2. Web Scraping.ipynb

{
    "cells": [
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "<center>\n    <img src=\"https://gitlab.com/ibm/skills-network/courses/placeholder101/-/raw/master/labs/module%201/images/IDSNlogo.png\" width=\"300\" alt=\"cognitiveclass.ai logo\"  />\n</center>\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "# **Hands-on Lab : Web Scraping**\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Estimated time needed: **30 to 45** minutes\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "## Objectives\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "In this lab you will perform the following:\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "-   Extract information from a given web site \n-   Write the scraped data into a csv file.\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "## Extract information from the given web site\n\nYou will extract the data from the below web site: <br> \n"
        },
        {
            "cell_type": "code",
            "execution_count": 1,
            "metadata": {},
            "outputs": [],
            "source": "#this url contains the data you need to scrape\nurl = \"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBM-DA0321EN-SkillsNetwork/labs/datasets/Programming_Languages.html\""
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "The data you need to scrape is the **name of the programming language** and **average annual salary**.<br> It is a good idea to open the url in your web broswer and study the contents of the web page before you start to scrape.\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Import the required libraries\n"
        },
        {
            "cell_type": "code",
            "execution_count": 2,
            "metadata": {},
            "outputs": [],
            "source": "# Your code here\nfrom bs4 import BeautifulSoup \nimport requests"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Download the webpage at the url\n"
        },
        {
            "cell_type": "code",
            "execution_count": 3,
            "metadata": {},
            "outputs": [],
            "source": "#your code goes here\ndata = requests.get(url).text"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Create a soup object\n"
        },
        {
            "cell_type": "code",
            "execution_count": 4,
            "metadata": {},
            "outputs": [],
            "source": "#your code goes here\nsoup = BeautifulSoup(data,'html5lib')"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Scrape the `Language name` and `annual average salary`.\n"
        },
        {
            "cell_type": "code",
            "execution_count": 5,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "Language------------------->Average Annual Salary\nPython------------------->$114,383\nJava------------------->$101,013\nR------------------->$92,037\nJavascript------------------->$110,981\nSwift------------------->$130,801\nC++------------------->$113,865\nC#------------------->$88,726\nPHP------------------->$84,727\nSQL------------------->$84,793\nGo------------------->$94,082\n"
                }
            ],
            "source": "#your code goes here\ntable = soup.find('table')\nl1 = []\nl2 = []\nfor row in table.find_all('tr'):\n    cols = row.find_all('td')\n    langn = cols[1].getText()\n    anavgsal = cols[3].getText()\n    l1.append(langn)\n    l2.append(anavgsal)\n    print(f\"{langn}------------------->{anavgsal}\")"
        },
        {
            "cell_type": "code",
            "execution_count": 6,
            "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>Language</th>\n      <th>Average Annual Salary</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1</th>\n      <td>Python</td>\n      <td>$114,383</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Java</td>\n      <td>$101,013</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>R</td>\n      <td>$92,037</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Javascript</td>\n      <td>$110,981</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Swift</td>\n      <td>$130,801</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>C++</td>\n      <td>$113,865</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>C#</td>\n      <td>$88,726</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>PHP</td>\n      <td>$84,727</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>SQL</td>\n      <td>$84,793</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>Go</td>\n      <td>$94,082</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
                        "text/plain": "      Language Average Annual Salary\n1       Python              $114,383\n2         Java              $101,013\n3            R               $92,037\n4   Javascript              $110,981\n5        Swift              $130,801\n6          C++              $113,865\n7           C#               $88,726\n8          PHP               $84,727\n9          SQL               $84,793\n10          Go               $94,082"
                    },
                    "execution_count": 6,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "# your code goes here\nimport pandas as pd\n\nsal_df=pd.DataFrame({'Language': l1, 'Average Annual Salary': l2})\nsal_df.drop([0],axis = 0,inplace = True)\n\nsal_df"
        },
        {
            "cell_type": "code",
            "execution_count": 7,
            "metadata": {
                "scrolled": true
            },
            "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>Language</th>\n      <th>Average Annual Salary</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1</th>\n      <td>Python</td>\n      <td>114383</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Java</td>\n      <td>101013</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>R</td>\n      <td>92037</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Javascript</td>\n      <td>110981</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Swift</td>\n      <td>130801</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>C++</td>\n      <td>113865</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>C#</td>\n      <td>88726</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>PHP</td>\n      <td>84727</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>SQL</td>\n      <td>84793</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>Go</td>\n      <td>94082</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
                        "text/plain": "      Language  Average Annual Salary\n1       Python                 114383\n2         Java                 101013\n3            R                  92037\n4   Javascript                 110981\n5        Swift                 130801\n6          C++                 113865\n7           C#                  88726\n8          PHP                  84727\n9          SQL                  84793\n10          Go                  94082"
                    },
                    "execution_count": 7,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "sal_df['Average Annual Salary']=sal_df['Average Annual Salary'].str.replace(\",\",\"\").str.replace(\"$\",\" \").astype('int64')\nsal_df"
        },
        {
            "cell_type": "code",
            "execution_count": 8,
            "metadata": {},
            "outputs": [],
            "source": "sal_df.sort_values('Average Annual Salary',ascending = False,inplace = True)\nsal_df\nsal_df.reset_index(drop = True,inplace =True)"
        },
        {
            "cell_type": "code",
            "execution_count": 9,
            "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>Language</th>\n      <th>Average Annual Salary</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Swift</td>\n      <td>130801</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Python</td>\n      <td>114383</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>C++</td>\n      <td>113865</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Javascript</td>\n      <td>110981</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Java</td>\n      <td>101013</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Go</td>\n      <td>94082</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>R</td>\n      <td>92037</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>C#</td>\n      <td>88726</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>SQL</td>\n      <td>84793</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>PHP</td>\n      <td>84727</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
                        "text/plain": "     Language  Average Annual Salary\n0       Swift                 130801\n1      Python                 114383\n2         C++                 113865\n3  Javascript                 110981\n4        Java                 101013\n5          Go                  94082\n6           R                  92037\n7          C#                  88726\n8         SQL                  84793\n9         PHP                  84727"
                    },
                    "execution_count": 9,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "sal_df"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Save the scrapped data into a file named _popular-languages.csv_\n"
        },
        {
            "cell_type": "code",
            "execution_count": 10,
            "metadata": {},
            "outputs": [],
            "source": "sal_df.to_csv(\"popular-languages.csv\")"
        },
        {
            "cell_type": "code",
            "execution_count": 11,
            "metadata": {},
            "outputs": [],
            "source": "import pandas as pd\nmdf = pd.read_csv('popular-languages.csv')\nmdf.drop('Unnamed: 0',axis = 1,inplace=True)\nmdf = mdf.set_index('Language')"
        },
        {
            "cell_type": "code",
            "execution_count": 12,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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\n",
                        "text/plain": "<Figure size 1584x648 with 1 Axes>"
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": "# mdf.drop('index',axis = 1,inplace = True)\n\nimport matplotlib.pyplot as plt\n\nmdf.plot(kind = 'bar',figsize = (22,9))\nplt.xticks(rotation = 0)\nplt.show()"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "## Authors\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Ramesh Sannareddy\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "### Other Contributors\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Rav Ahuja\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "## Change Log\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "| Date (YYYY-MM-DD) | Version | Changed By        | Change Description                 |\n| ----------------- | ------- | ----------------- | ---------------------------------- |\n| 2020-10-17        | 0.1     | Ramesh Sannareddy | Created initial version of the lab |\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": " Copyright \u00a9 2020 IBM Corporation. This notebook and its source code are released under the terms of the [MIT License](https://cognitiveclass.ai/mit-license?cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBM-DA0321EN-SkillsNetwork-21426264&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ&cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBM-DA0321EN-SkillsNetwork-21426264&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ&cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBM-DA0321EN-SkillsNetwork-21426264&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ&cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBM-DA0321EN-SkillsNetwork-21426264&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ).\n"
        }
    ],
    "metadata": {
        "kernelspec": {
            "display_name": "Python 3.7",
            "language": "python",
            "name": "python3"
        },
        "language_info": {
            "codemirror_mode": {
                "name": "ipython",
                "version": 3
            },
            "file_extension": ".py",
            "mimetype": "text/x-python",
            "name": "python",
            "nbconvert_exporter": "python",
            "pygments_lexer": "ipython3",
            "version": "3.7.10"
        }
    },
    "nbformat": 4,
    "nbformat_minor": 4
}