data-collection-by-web-scraping.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Programming Languages Annual Salary Survey - Collected Data using Web Scraping\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# URL to extract from the data\n",
    "\n",
    "url = \"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBM-DA0321EN-SkillsNetwork/labs/datasets/Programming_Languages.html\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data needed to be scraped is:\n",
    "<b> the name of the programming language </b>, and the <b>average annual salary.</b>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: bs4 in c:\\python39\\lib\\site-packages (0.0.1)\n",
      "Requirement already satisfied: beautifulsoup4 in c:\\python39\\lib\\site-packages (from bs4) (4.11.1)\n",
      "Requirement already satisfied: soupsieve>1.2 in c:\\python39\\lib\\site-packages (from beautifulsoup4->bs4) (2.3.2.post1)\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import requests\n",
    "from bs4 import BeautifulSoup\n",
    "!pip3 install bs4\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = requests.get(url).text\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creating a soup object\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "soup = BeautifulSoup(data, \"html.parser\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Scraping the <b>Language name</b> and, <b>annual average salary</b>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "table = soup.find('table')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Language--->Average Annual Salary\n",
      "Python--->$114,383\n",
      "Java--->$101,013\n",
      "R--->$92,037\n",
      "Javascript--->$110,981\n",
      "Swift--->$130,801\n",
      "C++--->$113,865\n",
      "C#--->$88,726\n",
      "PHP--->$84,727\n",
      "SQL--->$84,793\n",
      "Go--->$94,082\n"
     ]
    }
   ],
   "source": [
    "language_list = []\n",
    "for row in table.find_all('tr'):\n",
    "    cols = row.find_all('td')\n",
    "    Language_name = cols[1].getText()\n",
    "    Annual_average_salary = cols[3].getText()\n",
    "    print(\"{}--->{}\".format(Language_name, Annual_average_salary))\n",
    "    language_list.append([Language_name, Annual_average_salary])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Saving the scarped data into a file named popular-languages.csv\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>0</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>1</td>\n",
       "      <td>Python</td>\n",
       "      <td>$114,383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Java</td>\n",
       "      <td>$101,013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>R</td>\n",
       "      <td>$92,037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Javascript</td>\n",
       "      <td>$110,981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Swift</td>\n",
       "      <td>$130,801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>C++</td>\n",
       "      <td>$113,865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>C#</td>\n",
       "      <td>$88,726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>PHP</td>\n",
       "      <td>$84,727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>SQL</td>\n",
       "      <td>$84,793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>Go</td>\n",
       "      <td>$94,082</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    0    Language Average Annual Salary\n",
       "0   1      Python              $114,383\n",
       "1   2        Java              $101,013\n",
       "2   3           R               $92,037\n",
       "3   4  Javascript              $110,981\n",
       "4   5       Swift              $130,801\n",
       "5   6         C++              $113,865\n",
       "6   7          C#               $88,726\n",
       "7   8         PHP               $84,727\n",
       "8   9         SQL               $84,793\n",
       "9  10          Go               $94,082"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_lang = pd.DataFrame(language_list, columns=[\n",
    "                       'Language', 'Average Annual Salary'])\n",
    "filename = \"popular-languages.csv\"\n",
    "df_lang.to_csv(filename)\n",
    "df = pd.read_csv(filename, header=1)\n",
    "df\n"
   ]
  },
  {
   "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>Average Annual Salary</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Language</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Swift</th>\n",
       "      <td>130801.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Python</th>\n",
       "      <td>114383.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C++</th>\n",
       "      <td>113865.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Javascript</th>\n",
       "      <td>110981.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Java</th>\n",
       "      <td>101013.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Go</th>\n",
       "      <td>94082.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R</th>\n",
       "      <td>92037.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C#</th>\n",
       "      <td>88726.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SQL</th>\n",
       "      <td>84793.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PHP</th>\n",
       "      <td>84727.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Average Annual Salary\n",
       "Language                         \n",
       "Swift                    130801.0\n",
       "Python                   114383.0\n",
       "C++                      113865.0\n",
       "Javascript               110981.0\n",
       "Java                     101013.0\n",
       "Go                        94082.0\n",
       "R                         92037.0\n",
       "C#                        88726.0\n",
       "SQL                       84793.0\n",
       "PHP                       84727.0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cleaning data\n",
    "df['Average Annual Salary'].replace('[\\$,]', '', regex=True, inplace=True)\n",
    "df['Average Annual Salary'] = df['Average Annual Salary'].astype(float)\n",
    "df.sort_values('Average Annual Salary', ascending=False, inplace=True)\n",
    "del df['0']\n",
    "df = df.set_index('Language')\n",
    "df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import matplotlib as mpl\n",
    "\n",
    "df.plot(kind='bar', label=True)\n",
    "plt.title(\"Popular Languages by Salary\")\n",
    "plt.xlabel(\"Language\")\n",
    "plt.ylabel(\"Average Annual Salary\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Observation </b> <br>\n",
    "\n",
    "For the top 10 rankings of the most popular languages arranged by salary. The highest paid programming language is Swift, which falls at $130,801. Swift is a programming language developed by Apple Inc. for its products among iOS, macOS, iPadOS, watchOS, and tvOS.\n",
    "In the recent years, we can see that there's a popular trend in companies developing their own apps, therefore we can see that they're willing to pay a higher wage for Swift proficient. Python,\n",
    "C++, and Javascript are also high up at around $110,000 annual salary.\n"
   ]
  }
 ],
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  "interpreter": {
   "hash": "81794d4967e6c3204c66dcd87b604927b115b27c00565d3d43f05ba2f3a2cb0d"
  },
  "kernelspec": {
   "display_name": "Python 3.9.7 64-bit",
   "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": "3.9.7"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}