Task01_Web_Scraping.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "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-SkillsNetworkCoursesIBMDeveloperSkillsNetworkPY0221ENSkillsNetwork899-2023-01-01\">\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": [
    "# Peer Review Assignment - Data Engineer - Webscraping\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Estimated time needed: **20** minutes\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Objectives\n",
    "\n",
    "In this part you will:\n",
    "\n",
    "- Use webscraping to get bank information\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this lab, we are going to be using Python and several Python libraries. Some of these libraries might be installed in your lab environment or in SN Labs. Others may need to be installed by you. The cells below will install these libraries when executed.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "                  __    __    __    __\n",
      "                 /  \\  /  \\  /  \\  /  \\\n",
      "                /    \\/    \\/    \\/    \\\n",
      "███████████████/  /██/  /██/  /██/  /████████████████████████\n",
      "              /  / \\   / \\   / \\   / \\  \\____\n",
      "             /  /   \\_/   \\_/   \\_/   \\    o \\__,\n",
      "            / _/                       \\_____/  `\n",
      "            |/\n",
      "        ███╗   ███╗ █████╗ ███╗   ███╗██████╗  █████╗\n",
      "        ████╗ ████║██╔══██╗████╗ ████║██╔══██╗██╔══██╗\n",
      "        ██╔████╔██║███████║██╔████╔██║██████╔╝███████║\n",
      "        ██║╚██╔╝██║██╔══██║██║╚██╔╝██║██╔══██╗██╔══██║\n",
      "        ██║ ╚═╝ ██║██║  ██║██║ ╚═╝ ██║██████╔╝██║  ██║\n",
      "        ╚═╝     ╚═╝╚═╝  ╚═╝╚═╝     ╚═╝╚═════╝ ╚═╝  ╚═╝\n",
      "\n",
      "        mamba (1.4.2) supported by @QuantStack\n",
      "\n",
      "        GitHub:  https://github.com/mamba-org/mamba\n",
      "        Twitter: https://twitter.com/QuantStack\n",
      "\n",
      "█████████████████████████████████████████████████████████████\n",
      "\n",
      "\n",
      "Looking for: ['bs4==4.10.0']\n",
      "\n",
      "\u001b[?25l\u001b[2K\u001b[0G[+] 0.0s\n",
      "\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 0.1s\n",
      "pkgs/main/linux-64 \u001b[90m━━━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━━\u001b[0m   0.0 B /  ??.?MB @  ??.?MB/s  0.1s\n",
      "pkgs/main/noarch   \u001b[90m━━━━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━\u001b[0m   0.0 B /  ??.?MB @  ??.?MB/s  0.1s\n",
      "pkgs/r/linux-64    \u001b[90m━━━━━━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━\u001b[0m   0.0 B /  ??.?MB @  ??.?MB/s  0.1s\n",
      "pkgs/r/noarch      \u001b[90m━━━━━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━\u001b[0m   0.0 B /  ??.?MB @  ??.?MB/s  0.1s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 0.2s\n",
      "pkgs/main/linux-64 \u001b[90m━━━━━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━\u001b[0m  28.7kB /  ??.?MB @ 187.3kB/s  0.2s\n",
      "pkgs/main/noarch   \u001b[90m━━━━━━━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━\u001b[0m  57.4kB /  ??.?MB @ 371.7kB/s  0.2s\n",
      "pkgs/r/linux-64    \u001b[90m━━━━━━━━━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━\u001b[0m  12.3kB /  ??.?MB @  79.4kB/s  0.2s\n",
      "pkgs/r/noarch      \u001b[90m━━━━━━━━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━\u001b[0m   0.0 B /  ??.?MB @  ??.?MB/s  0.2s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 0.3s\n",
      "pkgs/main/linux-64 \u001b[90m━━━━━━━━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━\u001b[0m 557.1kB /  ??.?MB @   2.2MB/s  0.3s\n",
      "pkgs/main/noarch   \u001b[90m━━━━━━━━━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━\u001b[0m 598.0kB /  ??.?MB @   2.3MB/s  0.3s\n",
      "pkgs/r/linux-64    \u001b[33m━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━\u001b[0m 565.3kB /  ??.?MB @   2.2MB/s  0.3s\n",
      "pkgs/r/noarch      \u001b[33m━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━\u001b[0m 548.9kB /  ??.?MB @   2.1MB/s  0.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gpkgs/main/noarch                                   852.3kB @   2.8MB/s  0.3s\n",
      "[+] 0.4s\n",
      "pkgs/main/linux-64 \u001b[33m━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━\u001b[0m   1.1MB @   3.1MB/s             0.4s\n",
      "pkgs/r/linux-64    \u001b[33m━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m   1.2MB @   3.2MB/s             0.4s\n",
      "pkgs/r/noarch      ━━━━━━━━━━━━━━━━━━━━━━━━   1.3MB @   3.3MB/s Finalizing  0.4s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gpkgs/r/noarch                                      @   3.3MB/s  0.4s\n",
      "pkgs/r/linux-64                                    @   3.3MB/s  0.5s\n",
      "[+] 0.5s\n",
      "pkgs/main/linux-64 \u001b[33m━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m   1.8MB /  ??.?MB @   3.6MB/s  0.5s\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 0.6s\n",
      "pkgs/main/linux-64 \u001b[33m━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m   2.3MB /  ??.?MB @   4.0MB/s  0.6s\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 0.7s\n",
      "pkgs/main/linux-64 \u001b[33m━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m   2.9MB /  ??.?MB @   4.3MB/s  0.7s\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 0.8s\n",
      "pkgs/main/linux-64 \u001b[90m━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━\u001b[0m   3.5MB /  ??.?MB @   4.4MB/s  0.8s\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 0.9s\n",
      "pkgs/main/linux-64 \u001b[90m━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━\u001b[0m   4.1MB /  ??.?MB @   4.6MB/s  0.9s\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 1.0s\n",
      "pkgs/main/linux-64 \u001b[90m━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━\u001b[0m   4.7MB /  ??.?MB @   4.7MB/s  1.0s\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 1.1s\n",
      "pkgs/main/linux-64 \u001b[90m━━━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━━\u001b[0m   5.3MB /  ??.?MB @   4.8MB/s  1.1s\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 1.2s\n",
      "pkgs/main/linux-64 \u001b[90m━━━━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━\u001b[0m   5.6MB /  ??.?MB @   4.8MB/s  1.2s\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 1.3s\n",
      "pkgs/main/linux-64 ━━━━━━━━━━━━━━━━━━━━━━━━   6.0MB @   4.9MB/s Finalizing  1.3s\u001b[2K\u001b[1A\u001b[2K\u001b[0Gpkgs/main/linux-64                                 @   4.9MB/s  1.3s\n",
      "\u001b[?25h\n",
      "Pinned packages:\n",
      "  - python 3.7.*\n",
      "\n",
      "\n",
      "Transaction\n",
      "\n",
      "  Prefix: /home/jupyterlab/conda/envs/python\n",
      "\n",
      "  Updating specs:\n",
      "\n",
      "   - bs4==4.10.0\n",
      "   - ca-certificates\n",
      "   - certifi\n",
      "   - openssl\n",
      "\n",
      "\n",
      "  Package               Version  Build         Channel                 Size\n",
      "─────────────────────────────────────────────────────────────────────────────\n",
      "  Install:\n",
      "─────────────────────────────────────────────────────────────────────────────\n",
      "\n",
      "  \u001b[32m+ bs4            \u001b[0m      4.10.0  hd3eb1b0_0    pkgs/main/noarch        10kB\n",
      "\n",
      "  Upgrade:\n",
      "─────────────────────────────────────────────────────────────────────────────\n",
      "\n",
      "  \u001b[31m- ca-certificates\u001b[0m    2023.5.7  hbcca054_0    conda-forge                 \n",
      "  \u001b[32m+ ca-certificates\u001b[0m  2023.08.22  h06a4308_0    pkgs/main/linux-64     125kB\n",
      "  \u001b[31m- openssl        \u001b[0m      1.1.1t  h0b41bf4_0    conda-forge                 \n",
      "  \u001b[32m+ openssl        \u001b[0m      1.1.1w  h7f8727e_0    pkgs/main/linux-64       4MB\n",
      "\n",
      "  Downgrade:\n",
      "─────────────────────────────────────────────────────────────────────────────\n",
      "\n",
      "  \u001b[31m- beautifulsoup4 \u001b[0m      4.11.1  pyha770c72_0  conda-forge                 \n",
      "  \u001b[32m+ beautifulsoup4 \u001b[0m      4.10.0  pyh06a4308_0  pkgs/main/noarch        87kB\n",
      "\n",
      "  Summary:\n",
      "\n",
      "  Install: 1 packages\n",
      "  Upgrade: 2 packages\n",
      "  Downgrade: 1 packages\n",
      "\n",
      "  Total download: 4MB\n",
      "\n",
      "─────────────────────────────────────────────────────────────────────────────\n",
      "\n",
      "\n",
      "\u001b[?25l\u001b[2K\u001b[0G[+] 0.0s\n",
      "Downloading      \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m   0.0 B                            0.0s\n",
      "Extracting       \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m       0                            0.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 0.1s\n",
      "Downloading  (4) \u001b[33m━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m   0.0 B beautifulsoup4             0.0s\n",
      "Extracting       \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m       0                            0.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gca-certificates                                    125.5kB @ 912.1kB/s  0.1s\n",
      "beautifulsoup4                                      86.6kB @ 623.0kB/s  0.1s\n",
      "bs4                                                 10.2kB @  66.7kB/s  0.2s\n",
      "openssl                                              3.9MB @  22.0MB/s  0.2s\n",
      "[+] 0.2s\n",
      "Downloading      ━━━━━━━━━━━━━━━━━━━━━━━   4.1MB                            0.1s\n",
      "Extracting   (4) \u001b[90m━━━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━\u001b[0m       0 beautifulsoup4             0.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 0.3s\n",
      "Downloading      ━━━━━━━━━━━━━━━━━━━━━━━   4.1MB                            0.1s\n",
      "Extracting   (4) \u001b[90m━━━━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━\u001b[0m       0 beautifulsoup4             0.1s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 0.4s\n",
      "Downloading      ━━━━━━━━━━━━━━━━━━━━━━━   4.1MB                            0.1s\n",
      "Extracting   (4) \u001b[90m━━━━━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━\u001b[0m       0 beautifulsoup4             0.2s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 0.5s\n",
      "Downloading      ━━━━━━━━━━━━━━━━━━━━━━━   4.1MB                            0.1s\n",
      "Extracting   (4) \u001b[90m━━━━━━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━\u001b[0m       0 beautifulsoup4             0.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 0.6s\n",
      "Downloading      ━━━━━━━━━━━━━━━━━━━━━━━   4.1MB                            0.1s\n",
      "Extracting   (4) \u001b[90m━━━━━━━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━\u001b[0m       0 bs4                        0.4s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 0.7s\n",
      "Downloading      ━━━━━━━━━━━━━━━━━━━━━━━   4.1MB                            0.1s\n",
      "Extracting   (4) \u001b[33m━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m       0 bs4                        0.5s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 0.8s\n",
      "Downloading      ━━━━━━━━━━━━━━━━━━━━━━━   4.1MB                            0.1s\n",
      "Extracting   (4) \u001b[33m━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m       0 bs4                        0.6s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 0.9s\n",
      "Downloading      ━━━━━━━━━━━━━━━━━━━━━━━   4.1MB                            0.1s\n",
      "Extracting   (4) \u001b[33m━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m       0 bs4                        0.7s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 1.0s\n",
      "Downloading      ━━━━━━━━━━━━━━━━━━━━━━━   4.1MB                            0.1s\n",
      "Extracting   (4) \u001b[33m━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m       0 ca-certificates            0.8s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 1.1s\n",
      "Downloading      ━━━━━━━━━━━━━━━━━━━━━━━   4.1MB                            0.1s\n",
      "Extracting   (4) \u001b[33m━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m       0 ca-certificates            0.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 1.2s\n",
      "Downloading      ━━━━━━━━━━━━━━━━━━━━━━━   4.1MB                            0.1s\n",
      "Extracting   (4) \u001b[33m━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m       0 ca-certificates            1.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 1.3s\n",
      "Downloading      ━━━━━━━━━━━━━━━━━━━━━━━   4.1MB                            0.1s\n",
      "Extracting   (4) \u001b[33m━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m       0 ca-certificates            1.1s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 1.4s\n",
      "Downloading      ━━━━━━━━━━━━━━━━━━━━━━━   4.1MB                            0.1s\n",
      "Extracting   (4) \u001b[33m━━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━\u001b[0m       0 openssl                    1.2s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 1.5s\n",
      "Downloading      ━━━━━━━━━━━━━━━━━━━━━━━   4.1MB                            0.1s\n",
      "Extracting   (4) \u001b[90m╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━\u001b[0m       0 openssl                    1.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 1.6s\n",
      "Downloading      ━━━━━━━━━━━━━━━━━━━━━━━   4.1MB                            0.1s\n",
      "Extracting   (4) \u001b[90m━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━\u001b[0m       0 openssl                    1.4s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 1.7s\n",
      "Downloading      ━━━━━━━━━━━━━━━━━━━━━━━   4.1MB                            0.1s\n",
      "Extracting   (4) \u001b[90m━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━\u001b[0m       0 openssl                    1.5s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 1.8s\n",
      "Downloading      ━━━━━━━━━━━━━━━━━━━━━━━   4.1MB                            0.1s\n",
      "Extracting   (4) \u001b[90m━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━\u001b[0m       0 beautifulsoup4             1.6s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 1.9s\n",
      "Downloading      ━━━━━━━━━━━━━━━━━━━━━━━   4.1MB                            0.1s\n",
      "Extracting   (4) \u001b[90m━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━\u001b[0m       0 beautifulsoup4             1.7s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 2.0s\n",
      "Downloading      ━━━━━━━━━━━━━━━━━━━━━━━   4.1MB                            0.1s\n",
      "Extracting   (3) ━━━━╸\u001b[33m━━━━━━━━━━━━━━━━━━\u001b[0m       1 beautifulsoup4             1.8s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 2.1s\n",
      "Downloading      ━━━━━━━━━━━━━━━━━━━━━━━   4.1MB                            0.1s\n",
      "Extracting   (2) ━━━━━━━━━━╸\u001b[33m━━━━━━━━━━━━\u001b[0m       2 bs4                        1.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 2.2s\n",
      "Downloading      ━━━━━━━━━━━━━━━━━━━━━━━   4.1MB                            0.1s\n",
      "Extracting   (1) ━━━━━━━━━━━━━━━━╸\u001b[33m━━━━━━\u001b[0m       3 openssl                    2.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G\u001b[?25h\n",
      "Downloading and Extracting Packages\n",
      "\n",
      "Preparing transaction: done\n",
      "Verifying transaction: done\n",
      "Executing transaction: done\n",
      "\n",
      "                  __    __    __    __\n",
      "                 /  \\  /  \\  /  \\  /  \\\n",
      "                /    \\/    \\/    \\/    \\\n",
      "███████████████/  /██/  /██/  /██/  /████████████████████████\n",
      "              /  / \\   / \\   / \\   / \\  \\____\n",
      "             /  /   \\_/   \\_/   \\_/   \\    o \\__,\n",
      "            / _/                       \\_____/  `\n",
      "            |/\n",
      "        ███╗   ███╗ █████╗ ███╗   ███╗██████╗  █████╗\n",
      "        ████╗ ████║██╔══██╗████╗ ████║██╔══██╗██╔══██╗\n",
      "        ██╔████╔██║███████║██╔████╔██║██████╔╝███████║\n",
      "        ██║╚██╔╝██║██╔══██║██║╚██╔╝██║██╔══██╗██╔══██║\n",
      "        ██║ ╚═╝ ██║██║  ██║██║ ╚═╝ ██║██████╔╝██║  ██║\n",
      "        ╚═╝     ╚═╝╚═╝  ╚═╝╚═╝     ╚═╝╚═════╝ ╚═╝  ╚═╝\n",
      "\n",
      "        mamba (1.4.2) supported by @QuantStack\n",
      "\n",
      "        GitHub:  https://github.com/mamba-org/mamba\n",
      "        Twitter: https://twitter.com/QuantStack\n",
      "\n",
      "█████████████████████████████████████████████████████████████\n",
      "\n",
      "\n",
      "Looking for: ['html5lib==1.1']\n",
      "\n",
      "pkgs/main/linux-64                                          Using cache\n",
      "pkgs/main/noarch                                            Using cache\n",
      "pkgs/r/linux-64                                             Using cache\n",
      "pkgs/r/noarch                                               Using cache\n",
      "\n",
      "Pinned packages:\n",
      "  - python 3.7.*\n",
      "\n",
      "\n",
      "Transaction\n",
      "\n",
      "  Prefix: /home/jupyterlab/conda/envs/python\n",
      "\n",
      "  Updating specs:\n",
      "\n",
      "   - html5lib==1.1\n",
      "   - ca-certificates\n",
      "   - certifi\n",
      "   - openssl\n",
      "\n",
      "\n",
      "  Package         Version  Build         Channel                Size\n",
      "──────────────────────────────────────────────────────────────────────\n",
      "  Install:\n",
      "──────────────────────────────────────────────────────────────────────\n",
      "\n",
      "  \u001b[32m+ html5lib    \u001b[0m      1.1  pyhd3eb1b0_0  pkgs/main/noarch       93kB\n",
      "  \u001b[32m+ webencodings\u001b[0m    0.5.1  py37_1        pkgs/main/linux-64     20kB\n",
      "\n",
      "  Summary:\n",
      "\n",
      "  Install: 2 packages\n",
      "\n",
      "  Total download: 113kB\n",
      "\n",
      "──────────────────────────────────────────────────────────────────────\n",
      "\n",
      "\n",
      "\u001b[?25l\u001b[2K\u001b[0G[+] 0.0s\n",
      "Downloading      \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m   0.0 B                            0.0s\n",
      "Extracting       \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m       0                            0.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 0.1s\n",
      "Downloading  (2) \u001b[33m━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m   0.0 B html5lib                   0.0s\n",
      "Extracting       \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m       0                            0.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gwebencodings                                        19.6kB @ 148.9kB/s  0.1s\n",
      "html5lib                                            93.0kB @ 670.2kB/s  0.1s\n",
      "[+] 0.2s\n",
      "Downloading      ━━━━━━━━━━━━━━━━━━━━━━━ 112.6kB                            0.1s\n",
      "Extracting   (2) \u001b[90m━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━\u001b[0m       0 html5lib                   0.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 0.3s\n",
      "Downloading      ━━━━━━━━━━━━━━━━━━━━━━━ 112.6kB                            0.1s\n",
      "Extracting   (2) \u001b[90m━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━━\u001b[0m       0 html5lib                   0.1s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 0.4s\n",
      "Downloading      ━━━━━━━━━━━━━━━━━━━━━━━ 112.6kB                            0.1s\n",
      "Extracting   (2) \u001b[90m━━━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━\u001b[0m       0 html5lib                   0.2s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 0.5s\n",
      "Downloading      ━━━━━━━━━━━━━━━━━━━━━━━ 112.6kB                            0.1s\n",
      "Extracting   (2) \u001b[90m━━━━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━\u001b[0m       0 html5lib                   0.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 0.6s\n",
      "Downloading      ━━━━━━━━━━━━━━━━━━━━━━━ 112.6kB                            0.1s\n",
      "Extracting   (2) \u001b[90m━━━━━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━\u001b[0m       0 webencodings               0.4s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 0.7s\n",
      "Downloading      ━━━━━━━━━━━━━━━━━━━━━━━ 112.6kB                            0.1s\n",
      "Extracting   (2) \u001b[90m━━━━━━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━\u001b[0m       0 webencodings               0.5s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 0.8s\n",
      "Downloading      ━━━━━━━━━━━━━━━━━━━━━━━ 112.6kB                            0.1s\n",
      "Extracting   (2) \u001b[90m━━━━━━━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━\u001b[0m       0 webencodings               0.6s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 0.9s\n",
      "Downloading      ━━━━━━━━━━━━━━━━━━━━━━━ 112.6kB                            0.1s\n",
      "Extracting   (2) \u001b[33m━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m       0 webencodings               0.7s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 1.0s\n",
      "Downloading      ━━━━━━━━━━━━━━━━━━━━━━━ 112.6kB                            0.1s\n",
      "Extracting   (2) \u001b[33m━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━\u001b[0m       0 html5lib                   0.8s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 1.1s\n",
      "Downloading      ━━━━━━━━━━━━━━━━━━━━━━━ 112.6kB                            0.1s\n",
      "Extracting   (1) ━━━━━━━━━━╸\u001b[33m━━━━━━━━━━━━\u001b[0m       1 html5lib                   0.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G\u001b[?25h\n",
      "Downloading and Extracting Packages\n",
      "\n",
      "Preparing transaction: done\n",
      "Verifying transaction: done\n",
      "Executing transaction: done\n"
     ]
    }
   ],
   "source": [
    "#!mamba install pandas==1.3.3 -y\n",
    "#!mamba install requests==2.26.0 -y\n",
    "!mamba install bs4==4.10.0 -y\n",
    "!mamba install html5lib==1.1 -y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports\n",
    "\n",
    "Import any additional libraries you may need here.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from bs4 import BeautifulSoup\n",
    "import html5lib\n",
    "import requests\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extract Data Using Web Scraping\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The wikipedia webpage https://web.archive.org/web/20200318083015/https://en.wikipedia.org/wiki/List_of_largest_banks provides information about largest banks in the world by various parameters. Scrape the data from the table 'By market capitalization' and store it in a JSON file.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Webpage Contents\n",
    "\n",
    "Gather the contents of the webpage in text format using the `requests` library and assign it to the variable <code>html_data</code>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Write your code here\n",
    "import requests\n",
    "\n",
    "url = \"https://web.archive.org/web/20200318083015/https://en.wikipedia.org/wiki/List_of_largest_banks\"\n",
    "html_data = requests.get(url).text\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Question 1</b> Print out the output of the following line, and remember it as it will be a quiz question:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'tic/css/banner-styles.c'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "html_data[760:783]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scraping the Data\n",
    "\n",
    "<b> Question 2</b> Using the contents and `beautiful soup` load the data from the `By market capitalization` table into a `pandas` dataframe. The dataframe should have the bank `Name` and `Market Cap (US$ Billion)` as column names.  Display the first five rows using head. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using BeautifulSoup parse the contents of the webpage.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Replace the dots below\n",
    "soup = BeautifulSoup(html_data, 'html5lib')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the data from the `By market capitalization` table into a pandas dataframe. The dataframe should have the bank `Name` and `Market Cap (US$ Billion)` as column names. Using the empty dataframe `data` and the given loop extract the necessary data from each row and append it to the empty dataframe.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.DataFrame(columns=[\"Name\", \"Market Cap (US$ Billion)\"])\n",
    "\n",
    "for row in soup.find_all('tbody')[2].find_all('tr'):\n",
    "    col = row.find_all('td')\n",
    "    #Write your code here\n",
    "    if len(col) >= 3:\n",
    "        rank = col[0].text.strip()\n",
    "        bank_name = col[1].text.strip()\n",
    "        market_cap = col[2].text.strip()\n",
    "        data = data.append({\"Rank\": rank, \"Bank name\": bank_name, \"Market Cap (US$ billion)\": market_cap}, ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Question 3** Display the first five rows using the `head` function.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Name Market Cap (US$ Billion)                                Bank name  \\\n",
      "0  NaN                      NaN                           JPMorgan Chase   \n",
      "1  NaN                      NaN  Industrial and Commercial Bank of China   \n",
      "2  NaN                      NaN                          Bank of America   \n",
      "3  NaN                      NaN                              Wells Fargo   \n",
      "4  NaN                      NaN                  China Construction Bank   \n",
      "\n",
      "  Market Cap (US$ billion) Rank  \n",
      "0                  390.934    1  \n",
      "1                  345.214    2  \n",
      "2                  325.331    3  \n",
      "3                  308.013    4  \n",
      "4                  257.399    5  \n"
     ]
    }
   ],
   "source": [
    "#Write your code here\n",
    "print(data.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### Loading the Data\n",
    "\n",
    "Load the `pandas` dataframe created above into a JSON named `bank_market_cap.json` using the `to_json()` function.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Write your code here\n",
    "data.to_json(\"bank_market_cap.json\", orient=\"records\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Authors\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ramesh Sannareddy, Joseph Santarcangelo and Azim Hirjani\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",
    "| 2022-07-12        | 0.2     | Appalabhaktula Hema | Corrected the code and markdown |\n",
    "| 2020-11-25        | 0.1     | Ramesh Sannareddy | Created initial version of the lab |\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Copyright © 2020 IBM Corporation.\n"
   ]
  }
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