201</td>\n",
       "      <td>201</td>\n",
       "      <td>201</td>\n",
       "      <td>201.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>201.000000</td>\n",
       "      <td>201</td>\n",
       "      <td>197</td>\n",
       "      <td>197</td>\n",
       "      <td>201.000000</td>\n",
       "      <td>199</td>\n",
       "      <td>199</td>\n",
       "      <td>201.000000</td>\n",
       "      <td>201.000000</td>\n",
       "      <td>201</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>186</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>4800</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>NaN</td>\n",
       "      <td>11</td>\n",
       "      <td>32</td>\n",
       "      <td>181</td>\n",
       "      <td>165</td>\n",
       "      <td>113</td>\n",
       "      <td>94</td>\n",
       "      <td>118</td>\n",
       "      <td>198</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>92</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>0.840796</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>98.797015</td>\n",
       "      <td>...</td>\n",
       "      <td>126.875622</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.164279</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.179104</td>\n",
       "      <td>30.686567</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.254802</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>6.066366</td>\n",
       "      <td>...</td>\n",
       "      <td>41.546834</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.004965</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.423220</td>\n",
       "      <td>6.815150</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>98.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.600000</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>120.000000</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>141.000000</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>"
      ],
      "text/plain": [
       "         symboling normalized-losses    make fuel-type aspiration  \\\n",
       "count   201.000000               164     201       201        201   \n",
       "unique         NaN                51      22         2          2   \n",
       "top            NaN               161  toyota       gas        std   \n",
       "freq           NaN                11      32       181        165   \n",
       "mean      0.840796               NaN     NaN       NaN        NaN   \n",
       "std       1.254802               NaN     NaN       NaN        NaN   \n",
       "min      -2.000000               NaN     NaN       NaN        NaN   \n",
       "25%       0.000000               NaN     NaN       NaN        NaN   \n",
       "50%       1.000000               NaN     NaN       NaN        NaN   \n",
       "75%       2.000000               NaN     NaN       NaN        NaN   \n",
       "max       3.000000               NaN     NaN       NaN        NaN   \n",
       "\n",
       "       num-of-doors body-style drive-wheels engine-location  wheel-base  ...  \\\n",
       "count           199        201          201             201  201.000000  ...   \n",
       "unique            2          5            3               2         NaN  ...   \n",
       "top            four      sedan          fwd           front         NaN  ...   \n",
       "freq            113         94          118             198         NaN  ...   \n",
       "mean            NaN        NaN          NaN             NaN   98.797015  ...   \n",
       "std             NaN        NaN          NaN             NaN    6.066366  ...   \n",
       "min             NaN        NaN          NaN             NaN   86.600000  ...   \n",
       "25%             NaN        NaN          NaN             NaN   94.500000  ...   \n",
       "50%             NaN        NaN          NaN             NaN   97.000000  ...   \n",
       "75%             NaN        NaN          NaN             NaN  102.400000  ...   \n",
       "max             NaN        NaN          NaN             NaN  120.900000  ...   \n",
       "\n",
       "        engine-size  fuel-system  bore  stroke compression-ratio horsepower  \\\n",
       "count    201.000000          201   197     197        201.000000        199   \n",
       "unique          NaN            8    38      36               NaN         58   \n",
       "top             NaN         mpfi  3.62    3.40               NaN         68   \n",
       "freq            NaN           92    23      19               NaN         19   \n",
       "mean     126.875622          NaN   NaN     NaN         10.164279        NaN   \n",
       "std       41.546834          NaN   NaN     NaN          4.004965        NaN   \n",
       "min       61.000000          NaN   NaN     NaN          7.000000        NaN   \n",
       "25%       98.000000          NaN   NaN     NaN          8.600000        NaN   \n",
       "50%      120.000000          NaN   NaN     NaN          9.000000        NaN   \n",
       "75%      141.000000          NaN   NaN     NaN          9.400000        NaN   \n",
       "max      326.000000          NaN   NaN     NaN         23.000000        NaN   \n",
       "\n",
       "        peak-rpm    city-mpg highway-mpg price  \n",
       "count        199  201.000000  201.000000   201  \n",
       "unique        22         NaN         NaN   186  \n",
       "top         4800         NaN         NaN  7295  \n",
       "freq          36         NaN         NaN     2  \n",
       "mean         NaN   25.179104   30.686567   NaN  \n",
       "std          NaN    6.423220    6.815150   NaN  \n",
       "min          NaN   13.000000   16.000000   NaN  \n",
       "25%          NaN   19.000000   25.000000   NaN  \n",
       "50%          NaN   24.000000   30.000000   NaN  \n",
       "75%          NaN   30.000000   34.000000   NaN  \n",
       "max          NaN   49.000000   54.000000   NaN  \n",
       "\n",
       "[11 rows x 26 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# describe all the columns in \"df\" \n",
    "df.describe(include = \"all\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>\n",
    "Now, it provides the statistical summary of all the columns, including object-typed attributes.<br>\n",
    "We can now see how many unique values, which is the top value and the frequency of top value in the object-typed columns.<br>\n",
    "Some values in the table above show as \"NaN\", this is because those numbers are not available regarding a particular column type.<br>\n",
    "</p>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-danger alertdanger\" style=\"margin-top: 20px\">\n",
    "<h1> Question #3: </h1>\n",
    "\n",
    "<p>\n",
    "You can select the columns of a data frame 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>\n",
    "Where \"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",
    "\n",
    "Apply the  method to \".describe()\" to the columns 'length' and 'compression-ratio'.\n",
    "\n",
    "</div>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "\"None of [Int64Index([0], dtype='int64')] are in the [columns]\"",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-13-21de5120e784>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Write your code below and press Shift+Enter to execute\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/conda/envs/python/lib/python3.6/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   2910\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_iterator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2911\u001b[0m                 \u001b[0mkey\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2912\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_listlike_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mraise_missing\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2913\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2914\u001b[0m         \u001b[0;31m# take() does not accept boolean indexers\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/conda/envs/python/lib/python3.6/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_get_listlike_indexer\u001b[0;34m(self, key, axis, raise_missing)\u001b[0m\n\u001b[1;32m   1252\u001b[0m             \u001b[0mkeyarr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnew_indexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reindex_non_unique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkeyarr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1253\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1254\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_read_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkeyarr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mraise_missing\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mraise_missing\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1255\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mkeyarr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1256\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/conda/envs/python/lib/python3.6/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_validate_read_indexer\u001b[0;34m(self, key, indexer, axis, raise_missing)\u001b[0m\n\u001b[1;32m   1296\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mmissing\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1297\u001b[0m                 \u001b[0maxis_name\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_axis_name\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1298\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"None of [{key}] are in the [{axis_name}]\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1299\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1300\u001b[0m             \u001b[0;31m# We (temporarily) allow for some missing keys with .loc, except in\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: \"None of [Int64Index([0], dtype='int64')] are in the [columns]\""
     ]
    }
   ],
   "source": [
    "# Write your code below and press Shift+Enter to execute \n",
    "df[[0]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details><summary>Click here for the solution</summary>\n",
    "\n",
    "```python\n",
    "df[['length', 'compression-ratio']].describe()\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Info</h2>\n",
    "Another method you can use to check your dataset is:\n"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "dataframe.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It provide a concise summary of your DataFrame. \n",
    "\n",
    "This method prints information about a DataFrame including the index dtype and columns, non-null values and memory usage.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# look at the info of \"df\"\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>Excellent! You have just completed the  Introduction  Notebook!</h1>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Thank you for completing this lab!\n",
    "\n",
    "## Author\n",
    "\n",
    "<a href=\"https://www.linkedin.com/in/joseph-s-50398b136/\" target=\"_blank\">Joseph Santarcangelo</a>\n",
    "\n",
    "### Other Contributors\n",
    "\n",
    "<a href=\"https://www.linkedin.com/in/mahdi-noorian-58219234/\" target=\"_blank\">Mahdi Noorian PhD</a>\n",
    "\n",
    "Bahare Talayian\n",
    "\n",
    "Eric Xiao\n",
    "\n",
    "Steven Dong\n",
    "\n",
    "Parizad\n",
    "\n",
    "Hima Vasudevan\n",
    "\n",
    "<a href=\"https://www.linkedin.com/in/fiorellawever/\" target=\"_blank\">Fiorella Wenver</a>\n",
    "\n",
    "<a href=\" 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",
    "| 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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