Web-Scraping-Lab.ipynb

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   "source": [
    "<p style=\"text-align:center\">\n",
    "    <a href=\"https://skills.network/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDA0321ENSkillsNetwork928-2022-01-01\" target=\"_blank\">\n",
    "    <img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/assets/logos/SN_web_lightmode.png\" width=\"200\" alt=\"Skills Network Logo\"  />\n",
    "    </a>\n",
    "</p>\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",
    "You will extract the data from the below web site: <br> \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#this url contains the data you need to scrape\n",
    "url = \"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": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Your code here\n",
    "from bs4 import BeautifulSoup\n",
    "import requests\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import re"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Download the webpage at the url\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = requests.get(url).text"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a soup object\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "soup = BeautifulSoup(data, features='html')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Scrape the `Language name` and `annual average salary`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Language</th>\n",
       "      <th>Annual_Average_Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>Python</td>\n",
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       "      <td>Javascript</td>\n",
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       "    </tr>\n",
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       "      <th>4</th>\n",
       "      <td>Swift</td>\n",
       "      <td>130801</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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       "     Language  Annual_Average_Salary\n",
       "0      Python                 114383\n",
       "1        Java                 101013\n",
       "2           R                  92037\n",
       "3  Javascript                 110981\n",
       "4       Swift                 130801"
      ]
     },
     "execution_count": 37,
     "metadata": {},
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   "source": [
    "languages = []\n",
    "salaries = []\n",
    "\n",
    "table = soup.find('table')\n",
    "for row in table.find_all('tr')[1:]:\n",
    "    cols = row.find_all('td')\n",
    "    languages.append(cols[1].getText())\n",
    "    salaries.append(int(re.sub('[$,.]','', cols[3].getText())))\n",
    "\n",
    "df = pd.DataFrame({'Language':languages, 'Annual_Average_Salary':salaries})\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Save the scrapped data into a file named *popular-languages.csv*\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('popular-languages.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Language</th>\n",
       "      <th>Annual_Average_Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Swift</td>\n",
       "      <td>130801</td>\n",
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       "      <td>Python</td>\n",
       "      <td>114383</td>\n",
       "    </tr>\n",
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       "      <th>5</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>1</th>\n",
       "      <td>Java</td>\n",
       "      <td>101013</td>\n",
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       "      <td>Go</td>\n",
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       "      <td>R</td>\n",
       "      <td>92037</td>\n",
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       "      <td>C#</td>\n",
       "      <td>88726</td>\n",
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       "      <td>SQL</td>\n",
       "      <td>84793</td>\n",
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       "      <td>PHP</td>\n",
       "      <td>84727</td>\n",
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       "     Language  Annual_Average_Salary\n",
       "4       Swift                 130801\n",
       "0      Python                 114383\n",
       "5         C++                 113865\n",
       "3  Javascript                 110981\n",
       "1        Java                 101013\n",
       "9          Go                  94082\n",
       "2           R                  92037\n",
       "6          C#                  88726\n",
       "8         SQL                  84793\n",
       "7         PHP                  84727"
      ]
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     "execution_count": 40,
     "metadata": {},
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   "source": [
    "df.sort_values(['Annual_Average_Salary'], ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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 &copy; 2020 IBM Corporation. This notebook and its source code are released under the terms of the [MIT License](https://cognitiveclass.ai/mit-license/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDA0321ENSkillsNetwork928-2022-01-01).\n"
   ]
  }
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