CIA-Factbook-Analytics2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## This notebook scrapes the CIA World Factbook website and downloads the text data for any country of your choosing (or all of them)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import re\n",
    "import urllib.request, urllib.parse, urllib.error\n",
    "from bs4 import BeautifulSoup\n",
    "import ssl"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ignore SSL certificate errors, send HHTP request, and read the HTML"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Opening the file connection...\n",
      "HTTP status 200\n",
      "Reading done. Total 75036 characters read.\n"
     ]
    }
   ],
   "source": [
    "ctx = ssl.create_default_context()\n",
    "ctx.check_hostname = False\n",
    "ctx.verify_mode = ssl.CERT_NONE\n",
    "\n",
    "# Read the HTML from the URL and pass on to BeautifulSoup\n",
    "url = 'https://www.cia.gov/library/publications/the-world-factbook/'\n",
    "print(\"Opening the file connection...\")\n",
    "uh= urllib.request.urlopen(url, context=ctx)\n",
    "print(\"HTTP status\",uh.getcode())\n",
    "html =uh.read().decode()\n",
    "print(f\"Reading done. Total {len(html)} characters read.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Parse the front-page HTML using BeautifulSoup and print it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<!DOCTYPE html>\n",
      "<!--[if lt IE 7]> <html class=\"no-js lt-ie9 lt-ie8 lt-ie7\" lang=\"en\"> <![endif]-->\n",
      "<!--[if IE 7]>    <html class=\"no-js lt-ie9 lt-ie8\" lang=\"en\"> <![endif]-->\n",
      "<!--[if IE 8]>    <html class=\"no-js lt-ie9\" lang=\"en\"> <![endif]-->\n",
      "<!--[if gt IE 8]><!-->\n",
      "<html class=\"no-js\" lang=\"en\" xml:lang=\"en\" xmlns=\"http://www.w3.org/1999/xhtml\">\n",
      " <!--<![endif]-->\n",
      " <head>\n",
      "  <meta content=\"text/html; charset=utf-8\" http-equiv=\"Content-Type\"/>\n",
      "  <link href=\"css/publications.css\" rel=\"stylesheet\" type=\"text/css\"/>\n",
      "  <link href=\"css/publications-detail.css\" rel=\"stylesheet\" type=\"text/css\"/>\n",
      "  <meta charset=\"utf-8\"/>\n",
      "  <meta content=\"IE=edge,chrome=1\" http-equiv=\"X-UA-Compatible\"/>\n",
      "  <title>\n",
      "   The World Factbook — Central Intelligence Agency\n",
      "  </title>\n",
      "  <meta content=\"\" name=\"description\"/>\n",
      "  <meta content=\"width=device-width\" name=\"viewport\"/>\n",
      "  <meta content=\"Apr 01, 2016\" name=\"LastModified\"/>\n",
      "  <link href=\"css/jobcart.css\" rel=\"stylesheet\"/>\n",
      "  <link href=\"css/smallscreen.css\" rel=\"stylesheet\"/>\n",
      "  <link href=\"css/modules.css\" rel=\"stylesheet\"/>\n",
      "  <!--[if lt IE 9]><link href=\"css/fullscreen.css\" rel=\"stylesheet\" type=\"text/css\"><![endif]-->\n",
      "  <link href=\"css/fullscreen.css\" media=\"only screen and (min-width: 993px)\" rel=\"stylesheet\" type=\"text/css\"/>\n",
      "  <script src=\"js/modernizr.custom.73407.js\">\n",
      "  </script>\n",
      "  <script src=\"js/jquery-1.8.3.min.js\" type=\"text/javascript\">\n",
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      "  </script>\n",
      "  <script src=\"js/rwd.js\" type=\"text/javascript\">\n",
      "  </script>\n",
      "  <script src=\"js/jquery.main.js\" type=\"text/javascript\">\n",
      "  </script>\n",
      "  <script src=\"js/connect.js\" type=\"text/javascript\">\n",
      "  </script>\n",
      "  <!-- css files for popup -->\n",
      "  <script>\n",
      "   var isIElt9 = false;\n",
      "  </script>\n",
      "  <link href=\"css/bootstrap_customized.css\" rel=\"stylesheet\"/>\n",
      "  <link href=\"css/expandcollapse.css\" media=\"screen\" rel=\"stylesheet\" type=\"text/css\"/>\n",
      "  <!--[if lt IE 9]>\r\n",
      "    <script async src=\"js/respond.min.js\"></script>\r\n",
      "    <script async src=\"js/html5shiv.min.js\"></script>\r\n",
      "    <script>var isIElt9 = true;</script>\r\n",
      "<![endif]-->\n",
      "  <!--   jquery code added for popup -->\n",
      "  <script async=\"\" src=\"js/bootstrap_customized.min.js\">\n",
      "  </script>\n",
      "  <!-- our public key -->\n",
      "  <script async=\"\" src=\"/js2/pubkey.js\">\n",
      "  </script>\n",
      "  <!-- image preloading -->\n",
      "  <script>\n",
      "   prePic = new Image();\n",
      "  prePic.src =\"images/separator-2.gif\";\n",
      "  prePic2 = new Image();\n",
      "  prePic2.src=\"images/arrow-5.gif\";\n",
      "  prePic3 = new Image();\n",
      "  prePic3.src=\"images/bg-heading-panel.jpg\";\n",
      "  prePic4 = new Image();\n",
      "  prePic4.src=\"images/heading-banners/CHI12052_Headers_contact.jpg\";\n",
      "  </script>\n",
      "  <script src=\"js/jquery.ba-throttle-debounce.min.js\" type=\"text/javascript\">\n",
      "  </script>\n",
      "  <script src=\"js/expandcollapse.js\" type=\"text/javascript\">\n",
      "  </script>\n",
      " </head>\n",
      " <body class=\"template-document_view_publications_detail portaltype-agencypage site-CIAgov section-library icons-on\">\n",
      "  <div>\n",
      "   <noscript>\n",
      "    Javascript must be enabled for the correct page display\n",
      "   </noscript>\n",
      "   <div id=\"wrapper\">\n",
      "    <!-- Modal for Landing Page-->\n",
      "    <div aria-hidden=\"true\" aria-labelledby=\"myModalLabel\" class=\"modal fade\" id=\"popupLandingModal\" role=\"dialog\" style=\"z-index:2147483647;overflow:auto\" tabindex=\"-1\">\n",
      "     <div class=\"modal-dialog\">\n",
      "      <div class=\"modal-content\">\n",
      "       <div class=\"modal-body\">\n",
      "        <div class=\"section-contact-cia main-block-popup\">\n",
      "         <button aria-hidden=\"true\" aria-label=\"Close\" class=\"cfclose\" data-dismiss=\"modal\" type=\"button\">\n",
      "          ×\n",
      "         </button>\n",
      "         <br/>\n",
      "         <section id=\"mainLanding\">\n",
      "          <div class=\"section-contact-cia heading-panel\" style=\"width:93%\">\n",
      "           <!--  <h1>Contact CIA</h1> -->\n",
      "          </div>\n",
      "          <div class=\"main-holder threecol\" style=\"width:100%\">\n",
      "           <div>\n",
      "            <ul class=\"breadcrumbs\">\n",
      "             <li>\n",
      "              <a href=\"/\">\n",
      "               Home\n",
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      "              <a href=\"/library\">\n",
      "               Library\n",
      "              </a>\n",
      "             </li>\n",
      "             <li>\n",
      "              <a href=\"/library/publications\">\n",
      "               Publications\n",
      "              </a>\n",
      "             </li>\n",
      "             <li>\n",
      "              The World Factbook\n",
      "             </li>\n",
      "            </ul>\n",
      "            <article class=\"description-box\">\n",
      "             <a id=\"main-content\" tabindex=\"-1\">\n",
      "             </a>\n",
      "             <div>\n",
      "              <div class=\"\">\n",
      "               <div id=\"content\">\n",
      "                <h1>\n",
      "                 Contact CIA\n",
      "                </h1>\n",
      "                <div>\n",
      "                 <div class=\"\" id=\"parent-fieldname-text-55a5766e45f049b42fce0fdbf970b9ae\">\n",
      "                  <p>\n",
      "                   The Office of Public Affairs (OPA) is the single point of contact for all inquiries about the Central Intelligence Agency (CIA).\n",
      "                  </p>\n",
      "                  <p>\n",
      "                   We read every letter, fax, or e-mail we receive, and we will convey your comments to CIA officials outside OPA as appropriate. However, with limited staff and resources, we simply cannot respond to all who write to us.\n",
      "                  </p>\n",
      "                  <hr class=\"centerline\" styple=\"margin-top:2px;margin-bottom:2px;\"/>\n",
      "                  <h3>\n",
      "                   Contact Information\n",
      "                  </h3>\n",
      "                  <p>\n",
      "                   <a class=\"contactTriggerFromLanding btn btn-info btn-block\" href=\"#\" role=\"button\">\n",
      "                    Submit questions or comments online\n",
      "                   </a>\n",
      "                  </p>\n",
      "                  <p>\n",
      "                   <b>\n",
      "                    By postal mail:\n",
      "                   </b>\n",
      "                   <br/>\n",
      "                   Central Intelligence Agency\n",
      "                   <br/>\n",
      "                   Office of Public Affairs\n",
      "                   <br/>\n",
      "                   Washington, D.C. 20505\n",
      "                  </p>\n",
      "                  <p>\n",
      "                   <b>\n",
      "                    By phone:\n",
      "                    <br/>\n",
      "                   </b>\n",
      "                   (703) 482-0623\n",
      "                   <br/>\n",
      "                   Open during normal business hours.\n",
      "                  </p>\n",
      "                  <p>\n",
      "                   <b>\n",
      "                    By fax:\n",
      "                    <br/>\n",
      "                   </b>\n",
      "                   (571) 204-3800\n",
      "                   <i>\n",
      "                    <br/>\n",
      "                    (please include a phone number where we may call you)\n",
      "                   </i>\n",
      "                  </p>\n",
      "                  <p>\n",
      "                   <br/>\n",
      "                   Contact the\n",
      "                   <a class=\"internal-link\" href=\"/offices-of-cia/inspector-general/index.html\">\n",
      "                    Office of Inspector General\n",
      "                   </a>\n",
      "                  </p>\n",
      "                  <p>\n",
      "                   Contact the\n",
      "                   <a class=\"internal-link\" href=\"/contact-cia/employment-verification-office.html\">\n",
      "                    Employment Verification Office\n",
      "                   </a>\n",
      "                  </p>\n",
      "                  <p>\n",
      "                  </p>\n",
      "                  <hr class=\"centerline\" styple=\"margin-top:2px;margin-bottom:2px;\"/>\n",
      "                  <p>\n",
      "                   <b>\n",
      "                    Before contacting us:\n",
      "                   </b>\n",
      "                  </p>\n",
      "                  <ul>\n",
      "                   <li>\n",
      "                    Please check our\n",
      "                    <a href=\"/sitemap.html\" title=\"Site Map\">\n",
      "                     <b>\n",
      "                      site map\n",
      "                     </b>\n",
      "                    </a>\n",
      "                    ,\n",
      "                    <b>\n",
      "                     search\n",
      "                    </b>\n",
      "                    feature, or\n",
      "                    <b>\n",
      "                     our site navigation on the left\n",
      "                    </b>\n",
      "                    to locate the information you seek. We do not routinely respond to questions for which answers are found within this Web site.\n",
      "                    <br/>\n",
      "                    <br/>\n",
      "                   </li>\n",
      "                   <li>\n",
      "                    <a href=\"/careers/index.html\" title=\"Careers\">\n",
      "                     <b>\n",
      "                      Employment\n",
      "                     </b>\n",
      "                    </a>\n",
      "                    <b>\n",
      "                     :\n",
      "                    </b>\n",
      "                    We do not routinely answer questions about employment beyond the information on this Web site, and we do not routinely answer inquiries about the status of job applications. Recruiting will contact applicants within 45 days if their qualifications meet our needs.\n",
      "                    <br/>\n",
      "                    <br/>\n",
      "                    Because of safety concerns for the prospective applicant, as well as security and communication issues, the CIA Recruitment Center does not accept resumes, nor can we return phone calls, e-mails or other forms of communication, from US citizens living outside of the US. When you return permanently to the US (not on vacation or leave), please visit the\n",
      "                    <a href=\"/careers/opportunities/index.html\" title=\"Careers at CIA\">\n",
      "                     CIA Careers page\n",
      "                    </a>\n",
      "                    and apply online for the position of interest.\n",
      "                    <br/>\n",
      "                    <br/>\n",
      "                    To verify an employee's employment, please contact the\n",
      "                    <a class=\"internal-link\" href=\"/contact-cia/employment-verification-office.html\">\n",
      "                     Employment Verification Office\n",
      "                    </a>\n",
      "                    .\n",
      "                    <br/>\n",
      "                    <br/>\n",
      "                   </li>\n",
      "                   <li>\n",
      "                    <b>\n",
      "                     Solicitations to transfer large sums of money to your bank account:\n",
      "                    </b>\n",
      "                    If you receive a solicitation to transfer a large amount of money from an African nation to your bank account in exchange for a payment of millions of dollars, go to the US Secret Service Web site for information about the Nigerian Advance Fee Fraud or \"4-1-9\" Fraud scheme.\n",
      "                    <br/>\n",
      "                    <br/>\n",
      "                   </li>\n",
      "                   <li>\n",
      "                    If you have information which you believe might be of interest to the CIA in pursuit of the CIA's foreign intelligence mission, you may use our\n",
      "                    <a class=\"contactTriggerFromLanding\" href=\"#\">\n",
      "                     <b>\n",
      "                      e-mail form\n",
      "                     </b>\n",
      "                    </a>\n",
      "                    . We will carefully protect all information you provide, including your identity. The CIA, as a foreign intelligence agency, does not engage in US domestic law enforcement.\n",
      "                    <br/>\n",
      "                    <br/>\n",
      "                   </li>\n",
      "                   <li>\n",
      "                    If you have information relating to Iraq which you believe might be of interest to the US Government, please contact us through the\n",
      "                    <a href=\"/about-cia/iraqi-rewards-program.html\" title=\"Iraqi Rewards Program\">\n",
      "                     <b>\n",
      "                      Iraqi Rewards Program\n",
      "                     </b>\n",
      "                    </a>\n",
      "                    —\n",
      "                    <a href=\"/about-cia/iraqi-rewards-program.html\" title=\"Iraqi Rewards Program\">\n",
      "                     <img alt=\"برنامج مكافآت العراق\" class=\"image-inline\" src=\"/contact-cia/secondary_page_link.gif/image.gif\" title=\"secondary_page_link.gif\"/>\n",
      "                    </a>\n",
      "                   </li>\n",
      "                  </ul>\n",
      "                 </div>\n",
      "                </div>\n",
      "               </div>\n",
      "              </div>\n",
      "             </div>\n",
      "            </article>\n",
      "           </div>\n",
      "          </div>\n",
      "         </section>\n",
      "        </div>\n",
      "       </div>\n",
      "      </div>\n",
      "     </div>\n",
      "    </div>\n",
      "    <!-- Modal for Contact Page-->\n",
      "    <div aria-hidden=\"true\" aria-labelledby=\"myModalLabel\" class=\"modal fade\" id=\"popupContactModal\" role=\"dialog\" style=\"z-index:2147483647;overflow:auto\" tabindex=\"-1\">\n",
      "     <div class=\"modal-dialog\">\n",
      "      <div class=\"modal-content\">\n",
      "       <div class=\"modal-body\">\n",
      "        <div class=\"section-contact-cia main-block-popup\">\n",
      "         <button aria-hidden=\"true\" aria-label=\"Close\" class=\"cfclose\" data-dismiss=\"modal\" type=\"button\">\n",
      "          ×\n",
      "         </button>\n",
      "         <br/>\n",
      "         <div id=\"mainContact\">\n",
      "          <div class=\"section-contact-cia heading-panel\" style=\"width:93%\">\n",
      "           <h1>\n",
      "            Library\n",
      "           </h1>\n",
      "          </div>\n",
      "          <div class=\"main-holder threecol\" style=\"width:100%\">\n",
      "           <div id=\"contentContact\">\n",
      "            <ul class=\"breadcrumbs\">\n",
      "             <li>\n",
      "              <a href=\"/\">\n",
      "               Home\n",
      "              </a>\n",
      "             </li>\n",
      "             <li>\n",
      "              <a href=\"/library\">\n",
      "               Library\n",
      "              </a>\n",
      "             </li>\n",
      "             <li>\n",
      "              <a href=\"/library/publications\">\n",
      "               Publications\n",
      "              </a>\n",
      "             </li>\n",
      "             <li>\n",
      "              The World Factbook\n",
      "             </li>\n",
      "            </ul>\n",
      "            <div class=\"description-box\">\n",
      "             <a id=\"main-content\" tabindex=\"-1\">\n",
      "             </a>\n",
      "             <div class=\"text-holderPopup\" style=\"width:100%\">\n",
      "              <div id=\"viewlet-above-content\">\n",
      "              </div>\n",
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      "               <div id=\"contentContact\">\n",
      "                <h1>\n",
      "                 <div id=\"popupBanner\" style=\"margin-left:5%;margin-right:5%;color:#5f1d1d;font-size:0.6em\">\n",
      "                  Contact Us Form\n",
      "                 </div>\n",
      "                </h1>\n",
      "                <div>\n",
      "                 <div class=\"\" id=\"parent-fieldname-text-b7a1aa432b8946fc8bc268300efe896d\">\n",
      "                  <div id=\"popupBodyFull\">\n",
      "                  </div>\n",
      "                  <!-- popup form here -->\n",
      "                  <div id=\"popupBody\">\n",
      "                   <meta content=\"telephone=no\" name=\"format-detection\"/>\n",
      "                   <form accept-charset=\"utf-8\" class=\"form-horizontal\" id=\"cf\" name=\"cf\" role=\"form\">\n",
      "                    <div class=\"form-group\" style=\"margin-left:6%;width:95%;margin-bottom:7px;\">\n",
      "                     <label for=\"message\" id=\"mlabel\" type=\"label\">\n",
      "                      Message:\n",
      "                      <span style=\"color:red\">\n",
      "                       *\n",
      "                      </span>\n",
      "                     </label>\n",
      "                     <textarea autocapitalize=\"off\" autocomplete=\"off\" autocorrect=\"off\" class=\"form-control\" cols=\"70\" id=\"message\" name=\"message\" placeholder=\"Message Text\" required=\"true\" rows=\"10\" style=\"resize:none;width:90%\"></textarea>\n",
      "                     <div id=\"messageErr\">\n",
      "                     </div>\n",
      "                    </div>\n",
      "                    <div class=\"form-group\" style=\"margin-left:6%;width:95%;margin-bottom:7px;\">\n",
      "                     <label for=\"email\" id=\"elabel\">\n",
      "                      Email:\n",
      "                      <span style=\"color:red\">\n",
      "                       *\n",
      "                      </span>\n",
      "                     </label>\n",
      "                     <input autocapitalize=\"off\" autocomplete=\"off\" autocorrect=\"off\" class=\"form-control\" id=\"email\" maxlength=\"40\" name=\"email\" pattern=\"[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\\.[a-zA-Z0-9-]+)*$\" placeholder=\"user@email.com\" required=\"\" size=\"40\" style=\"width:90%\" type=\"email\"/>\n",
      "                    </div>\n",
      "                    <div class=\"form-group\" style=\"margin-left:6%;width:95%;margin-bottom:7px;\">\n",
      "                     <label for=\"name\">\n",
      "                      Name:\n",
      "                     </label>\n",
      "                     <input autocapitalize=\"off\" autocomplete=\"off\" autocorrect=\"off\" class=\"form-control\" id=\"name\" maxlength=\"40\" name=\"sender\" placeholder=\"Firstname Lastname\" size=\"40\" style=\"width:90%\" type=\"text\"/>\n",
      "                    </div>\n",
      "                    <div class=\"form-inline\" style=\"margin-left:6%;margin-bottom:7px;\">\n",
      "                     <label>\n",
      "                      Phone Number:\n",
      "                     </label>\n",
      "                     <br/>\n",
      "                     <input autocomplete=\"off\" class=\"form-control\" id=\"phone_cc\" maxlength=\"5\" name=\"ph_cc\" placeholder=\"Country Code\" size=\"12\" style=\"max-width:55%\" type=\"cc\"/>\n",
      "                     <input autocomplete=\"off\" class=\"form-control\" id=\"phone\" maxlength=\"20\" name=\"phone_num\" placeholder=\"Number\" size=\"40\" style=\"max-width:90%\" type=\"phone\"/>\n",
      "                    </div>\n",
      "                    <div class=\"form-inline\" style=\"margin-left:6%;margin-bottom:7px;\">\n",
      "                     <label>\n",
      "                      Mobile/Cell:\n",
      "                     </label>\n",
      "                     <br/>\n",
      "                     <input autocomplete=\"off\" class=\"form-control\" id=\"cellcc\" maxlength=\"5\" name=\"mobilecc\" placeholder=\"Country Code\" size=\"12\" style=\"max-width:55%\" type=\"cc\"/>\n",
      "                     <input autocomplete=\"off\" class=\"form-control\" id=\"cellnumber\" maxlength=\"20\" name=\"mobilenum\" placeholder=\"Number\" size=\"40\" style=\"max-width:90%\" type=\"phone\"/>\n",
      "                    </div>\n",
      "                    <br/>\n",
      "                    <p align=\"center\">\n",
      "                    </p>\n",
      "                    <div id=\"refId\" style=\"margin-left:5%\">\n",
      "                    </div>\n",
      "                    <br/>\n",
      "                    <span id=\"submitFormButton\">\n",
      "                     <input class=\"btn-lg btn-block btn-primary center-block\" onclick=\"javascript:confirm_submission();\" type=\"button\" value=\"Send\"/>\n",
      "                    </span>\n",
      "                    <br/>\n",
      "                    <input class=\"btn-lg btn-block center-block\" name=\"Reset\" type=\"reset\" value=\"Reset\"/>\n",
      "                    <p align=\"left\">\n",
      "                     <span style=\"color:red;margin-left:5%\">\n",
      "                      *\n",
      "                     </span>\n",
      "                     = required\n",
      "                    </p>\n",
      "                   </form>\n",
      "                  </div>\n",
      "                 </div>\n",
      "                </div>\n",
      "               </div>\n",
      "              </div>\n",
      "             </div>\n",
      "            </div>\n",
      "           </div>\n",
      "          </div>\n",
      "         </div>\n",
      "        </div>\n",
      "       </div>\n",
      "      </div>\n",
      "     </div>\n",
      "    </div>\n",
      "    <!-- Modal for Report Page-->\n",
      "    <div aria-hidden=\"true\" aria-labelledby=\"myModalLabel\" class=\"modal fade\" id=\"popupReportModal\" role=\"dialog\" style=\"z-index:2147483647;overflow:auto\" tabindex=\"-1\">\n",
      "     <div class=\"modal-dialog\">\n",
      "      <div class=\"modal-content\">\n",
      "       <div class=\"modal-body\">\n",
      "        <div class=\"section-contact-cia main-block-popup\">\n",
      "         <button aria-hidden=\"true\" aria-label=\"Close\" class=\"cfclose\" data-dismiss=\"modal\" type=\"button\">\n",
      "          ×\n",
      "         </button>\n",
      "         <br/>\n",
      "         <section id=\"mainReport\">\n",
      "          <div class=\"section-contact-cia heading-panel\" style=\"width:93%\">\n",
      "           <h1>\n",
      "            Library\n",
      "           </h1>\n",
      "          </div>\n",
      "          <div class=\"main-holder threecol\" style=\"width:100%\">\n",
      "           <div>\n",
      "            <ul class=\"breadcrumbs\">\n",
      "             <li>\n",
      "              <a href=\"/\">\n",
      "               Home\n",
      "              </a>\n",
      "             </li>\n",
      "             <li>\n",
      "              <a href=\"/library\">\n",
      "               Library\n",
      "              </a>\n",
      "             </li>\n",
      "             <li>\n",
      "              <a href=\"/library/publications\">\n",
      "               Publications\n",
      "              </a>\n",
      "             </li>\n",
      "             <li>\n",
      "              The World Factbook\n",
      "             </li>\n",
      "            </ul>\n",
      "            <article class=\"description-box\">\n",
      "             <a id=\"main-content\" tabindex=\"-1\">\n",
      "             </a>\n",
      "             <div>\n",
      "              <div class=\"\">\n",
      "               <div>\n",
      "                <h1>\n",
      "                 Report Threats\n",
      "                </h1>\n",
      "                <div>\n",
      "                 <div class=\"\" id=\"parent-fieldname-text-84043b8c4f22633413a10d8a8009e18c\">\n",
      "                  <p>\n",
      "                   The United States and its partners continue to face a \n",
      "growing number of global threats and challenges. The CIA’s mission \n",
      "includes collecting and analyzing information about high priority \n",
      "national security issues such as international terrorism, the \n",
      "proliferation of weapons of mass destruction, cyber attacks, \n",
      "international organized crime and narcotics trafficking, regional \n",
      "conflicts, counterintelligence threats, and the effects of environmental\n",
      " and natural disasters.\n",
      "                  </p>\n",
      "                  <p>\n",
      "                   These challenges are international in scope and are priorities for \n",
      "the Central Intelligence Agency. If you have information about these or \n",
      "other national security challenges, please provide it through our secure\n",
      " online form.  The information you provide will be protected and \n",
      "confidential. The CIA is particularly interested in information about \n",
      "imminent or planned terrorist attacks.  In cases where an imminent \n",
      "threat exists, immediately contact your local law enforcement agencies \n",
      "and provide them with the threat information.\n",
      "                  </p>\n",
      "                  <p align=\"center\">\n",
      "                   <a class=\"contactTriggerFromReport\" href=\"#\">\n",
      "                    To contact the Central Intelligence Agency click here.\n",
      "                   </a>\n",
      "                  </p>\n",
      "                 </div>\n",
      "                </div>\n",
      "               </div>\n",
      "              </div>\n",
      "             </div>\n",
      "            </article>\n",
      "           </div>\n",
      "          </div>\n",
      "         </section>\n",
      "        </div>\n",
      "       </div>\n",
      "       <div>\n",
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      "      </div>\n",
      "     </div>\n",
      "    </div>\n",
      "    <header id=\"header\">\n",
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      "      <a accesskey=\"S\" class=\"skip\" href=\"#main-content\">\n",
      "       skip to content\n",
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      "         <a href=\"/\">\n",
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      "          <span>\n",
      "           Central Intelligence Agency\n",
      "          </span>\n",
      "         </a>\n",
      "        </h1>\n",
      "        <h2 class=\"work-text\">\n",
      "         The Work Of A Nation. The Center of Intelligence.\n",
      "        </h2>\n",
      "       </hgroup>\n",
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      "             Report Threats\n",
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      "            <a href=\"/about-cia/iraqi-rewards-program.html\" title=\"Iraqi Rewards Program\">\n",
      "             رعربيعربي\n",
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      "            <a class=\"landingTrigger\" href=\"/contact-cia\" title=\"A single point of contact for all CIA inquiries.\">\n",
      "             Contact\n",
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      "        Navigation\n",
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      "         <a class=\"hasChildrens\" href=\"/about-cia\" title=\"About CIA Description\">\n",
      "          <span>\n",
      "           About CIA\n",
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      "         <div class=\"drop\">\n",
      "          <ul class=\"globalSectionsLevel1 show-static\">\n",
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      "             <span>\n",
      "              Today's CIA\n",
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      "            <a class=\"\" href=\"/about-cia/leadership\" title=\"\">\n",
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      "              Leadership\n",
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      "              CIA Vision, Mission, Ethos &amp; Challenges\n",
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      "              Headquarters Tour\n",
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      "             <span>\n",
      "              CIA Museum\n",
      "             </span>\n",
      "            </a>\n",
      "           </li>\n",
      "           <li class=\"plain \">\n",
      "            <a class=\"\" href=\"/about-cia/history-of-the-cia\" title=\"\">\n",
      "             <span>\n",
      "              History of the CIA\n",
      "             </span>\n",
      "            </a>\n",
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      "            <a class=\"\" href=\"/about-cia/publications-review-board\" title=\"Publications Review Board\">\n",
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      "              Publications Review Board\n",
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      "              Accessibility\n",
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      "              NoFEAR Act\n",
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      "            <a href=\"/about-cia/\">\n",
      "             About CIA\n",
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      "           </h4>\n",
      "           <p>\n",
      "            Discover the CIA\n",
      "            <a href=\"/about-cia/\">\n",
      "             history, mission, vision and values\n",
      "            </a>\n",
      "            .\n",
      "           </p>\n",
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      "          <div class=\"visualClear\">\n",
      "           <!-- -->\n",
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      "         <a class=\"hasChildrens\" href=\"/careers\" title=\"\">\n",
      "          <span>\n",
      "           Careers &amp; Internships\n",
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      "           <li class=\"plain \">\n",
      "            <a class=\"\" href=\"/careers/opportunities\" title=\"This is an overview of all career opportunities at the CIA. \">\n",
      "             <span>\n",
      "              Career Opportunities\n",
      "             </span>\n",
      "            </a>\n",
      "           </li>\n",
      "           <li class=\"plain \">\n",
      "            <a class=\"\" href=\"/careers/student-opportunities\" title=\"This is the student profile page for candidates looking for jobs/ job listings at the CIA. Student Opportunities - Student Profiles\">\n",
      "             <span>\n",
      "              Student Opportunities\n",
      "             </span>\n",
      "            </a>\n",
      "           </li>\n",
      "           <li class=\"plain \">\n",
      "            <a class=\"\" href=\"/careers/application-process\" title=\"How to apply to the CIA.\">\n",
      "             <span>\n",
      "              Application Process\n",
      "             </span>\n",
      "            </a>\n",
      "           </li>\n",
      "           <li class=\"plain \">\n",
      "            <a class=\"\" href=\"/careers/life-at-cia\" title=\"This is the about CIA section of the Careers Site\">\n",
      "             <span>\n",
      "              Life at CIA\n",
      "             </span>\n",
      "            </a>\n",
      "           </li>\n",
      "           <li class=\"plain \">\n",
      "            <a class=\"\" href=\"/careers/benefits.html\" title=\"Every career at the CIA is a rewarding one. In addition to serving your country with the certainty that your work makes a difference, the CIA offers a comprehensive benefits package to reflect the dedication and contributions of our employees.\">\n",
      "             <span>\n",
      "              Benefits\n",
      "             </span>\n",
      "            </a>\n",
      "           </li>\n",
      "           <li class=\"plain \">\n",
      "            <a class=\"\" href=\"/careers/diversity\" title=\"This is the diversity information for the Careers Site\">\n",
      "             <span>\n",
      "              Diversity\n",
      "             </span>\n",
      "            </a>\n",
      "           </li>\n",
      "           <li class=\"plain \">\n",
      "            <a class=\"\" href=\"/careers/military-transition\" title=\"Your prior military service could qualify you to continue to serve your nation at the Central Intelligence Agency. Opportunities for qualified applicants are available in the U.S. and abroad.\">\n",
      "             <span>\n",
      "              Military Transition\n",
      "             </span>\n",
      "            </a>\n",
      "           </li>\n",
      "           <li class=\"plain \">\n",
      "            <a class=\"\" href=\"/careers/games-information\" title=\"\">\n",
      "             <span>\n",
      "              Tools and Challenges\n",
      "             </span>\n",
      "            </a>\n",
      "           </li>\n",
      "           <li class=\"plain \">\n",
      "            <a class=\"\" href=\"/careers/faq\" title=\"Frequently Asked Questions/ FAQ for a Career at the CIA in the Careers Section\">\n",
      "             <span>\n",
      "              FAQs\n",
      "             </span>\n",
      "            </a>\n",
      "           </li>\n",
      "           <li class=\"plain \">\n",
      "            <a class=\"\" href=\"/careers/video-center\" title=\"Repository of CIA videos\">\n",
      "             <span>\n",
      "              Video Center\n",
      "             </span>\n",
      "            </a>\n",
      "           </li>\n",
      "          </ul>\n",
      "          <div class=\"static\">\n",
      "           <img alt=\"Careers Menu\" class=\"image-inline\" src=\"/images/section-banners/careers-menu/image.jpg\" title=\"Careers Menu\"/>\n",
      "           <h4>\n",
      "            <a href=\"/careers/\">\n",
      "             Careers &amp; Internships\n",
      "            </a>\n",
      "           </h4>\n",
      "           <p>\n",
      "            Your talent. Your diverse skills. Our mission. Learn more about\n",
      "            <a href=\"/careers/\">\n",
      "             Career Opportunities at CIA\n",
      "            </a>\n",
      "            .\n",
      "           </p>\n",
      "          </div>\n",
      "          <div class=\"visualClear\">\n",
      "           <!-- -->\n",
      "          </div>\n",
      "         </div>\n",
      "        </li>\n",
      "        <li class=\"\">\n",
      "         <a class=\"hasChildrens\" href=\"/offices-of-cia\" title=\"\">\n",
      "          <span>\n",
      "           Offices of CIA\n",
      "          </span>\n",
      "         </a>\n",
      "         <div class=\"drop\">\n",
      "          <ul class=\"globalSectionsLevel1 show-static\">\n",
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      "            <a class=\"\" href=\"/offices-of-cia/intelligence-analysis\" title=\"\">\n",
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      "              Intelligence &amp; Analysis\n",
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      "             <span>\n",
      "              Clandestine Service\n",
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      "           </li>\n",
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      "            <a class=\"\" href=\"/offices-of-cia/science-technology\" title=\"\">\n",
      "             <span>\n",
      "              Science &amp; Technology\n",
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      "             <span>\n",
      "              Support to Mission\n",
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      "           </li>\n",
      "           <li class=\"plain \">\n",
      "            <a class=\"\" href=\"/offices-of-cia/digital-innovation\" title=\"\">\n",
      "             <span>\n",
      "              Digital Innovation\n",
      "             </span>\n",
      "            </a>\n",
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      "           <li class=\"plain \">\n",
      "            <a class=\"\" href=\"/offices-of-cia/mission-centers\" title=\"\">\n",
      "             <span>\n",
      "              Mission Centers\n",
      "             </span>\n",
      "            </a>\n",
      "           </li>\n",
      "           <li class=\"plain \">\n",
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      "             <span>\n",
      "              Human Resources\n",
      "             </span>\n",
      "            </a>\n",
      "           </li>\n",
      "           <li class=\"plain \">\n",
      "            <a class=\"\" href=\"/offices-of-cia/public-affairs\" title=\"Public Affairs\">\n",
      "             <span>\n",
      "              Public Affairs\n",
      "             </span>\n",
      "            </a>\n",
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      "           <li class=\"plain \">\n",
      "            <a class=\"\" href=\"/offices-of-cia/general-counsel\" title=\"\">\n",
      "             <span>\n",
      "              General Counsel\n",
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      "            </a>\n",
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      "             <span>\n",
      "              Equal Employment Opportunity\n",
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      "           <li class=\"plain \">\n",
      "            <a class=\"\" href=\"/offices-of-cia/congressional-affairs\" title=\"Office of Congressional Affairs\">\n",
      "             <span>\n",
      "              Congressional Affairs\n",
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      "            </a>\n",
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      "           <li class=\"plain \">\n",
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      "             <span>\n",
      "              Inspector General\n",
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      "           </li>\n",
      "           <li class=\"plain \">\n",
      "            <a class=\"\" href=\"/offices-of-cia/military-affairs\" title=\"Military Affairs\">\n",
      "             <span>\n",
      "              Military Affairs\n",
      "             </span>\n",
      "            </a>\n",
      "           </li>\n",
      "          </ul>\n",
      "          <div class=\"static\">\n",
      "           <img alt=\"Offices Menu\" class=\"image-inline\" src=\"/images/section-banners/offices-menu/image.jpg\" title=\"Offices Menu\"/>\n",
      "           <h4>\n",
      "            <a href=\"/offices-of-cia/\">\n",
      "             Offices of CIA\n",
      "            </a>\n",
      "           </h4>\n",
      "           <p>\n",
      "            <a href=\"/offices-of-cia/\">\n",
      "             Learn how the CIA is organized\n",
      "            </a>\n",
      "            into directorates and key offices, responsible for securing our nation.\n",
      "           </p>\n",
      "          </div>\n",
      "          <div class=\"visualClear\">\n",
      "           <!-- -->\n",
      "          </div>\n",
      "         </div>\n",
      "        </li>\n",
      "        <li class=\"\">\n",
      "         <a class=\"hasChildrens\" href=\"/news-information\" title=\"News &amp; Information Description\">\n",
      "          <span>\n",
      "           News &amp; Information\n",
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      "         </a>\n",
      "         <div class=\"drop\">\n",
      "          <ul class=\"globalSectionsLevel1 show-static\">\n",
      "           <li class=\"plain \">\n",
      "            <a class=\"\" href=\"/news-information/blog\" title=\"\">\n",
      "             <span>\n",
      "              Blog\n",
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      "            </a>\n",
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      "           <li class=\"plain \">\n",
      "            <a class=\"\" href=\"/news-information/press-releases-statements\" title=\"\">\n",
      "             <span>\n",
      "              Press Releases &amp; Statements\n",
      "             </span>\n",
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      "            <a class=\"\" href=\"/news-information/speeches-testimony\" title=\"\">\n",
      "             <span>\n",
      "              Speeches &amp; Testimony\n",
      "             </span>\n",
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      "           <li class=\"plain \">\n",
      "            <a class=\"\" href=\"/news-information/cia-the-war-on-terrorism\" title=\"\">\n",
      "             <span>\n",
      "              CIA &amp; the War on Terrorism\n",
      "             </span>\n",
      "            </a>\n",
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      "           <li class=\"plain \">\n",
      "            <a class=\"\" href=\"/news-information/featured-story-archive\" title=\"index for featured story\">\n",
      "             <span>\n",
      "              Featured Story Archive\n",
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      "           <li class=\"plain \">\n",
      "            <a class=\"\" href=\"/news-information/your-news\" title=\"\">\n",
      "             <span>\n",
      "              Your News\n",
      "             </span>\n",
      "            </a>\n",
      "           </li>\n",
      "          </ul>\n",
      "          <div class=\"static\">\n",
      "           <img alt=\"News Menu\" class=\"image-inline\" src=\"/images/section-banners/news-menu/image.jpg\" title=\"News Menu\"/>\n",
      "           <h4>\n",
      "            <a href=\"/news-information/\">\n",
      "             News &amp; Information\n",
      "            </a>\n",
      "           </h4>\n",
      "           <p>\n",
      "            The most up-to-date CIA\n",
      "            <a href=\"/news-information/\">\n",
      "             news, press releases, information and more\n",
      "            </a>\n",
      "            .\n",
      "           </p>\n",
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      "          <span>\n",
      "           Library\n",
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      "            <a class=\"\" href=\"/library/publications\" title=\"\">\n",
      "             <span>\n",
      "              Publications\n",
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      "             <span>\n",
      "              Center for the Study of Intelligence\n",
      "             </span>\n",
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      "            <a class=\"\" href=\"/library/foia\" title=\"\">\n",
      "             <span>\n",
      "              Freedom of Information Act Electronic Reading Room\n",
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      "            <a class=\"\" href=\"/library/kent-center-occasional-papers\" title=\"\">\n",
      "             <span>\n",
      "              Kent Center Occasional Papers\n",
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      "            </a>\n",
      "           </li>\n",
      "           <li class=\"plain \">\n",
      "            <a class=\"\" href=\"/library/intelligence-literature\" title=\"\">\n",
      "             <span>\n",
      "              Intelligence Literature\n",
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      "             <span>\n",
      "              Reports\n",
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      "            <a class=\"\" href=\"/library/video-center\" title=\"Repository of CIA videos\">\n",
      "             <span>\n",
      "              Video Center\n",
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      "            </a>\n",
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      "          </ul>\n",
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      "             Library\n",
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      "            Our\n",
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      "             open-source library\n",
      "            </a>\n",
      "            houses the thousands of documents, periodicals, maps and reports released to the public.\n",
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      "           Kids' Zone\n",
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      "             <span>\n",
      "              K-5th Grade\n",
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      "             <span>\n",
      "              6-12th Grade\n",
      "             </span>\n",
      "            </a>\n",
      "           </li>\n",
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      "            <a class=\"\" href=\"/kids-page/parents-teachers\" title=\"\">\n",
      "             <span>\n",
      "              Parents &amp; Teachers\n",
      "             </span>\n",
      "            </a>\n",
      "           </li>\n",
      "           <li class=\"plain \">\n",
      "            <a class=\"\" href=\"/kids-page/games\" title=\"\">\n",
      "             <span>\n",
      "              Games\n",
      "             </span>\n",
      "            </a>\n",
      "           </li>\n",
      "           <li class=\"plain \">\n",
      "            <a class=\"\" href=\"/kids-page/related-links\" title=\"\">\n",
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      "            <a href=\"/kids-page/\">\n",
      "             Kids' Zone\n",
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      "           <p>\n",
      "            <a href=\"/kids-page/\">\n",
      "             Learn more about the Agency\n",
      "            </a>\n",
      "            and find some top secret things you won't see anywhere else.\n",
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      "    </header>\n",
      "    <div class=\"main-block\">\n",
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      "       <h1>\n",
      "        Library\n",
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      "      <div class=\"main-holder threecol\">\n",
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      "         <h2 class=\"visuallyhidden\">\n",
      "          Secondary Navigation\n",
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      "          <li class=\"navTreeItem navTreeTopNode\">\n",
      "           <a class=\"contenttype-agencyfolder\" href=\"/library\" title=\"\">\n",
      "            Library\n",
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      "           <a class=\"state-published navTreeItemInPath navTreeFolderish contenttype-agencyfolder\" href=\"/library/publications\" title=\"\">\n",
      "            Publications\n",
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      "          <li class=\"navTreeItem visualNoMarker navTreeFolderish section-center-for-the-study-of-intelligence\">\n",
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      "            Center for the Study of Intelligence\n",
      "           </a>\n",
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      "          <li class=\"navTreeItem visualNoMarker navTreeFolderish section-foia\">\n",
      "           <a class=\"state-published navTreeFolderish contenttype-agencyfolder\" href=\"/library/foia\" title=\"\">\n",
      "            Freedom of Information Act Electronic Reading Room\n",
      "           </a>\n",
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      "          <li class=\"navTreeItem visualNoMarker navTreeFolderish section-kent-center-occasional-papers\">\n",
      "           <a class=\"state-published navTreeFolderish contenttype-agencyfolder\" href=\"/library/kent-center-occasional-papers\" title=\"\">\n",
      "            Kent Center Occasional Papers\n",
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      "          </li>\n",
      "          <li class=\"navTreeItem visualNoMarker navTreeFolderish section-intelligence-literature\">\n",
      "           <a class=\"state-published navTreeFolderish contenttype-agencyfolder\" href=\"/library/intelligence-literature\" title=\"\">\n",
      "            Intelligence Literature\n",
      "           </a>\n",
      "          </li>\n",
      "          <li class=\"navTreeItem visualNoMarker navTreeFolderish section-reports\">\n",
      "           <a class=\"state-published navTreeFolderish contenttype-agencyfolder\" href=\"/library/reports\" title=\"Reports\">\n",
      "            Reports\n",
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      "            Video Center\n",
      "           </a>\n",
      "          </li>\n",
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      "        </nav>\n",
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      "        </div>\n",
      "       </div>\n",
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      "        <ul class=\"breadcrumbs\">\n",
      "         <li>\n",
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      "           Home\n",
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      "         <li>\n",
      "          <a href=\"/library\">\n",
      "           Library\n",
      "          </a>\n",
      "         </li>\n",
      "         <li>\n",
      "          <a href=\"/library/publications\">\n",
      "           Publications\n",
      "          </a>\n",
      "         </li>\n",
      "         <li>\n",
      "          The World Factbook\n",
      "         </li>\n",
      "        </ul>\n",
      "        <article class=\"description-box\">\n",
      "         <a id=\"main-content\" tabindex=\"-1\">\n",
      "         </a>\n",
      "         <div class=\"text-holder-full\">\n",
      "          <div id=\"content\">\n",
      "           <div id=\"content-core\">\n",
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      "             <!-- dfl                   <div class=\"text-box\">   -->\n",
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      "             <div id=\"fbHeader\">\n",
      "              <div id=\"fbLogo\">\n",
      "               <img src=\"./images/banner_ext2.png\" title=\"World Factbook Title\"/>\n",
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      "               <form action=\"#\" method=\"GET\">\n",
      "                <select class=\"selecter_links\" name=\"selecter_links\" onchange=\"document.location.href= this.value;\">\n",
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      "                 <option value=\"geos/xq.html\">\n",
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      "                 <option value=\"geos/ar.html\">\n",
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      "                 <option value=\"geos/zh.html\">\n",
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      "                 <option value=\"geos/bf.html\">\n",
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      "                 <option value=\"geos/fi.html\">\n",
      "                  Finland\n",
      "                 </option>\n",
      "                 <option value=\"geos/fr.html\">\n",
      "                  France\n",
      "                 </option>\n",
      "                 <option value=\"geos/fp.html\">\n",
      "                  French Polynesia\n",
      "                 </option>\n",
      "                 <option value=\"geos/fs.html\">\n",
      "                  French Southern and Antarctic Lands\n",
      "                 </option>\n",
      "                 <option value=\"geos/gb.html\">\n",
      "                  Gabon\n",
      "                 </option>\n",
      "                 <option value=\"geos/ga.html\">\n",
      "                  Gambia, The\n",
      "                 </option>\n",
      "                 <option value=\"geos/gz.html\">\n",
      "                  Gaza Strip\n",
      "                 </option>\n",
      "                 <option value=\"geos/gg.html\">\n",
      "                  Georgia\n",
      "                 </option>\n",
      "                 <option value=\"geos/gm.html\">\n",
      "                  Germany\n",
      "                 </option>\n",
      "                 <option value=\"geos/gh.html\">\n",
      "                  Ghana\n",
      "                 </option>\n",
      "                 <option value=\"geos/gi.html\">\n",
      "                  Gibraltar\n",
      "                 </option>\n",
      "                 <option value=\"geos/gr.html\">\n",
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      "                 </option>\n",
      "                 <option value=\"geos/gl.html\">\n",
      "                  Greenland\n",
      "                 </option>\n",
      "                 <option value=\"geos/gj.html\">\n",
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      "                 </option>\n",
      "                 <option value=\"geos/gq.html\">\n",
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      "                 </option>\n",
      "                 <option value=\"geos/gt.html\">\n",
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      "                 </option>\n",
      "                 <option value=\"geos/gk.html\">\n",
      "                  Guernsey\n",
      "                 </option>\n",
      "                 <option value=\"geos/gv.html\">\n",
      "                  Guinea\n",
      "                 </option>\n",
      "                 <option value=\"geos/pu.html\">\n",
      "                  Guinea-Bissau\n",
      "                 </option>\n",
      "                 <option value=\"geos/gy.html\">\n",
      "                  Guyana\n",
      "                 </option>\n",
      "                 <option value=\"geos/ha.html\">\n",
      "                  Haiti\n",
      "                 </option>\n",
      "                 <option value=\"geos/hm.html\">\n",
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      "                 <option value=\"geos/vt.html\">\n",
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      "                 <option value=\"geos/ho.html\">\n",
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      "                 <option value=\"geos/hk.html\">\n",
      "                  Hong Kong\n",
      "                 </option>\n",
      "                 <option value=\"geos/um.html\">\n",
      "                  Howland Island\n",
      "                 </option>\n",
      "                 <option value=\"geos/hu.html\">\n",
      "                  Hungary\n",
      "                 </option>\n",
      "                 <option value=\"geos/ic.html\">\n",
      "                  Iceland\n",
      "                 </option>\n",
      "                 <option value=\"geos/in.html\">\n",
      "                  India\n",
      "                 </option>\n",
      "                 <option value=\"geos/xo.html\">\n",
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      "                 </option>\n",
      "                 <option value=\"geos/id.html\">\n",
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      "                 </option>\n",
      "                 <option value=\"geos/ir.html\">\n",
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      "                 </option>\n",
      "                 <option value=\"geos/iz.html\">\n",
      "                  Iraq\n",
      "                 </option>\n",
      "                 <option value=\"geos/ei.html\">\n",
      "                  Ireland\n",
      "                 </option>\n",
      "                 <option value=\"geos/im.html\">\n",
      "                  Isle of Man\n",
      "                 </option>\n",
      "                 <option value=\"geos/is.html\">\n",
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      "                 <option value=\"geos/it.html\">\n",
      "                  Italy\n",
      "                 </option>\n",
      "                 <option value=\"geos/jm.html\">\n",
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      "                 <option value=\"geos/jn.html\">\n",
      "                  Jan Mayen\n",
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      "                 <option value=\"geos/ja.html\">\n",
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      "                 <option value=\"geos/um.html\">\n",
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      "                 <option value=\"geos/kv.html\">\n",
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      "                 <option value=\"geos/lu.html\">\n",
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      "                  Madagascar\n",
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      "                 <option value=\"geos/my.html\">\n",
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      "                 <option value=\"geos/ml.html\">\n",
      "                  Mali\n",
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      "                 <option value=\"geos/mt.html\">\n",
      "                  Malta\n",
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      "                 <option value=\"geos/rm.html\">\n",
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      "                 </option>\n",
      "                 <option value=\"geos/mr.html\">\n",
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      "                 <option value=\"geos/mp.html\">\n",
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      "                 <option value=\"geos/mx.html\">\n",
      "                  Mexico\n",
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      "                 <option value=\"geos/fm.html\">\n",
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      "                 <option value=\"geos/um.html\">\n",
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      "                 <option value=\"geos/md.html\">\n",
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      "                  Mongolia\n",
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      "                 <option value=\"geos/mj.html\">\n",
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      "                 <option value=\"geos/no.html\">\n",
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      "                 <option value=\"geos/um.html\">\n",
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      "                 <option value=\"geos/pm.html\">\n",
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      "                 </option>\n",
      "                 <option value=\"geos/pp.html\">\n",
      "                  Papua New Guinea\n",
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      "                 <option value=\"geos/pf.html\">\n",
      "                  Paracel Islands\n",
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      "                 <option value=\"geos/pa.html\">\n",
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      "                 <option value=\"geos/pe.html\">\n",
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      "                 <option value=\"geos/rp.html\">\n",
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      "                 </option>\n",
      "                 <option value=\"geos/pc.html\">\n",
      "                  Pitcairn Islands\n",
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      "                 <option value=\"geos/pl.html\">\n",
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      "                 <option value=\"geos/po.html\">\n",
      "                  Portugal\n",
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      "                 <option value=\"geos/rq.html\">\n",
      "                  Puerto Rico\n",
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      "                 <option value=\"geos/qa.html\">\n",
      "                  Qatar\n",
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      "                 <option value=\"geos/ro.html\">\n",
      "                  Romania\n",
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      "                 <option value=\"geos/rs.html\">\n",
      "                  Russia\n",
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      "                 <option value=\"geos/rw.html\">\n",
      "                  Rwanda\n",
      "                 </option>\n",
      "                 <option value=\"geos/tb.html\">\n",
      "                  Saint Barthelemy\n",
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      "                 <option value=\"geos/sh.html\">\n",
      "                  Saint Helena, Ascension, and Tristan da Cunha\n",
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      "                 <option value=\"geos/st.html\">\n",
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      "                 <option value=\"geos/ws.html\">\n",
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      "                 <option value=\"geos/sn.html\">\n",
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      "                 <option value=\"geos/sk.html\">\n",
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      "                 <option value=\"geos/lo.html\">\n",
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      "                 <option value=\"geos/od.html\">\n",
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      "                 <option value=\"geos/sp.html\">\n",
      "                  Spain\n",
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      "                 <option value=\"geos/ce.html\">\n",
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      "                 <option value=\"geos/su.html\">\n",
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      "                 <option value=\"geos/ns.html\">\n",
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      "                 <option value=\"geos/sv.html\">\n",
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      "                 <option value=\"geos/wz.html\">\n",
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      "                 <option value=\"geos/sw.html\">\n",
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      "                 <option value=\"geos/sz.html\">\n",
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      "                 <option value=\"geos/sy.html\">\n",
      "                  Syria\n",
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      "                 <option value=\"geos/tw.html\">\n",
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      "                 <option value=\"geos/ti.html\">\n",
      "                  Tajikistan\n",
      "                 </option>\n",
      "                 <option value=\"geos/tz.html\">\n",
      "                  Tanzania\n",
      "                 </option>\n",
      "                 <option value=\"geos/th.html\">\n",
      "                  Thailand\n",
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      "                 <option value=\"geos/tt.html\">\n",
      "                  Timor-Leste\n",
      "                 </option>\n",
      "                 <option value=\"geos/to.html\">\n",
      "                  Togo\n",
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      "                 <option value=\"geos/tl.html\">\n",
      "                  Tokelau\n",
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      "                 <option value=\"geos/tn.html\">\n",
      "                  Tonga\n",
      "                 </option>\n",
      "                 <option value=\"geos/td.html\">\n",
      "                  Trinidad and Tobago\n",
      "                 </option>\n",
      "                 <option value=\"geos/ts.html\">\n",
      "                  Tunisia\n",
      "                 </option>\n",
      "                 <option value=\"geos/tu.html\">\n",
      "                  Turkey\n",
      "                 </option>\n",
      "                 <option value=\"geos/tx.html\">\n",
      "                  Turkmenistan\n",
      "                 </option>\n",
      "                 <option value=\"geos/tk.html\">\n",
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      "                 <option value=\"geos/tv.html\">\n",
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      "                  Uganda\n",
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      "                 <option value=\"geos/up.html\">\n",
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      "                 <option value=\"geos/us.html\">\n",
      "                  United States\n",
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      "                 <option value=\"geos/um.html\">\n",
      "                  United States Pacific Island Wildlife Refuges\n",
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      "                 <option value=\"geos/uy.html\">\n",
      "                  Uruguay\n",
      "                 </option>\n",
      "                 <option value=\"geos/uz.html\">\n",
      "                  Uzbekistan\n",
      "                 </option>\n",
      "                 <option value=\"geos/nh.html\">\n",
      "                  Vanuatu\n",
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      "                 <option value=\"geos/ve.html\">\n",
      "                  Venezuela\n",
      "                 </option>\n",
      "                 <option value=\"geos/vm.html\">\n",
      "                  Vietnam\n",
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      "                 <option value=\"geos/vq.html\">\n",
      "                  Virgin Islands\n",
      "                 </option>\n",
      "                 <option value=\"geos/wq.html\">\n",
      "                  Wake Island\n",
      "                 </option>\n",
      "                 <option value=\"geos/wf.html\">\n",
      "                  Wallis and Futuna\n",
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      "                 <option value=\"geos/we.html\">\n",
      "                  West Bank\n",
      "                 </option>\n",
      "                 <option value=\"geos/wi.html\">\n",
      "                  Western Sahara\n",
      "                 </option>\n",
      "                 <option value=\"geos/ym.html\">\n",
      "                  Yemen\n",
      "                 </option>\n",
      "                 <option value=\"geos/za.html\">\n",
      "                  Zambia\n",
      "                 </option>\n",
      "                 <option value=\"geos/zi.html\">\n",
      "                  Zimbabwe\n",
      "                 </option>\n",
      "                 <option value=\"geos/ee.html\">\n",
      "                  European Union\n",
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      "              </div>\n",
      "              <!-- cntrySelect -->\n",
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      "                     Definitions and Notes\n",
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      "                     Guide to Country Profiles\n",
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      "                     A: abbreviations\n",
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      "                     B: international organizations and groups\n",
      "                    </a>\n",
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      "                    <a href=\"appendix/appendix-c.html\">\n",
      "                     C: selected international environmental agreements\n",
      "                    </a>\n",
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      "                    <a href=\"appendix/appendix-d.html\">\n",
      "                     D: cross-reference list of country data codes\n",
      "                    </a>\n",
      "                   </li>\n",
      "                   <li>\n",
      "                    <a href=\"appendix/appendix-e.html\">\n",
      "                     E: cross-reference list of hydrographic data codes\n",
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      "                   <li>\n",
      "                    <a href=\"appendix/appendix-f.html\">\n",
      "                     F: cross-reference list of geographic names\n",
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      "                     G: weights and measures\n",
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      "          <h3>\n",
      "           <a href=\"/about-cia\">\n",
      "            About CIA\n",
      "           </a>\n",
      "          </h3>\n",
      "          <ul>\n",
      "           <li>\n",
      "            <a href=\"/about-cia/todays-cia\">\n",
      "             Today's CIA\n",
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      "             Leadership\n",
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      "             History of the CIA\n",
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      "             Life at CIA\n",
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      "             Benefits\n",
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      "             Diversity\n",
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      "             Military Transition\n",
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      "             Tools and Challenges\n",
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      "             FAQs\n",
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      "             Video Center\n",
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      "          </ul>\n",
      "          <h3>\n",
      "           <a href=\"/offices-of-cia\">\n",
      "            Offices of CIA\n",
      "           </a>\n",
      "          </h3>\n",
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      "             Human Resources\n",
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      "             Public Affairs\n",
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      "             General Counsel\n",
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      "             Equal Employment Opportunity\n",
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      "           <li>\n",
      "            <a href=\"/offices-of-cia/congressional-affairs\">\n",
      "             Congressional Affairs\n",
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      "             Inspector General\n",
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      "             Military Affairs\n",
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      "          <h3>\n",
      "           <a href=\"/news-information\">\n",
      "            News &amp; Information\n",
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      "            <a href=\"/news-information/blog\">\n",
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      "             Press Releases &amp; Statements\n",
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      "             Speeches &amp; Testimony\n",
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      "             CIA &amp; the War on Terrorism\n",
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      "             Your News\n",
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      "            Library\n",
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      "             Kent Center Occasional Papers\n",
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      "            <a href=\"/library/intelligence-literature\">\n",
      "             Intelligence Literature\n",
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      "            <a href=\"/library/reports\">\n",
      "             Reports\n",
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      "           </li>\n",
      "           <li>\n",
      "            <a href=\"/library/related-links.html\">\n",
      "             Related Links\n",
      "            </a>\n",
      "           </li>\n",
      "           <li>\n",
      "            <a href=\"/library/video-center\">\n",
      "             Video Center\n",
      "            </a>\n",
      "           </li>\n",
      "          </ul>\n",
      "         </div>\n",
      "         <div class=\"info-block add\">\n",
      "          <h3>\n",
      "           <a href=\"/kids-page\">\n",
      "            Kids' Zone\n",
      "           </a>\n",
      "          </h3>\n",
      "          <ul>\n",
      "           <li>\n",
      "            <a href=\"/kids-page/k-5th-grade\">\n",
      "             K-5th Grade\n",
      "            </a>\n",
      "           </li>\n",
      "           <li>\n",
      "            <a href=\"/kids-page/6-12th-grade\">\n",
      "             6-12th Grade\n",
      "            </a>\n",
      "           </li>\n",
      "           <li>\n",
      "            <a href=\"/kids-page/parents-teachers\">\n",
      "             Parents &amp; Teachers\n",
      "            </a>\n",
      "           </li>\n",
      "           <li>\n",
      "            <a href=\"/kids-page/games\">\n",
      "             Games\n",
      "            </a>\n",
      "           </li>\n",
      "           <li>\n",
      "            <a href=\"/kids-page/related-links\">\n",
      "             Related Links\n",
      "            </a>\n",
      "           </li>\n",
      "           <li>\n",
      "            <a href=\"/kids-page/privacy-statement\">\n",
      "             Privacy Statement\n",
      "            </a>\n",
      "           </li>\n",
      "          </ul>\n",
      "          <h3>\n",
      "           <a href=\"/contact-cia\">\n",
      "            Connect with CIA\n",
      "           </a>\n",
      "          </h3>\n",
      "          <ul class=\"socials-list\">\n",
      "           <li>\n",
      "            <a class=\"social-6\" href=\"https://twitter.com/CIA\">\n",
      "             CIA Twitter\n",
      "            </a>\n",
      "           </li>\n",
      "           <li>\n",
      "            <a class=\"social-5\" href=\"https://www.facebook.com/Central.Intelligence.Agency\">\n",
      "             CIA Facebook\n",
      "            </a>\n",
      "           </li>\n",
      "           <li>\n",
      "            <a href=\"http://www.youtube.com/user/ciagov\">\n",
      "             CIA YouTube\n",
      "            </a>\n",
      "           </li>\n",
      "           <li>\n",
      "            <a class=\"social-2\" href=\"http://www.flickr.com/photos/ciagov\">\n",
      "             CIA Flickr PhotoStream\n",
      "            </a>\n",
      "           </li>\n",
      "           <li>\n",
      "            <a class=\"social-3\" href=\"/news-information/your-news\">\n",
      "             RSS\n",
      "            </a>\n",
      "           </li>\n",
      "           <li>\n",
      "            <a class=\"social-4 landingTrigger\" href=\"/contact-cia\">\n",
      "             Contact Us\n",
      "            </a>\n",
      "           </li>\n",
      "          </ul>\n",
      "         </div>\n",
      "        </nav>\n",
      "        <div class=\"info-panel\" id=\"plugins\">\n",
      "         <h4>\n",
      "          * Required plugins\n",
      "         </h4>\n",
      "         <ul>\n",
      "          <li data-plugin=\"swf\">\n",
      "           <a href=\"http://get.adobe.com/flashplayer/\">\n",
      "            Adobe® Flash Player\n",
      "           </a>\n",
      "          </li>\n",
      "          <li data-plugin=\"pdf\">\n",
      "           <a href=\"http://get.adobe.com/reader/\">\n",
      "            Adobe® Reader®\n",
      "           </a>\n",
      "          </li>\n",
      "          <li data-plugin=\"doc\">\n",
      "           <a href=\"http://www.microsoft.com/en-us/download/details.aspx?id=4\">\n",
      "            MS Word Viewer\n",
      "           </a>\n",
      "          </li>\n",
      "         </ul>\n",
      "        </div>\n",
      "       </div>\n",
      "      </div>\n",
      "     </footer>\n",
      "    </div>\n",
      "    <div class=\"footer-panel\">\n",
      "     <nav class=\"sub-nav\">\n",
      "      <h3 class=\"visuallyhidden\">\n",
      "       Footer Navigation\n",
      "      </h3>\n",
      "      <ul>\n",
      "       <li>\n",
      "        <a href=\"/about-cia/site-policies/#privacy-notice\" title=\"Site Policies\">\n",
      "         Privacy\n",
      "        </a>\n",
      "       </li>\n",
      "       <li>\n",
      "        <a href=\"/about-cia/site-policies/#copy\" title=\"Site Policies\">\n",
      "         Copyright\n",
      "        </a>\n",
      "       </li>\n",
      "       <li>\n",
      "        <a href=\"/about-cia/site-policies/\" title=\"Site Policies\">\n",
      "         Site Policies\n",
      "        </a>\n",
      "       </li>\n",
      "       <li>\n",
      "        <a href=\"http://www.usa.gov/\">\n",
      "         USA.gov\n",
      "        </a>\n",
      "       </li>\n",
      "       <li>\n",
      "        <a href=\"http://www.foia.cia.gov/\">\n",
      "         FOIA\n",
      "        </a>\n",
      "       </li>\n",
      "       <li>\n",
      "        <a href=\"http://www.dni.gov/\">\n",
      "         DNI.gov\n",
      "        </a>\n",
      "       </li>\n",
      "       <li>\n",
      "        <a href=\"/about-cia/no-fear-act/\" title=\"No FEAR Act\">\n",
      "         NoFEAR Act\n",
      "        </a>\n",
      "       </li>\n",
      "       <li>\n",
      "        <a href=\"/offices-of-cia/inspector-general/\">\n",
      "         Inspector General\n",
      "        </a>\n",
      "       </li>\n",
      "       <!--<li><a tal:attributes=\"href string:$portal_url/mobile/\" href=\"#\" >Mobile Site</a></li>-->\n",
      "       <li>\n",
      "        <a class=\"landingTrigger\" href=\"/contact-cia/\">\n",
      "         Contact CIA\n",
      "        </a>\n",
      "       </li>\n",
      "       <li>\n",
      "        <a href=\"/sitemap.html\">\n",
      "         Site Map\n",
      "        </a>\n",
      "       </li>\n",
      "      </ul>\n",
      "      <a class=\"footer-logo\" href=\"/open/\">\n",
      "       <img alt=\"open gov\" src=\"/++theme++contextual.agencytheme/images/ico-06.png\"/>\n",
      "      </a>\n",
      "     </nav>\n",
      "    </div>\n",
      "    <div class=\"skip\">\n",
      "     <a href=\"#wrapper\">\n",
      "      back to top\n",
      "     </a>\n",
      "    </div>\n",
      "   </div>\n",
      "  </div>\n",
      " </body>\n",
      "</html>\n",
      "\n"
     ]
    }
   ],
   "source": [
    "soup = BeautifulSoup(html, 'html.parser')\n",
    "print(soup.prettify())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Extract the country names and codes from the parsed HTML doc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "country_codes=[]\n",
    "country_names=[]\n",
    "for tag in soup.find_all('option'):\n",
    "    country_codes.append(tag.get('value')[5:7])\n",
    "    country_names.append(tag.text)\n",
    "\n",
    "temp=country_codes.pop(0) # To remove the first entry 'World'\n",
    "temp=country_names.pop(0) # To remove the first entry 'World'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "COUNTRY NAMES\n",
      "------------------------------\n",
      " Afghanistan , Akrotiri , Albania , Algeria , American Samoa , Andorra , Angola , Anguilla , Antarctica , Antigua and Barbuda , Arctic Ocean , Argentina , Armenia , Aruba , Ashmore and Cartier Islands , Atlantic Ocean , Australia , Austria , Azerbaijan , Bahamas, The , Bahrain , Baker Island , Bangladesh , Barbados , Belarus , Belgium , Belize , Benin , Bermuda , Bhutan , Bolivia , Bosnia and Herzegovina , Botswana , Bouvet Island , Brazil , British Indian Ocean Territory , British Virgin Islands , Brunei , Bulgaria , Burkina Faso , Burma , Burundi , Cabo Verde , Cambodia , Cameroon , Canada , Cayman Islands , Central African Republic , Chad , Chile , China , Christmas Island , Clipperton Island , Cocos (Keeling) Islands , Colombia , Comoros , Congo, Democratic Republic of the , Congo, Republic of the , Cook Islands , Coral Sea Islands , Costa Rica , Cote d'Ivoire , Croatia , Cuba , Curacao , Cyprus , Czechia , Denmark , Dhekelia , Djibouti , Dominica , Dominican Republic , Ecuador , Egypt , El Salvador , Equatorial Guinea , Eritrea , Estonia , Ethiopia , Falkland Islands (Islas Malvinas) , Faroe Islands , Fiji , Finland , France , French Polynesia , French Southern and Antarctic Lands , Gabon , Gambia, The , Gaza Strip , Georgia , Germany , Ghana , Gibraltar , Greece , Greenland , Grenada , Guam , Guatemala , Guernsey , Guinea , Guinea-Bissau , Guyana , Haiti , Heard Island and McDonald Islands , Holy See (Vatican City) , Honduras , Hong Kong , Howland Island , Hungary , Iceland , India , Indian Ocean , Indonesia , Iran , Iraq , Ireland , Isle of Man , Israel , Italy , Jamaica , Jan Mayen , Japan , Jarvis Island , Jersey , Johnston Atoll , Jordan , Kazakhstan , Kenya , Kingman Reef , Kiribati , Korea, North , Korea, South , Kosovo , Kuwait , Kyrgyzstan , Laos , Latvia , Lebanon , Lesotho , Liberia , Libya , Liechtenstein , Lithuania , Luxembourg , Macau , Macedonia , Madagascar , Malawi , Malaysia , Maldives , Mali , Malta , Marshall Islands , Mauritania , Mauritius , Mexico , Micronesia, Federated States of , Midway Islands , Moldova , Monaco , Mongolia , Montenegro , Montserrat , Morocco , Mozambique , Namibia , Nauru , Navassa Island , Nepal , Netherlands , New Caledonia , New Zealand , Nicaragua , Niger , Nigeria , Niue , Norfolk Island , Northern Mariana Islands , Norway , Oman , Pacific Ocean , Pakistan , Palau , Palmyra Atoll , Panama , Papua New Guinea , Paracel Islands , Paraguay , Peru , Philippines , Pitcairn Islands , Poland , Portugal , Puerto Rico , Qatar , Romania , Russia , Rwanda , Saint Barthelemy , Saint Helena, Ascension, and Tristan da Cunha , Saint Kitts and Nevis , Saint Lucia , Saint Martin , Saint Pierre and Miquelon , Saint Vincent and the Grenadines , Samoa , San Marino , Sao Tome and Principe , Saudi Arabia , Senegal , Serbia , Seychelles , Sierra Leone , Singapore , Sint Maarten , Slovakia , Slovenia , Solomon Islands , Somalia , South Africa , Southern Ocean , South Georgia and South Sandwich Islands , South Sudan , Spain , Spratly Islands , Sri Lanka , Sudan , Suriname , Svalbard , Swaziland , Sweden , Switzerland , Syria , Taiwan , Tajikistan , Tanzania , Thailand , Timor-Leste , Togo , Tokelau , Tonga , Trinidad and Tobago , Tunisia , Turkey , Turkmenistan , Turks and Caicos Islands , Tuvalu , Uganda , Ukraine , United Arab Emirates , United Kingdom , United States , United States Pacific Island Wildlife Refuges , Uruguay , Uzbekistan , Vanuatu , Venezuela , Vietnam , Virgin Islands , Wake Island , Wallis and Futuna , West Bank , Western Sahara , Yemen , Zambia , Zimbabwe , European Union ,\n",
      "\n",
      "COUNTRY CODES\n",
      "------------------------------\n",
      "af,ax,al,ag,aq,an,ao,av,ay,ac,xq,ar,am,aa,at,zh,as,au,aj,bf,ba,um,bg,bb,bo,be,bh,bn,bd,bt,bl,bk,bc,bv,br,io,vi,bx,bu,uv,bm,by,cv,cb,cm,ca,cj,ct,cd,ci,ch,kt,ip,ck,co,cn,cg,cf,cw,cr,cs,iv,hr,cu,cc,cy,ez,da,dx,dj,do,dr,ec,eg,es,ek,er,en,et,fk,fo,fj,fi,fr,fp,fs,gb,ga,gz,gg,gm,gh,gi,gr,gl,gj,gq,gt,gk,gv,pu,gy,ha,hm,vt,ho,hk,um,hu,ic,in,xo,id,ir,iz,ei,im,is,it,jm,jn,ja,um,je,um,jo,kz,ke,um,kr,kn,ks,kv,ku,kg,la,lg,le,lt,li,ly,ls,lh,lu,mc,mk,ma,mi,my,mv,ml,mt,rm,mr,mp,mx,fm,um,md,mn,mg,mj,mh,mo,mz,wa,nr,bq,np,nl,nc,nz,nu,ng,ni,ne,nf,cq,no,mu,zn,pk,ps,um,pm,pp,pf,pa,pe,rp,pc,pl,po,rq,qa,ro,rs,rw,tb,sh,sc,st,rn,sb,vc,ws,sm,tp,sa,sg,ri,se,sl,sn,sk,lo,si,bp,so,sf,oo,sx,od,sp,pg,ce,su,ns,sv,wz,sw,sz,sy,tw,ti,tz,th,tt,to,tl,tn,td,ts,tu,tx,tk,tv,ug,up,ae,uk,us,um,uy,uz,nh,ve,vm,vq,wq,wf,we,wi,ym,za,zi,ee,"
     ]
    }
   ],
   "source": [
    "print('COUNTRY NAMES\\n'+'-'*30)\n",
    "for country in country_names[1:]:\n",
    "    print(country,end=',')\n",
    "print('\\n\\nCOUNTRY CODES\\n'+'-'*30)\n",
    "for country in country_codes[1:]:\n",
    "    print(country,end=',')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Extract the demographics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0-14 years % data extraction complete for  Afghanistan !\n",
      "15-24 years % data extraction complete for  Afghanistan !\n",
      "25-54 years % data extraction complete for  Afghanistan !\n",
      "55-64 years % data extraction complete for  Afghanistan !\n",
      "65 years and beyond % data extraction complete for  Afghanistan !\n",
      "**0-14 years % data not found for  Akrotiri !**\n",
      "**15-24 years % data not found for  Akrotiri !**\n",
      "**25-54 years % data not found for  Akrotiri !**\n",
      "**55-64 years % data not found for  Akrotiri !**\n",
      "**65 years and beyond % data not found for  Akrotiri !**\n",
      "0-14 years % data extraction complete for  Albania !\n",
      "15-24 years % data extraction complete for  Albania !\n",
      "25-54 years % data extraction complete for  Albania !\n",
      "55-64 years % data extraction complete for  Albania !\n",
      "65 years and beyond % data extraction complete for  Albania !\n",
      "0-14 years % data extraction complete for  Algeria !\n",
      "15-24 years % data extraction complete for  Algeria !\n",
      "25-54 years % data extraction complete for  Algeria !\n",
      "55-64 years % data extraction complete for  Algeria !\n",
      "65 years and beyond % data extraction complete for  Algeria !\n",
      "0-14 years % data extraction complete for  American Samoa !\n",
      "15-24 years % data extraction complete for  American Samoa !\n",
      "25-54 years % data extraction complete for  American Samoa !\n",
      "55-64 years % data extraction complete for  American Samoa !\n",
      "65 years and beyond % data extraction complete for  American Samoa !\n",
      "0-14 years % data extraction complete for  Andorra !\n",
      "15-24 years % data extraction complete for  Andorra !\n",
      "25-54 years % data extraction complete for  Andorra !\n",
      "55-64 years % data extraction complete for  Andorra !\n",
      "65 years and beyond % data extraction complete for  Andorra !\n",
      "0-14 years % data extraction complete for  Angola !\n",
      "15-24 years % data extraction complete for  Angola !\n",
      "25-54 years % data extraction complete for  Angola !\n",
      "55-64 years % data extraction complete for  Angola !\n",
      "65 years and beyond % data extraction complete for  Angola !\n",
      "0-14 years % data extraction complete for  Anguilla !\n",
      "15-24 years % data extraction complete for  Anguilla !\n",
      "25-54 years % data extraction complete for  Anguilla !\n",
      "55-64 years % data extraction complete for  Anguilla !\n",
      "65 years and beyond % data extraction complete for  Anguilla !\n",
      "**0-14 years % data not found for  Antarctica !**\n",
      "**15-24 years % data not found for  Antarctica !**\n",
      "**25-54 years % data not found for  Antarctica !**\n",
      "**55-64 years % data not found for  Antarctica !**\n",
      "**65 years and beyond % data not found for  Antarctica !**\n",
      "0-14 years % data extraction complete for  Antigua and Barbuda !\n",
      "15-24 years % data extraction complete for  Antigua and Barbuda !\n",
      "25-54 years % data extraction complete for  Antigua and Barbuda !\n",
      "55-64 years % data extraction complete for  Antigua and Barbuda !\n",
      "65 years and beyond % data extraction complete for  Antigua and Barbuda !\n",
      "**0-14 years % data not found for  Arctic Ocean !**\n",
      "**15-24 years % data not found for  Arctic Ocean !**\n",
      "**25-54 years % data not found for  Arctic Ocean !**\n",
      "**55-64 years % data not found for  Arctic Ocean !**\n",
      "**65 years and beyond % data not found for  Arctic Ocean !**\n",
      "0-14 years % data extraction complete for  Argentina !\n",
      "15-24 years % data extraction complete for  Argentina !\n",
      "25-54 years % data extraction complete for  Argentina !\n",
      "55-64 years % data extraction complete for  Argentina !\n",
      "65 years and beyond % data extraction complete for  Argentina !\n",
      "0-14 years % data extraction complete for  Armenia !\n",
      "15-24 years % data extraction complete for  Armenia !\n",
      "25-54 years % data extraction complete for  Armenia !\n",
      "55-64 years % data extraction complete for  Armenia !\n",
      "65 years and beyond % data extraction complete for  Armenia !\n",
      "0-14 years % data extraction complete for  Aruba !\n",
      "15-24 years % data extraction complete for  Aruba !\n",
      "25-54 years % data extraction complete for  Aruba !\n",
      "55-64 years % data extraction complete for  Aruba !\n",
      "65 years and beyond % data extraction complete for  Aruba !\n",
      "**0-14 years % data not found for  Ashmore and Cartier Islands !**\n",
      "**15-24 years % data not found for  Ashmore and Cartier Islands !**\n",
      "**25-54 years % data not found for  Ashmore and Cartier Islands !**\n",
      "**55-64 years % data not found for  Ashmore and Cartier Islands !**\n",
      "**65 years and beyond % data not found for  Ashmore and Cartier Islands !**\n",
      "**0-14 years % data not found for  Atlantic Ocean !**\n",
      "**15-24 years % data not found for  Atlantic Ocean !**\n",
      "**25-54 years % data not found for  Atlantic Ocean !**\n",
      "**55-64 years % data not found for  Atlantic Ocean !**\n",
      "**65 years and beyond % data not found for  Atlantic Ocean !**\n",
      "0-14 years % data extraction complete for  Australia !\n",
      "15-24 years % data extraction complete for  Australia !\n",
      "25-54 years % data extraction complete for  Australia !\n",
      "55-64 years % data extraction complete for  Australia !\n",
      "65 years and beyond % data extraction complete for  Australia !\n",
      "0-14 years % data extraction complete for  Austria !\n",
      "15-24 years % data extraction complete for  Austria !\n",
      "25-54 years % data extraction complete for  Austria !\n",
      "55-64 years % data extraction complete for  Austria !\n",
      "65 years and beyond % data extraction complete for  Austria !\n",
      "0-14 years % data extraction complete for  Azerbaijan !\n",
      "15-24 years % data extraction complete for  Azerbaijan !\n",
      "25-54 years % data extraction complete for  Azerbaijan !\n",
      "55-64 years % data extraction complete for  Azerbaijan !\n",
      "65 years and beyond % data extraction complete for  Azerbaijan !\n",
      "0-14 years % data extraction complete for  Bahamas, The !\n",
      "15-24 years % data extraction complete for  Bahamas, The !\n",
      "25-54 years % data extraction complete for  Bahamas, The !\n",
      "55-64 years % data extraction complete for  Bahamas, The !\n",
      "65 years and beyond % data extraction complete for  Bahamas, The !\n",
      "0-14 years % data extraction complete for  Bahrain !\n",
      "15-24 years % data extraction complete for  Bahrain !\n",
      "25-54 years % data extraction complete for  Bahrain !\n",
      "55-64 years % data extraction complete for  Bahrain !\n",
      "65 years and beyond % data extraction complete for  Bahrain !\n",
      "**0-14 years % data not found for  Baker Island !**\n",
      "**15-24 years % data not found for  Baker Island !**\n",
      "**25-54 years % data not found for  Baker Island !**\n",
      "**55-64 years % data not found for  Baker Island !**\n",
      "**65 years and beyond % data not found for  Baker Island !**\n",
      "0-14 years % data extraction complete for  Bangladesh !\n",
      "15-24 years % data extraction complete for  Bangladesh !\n",
      "25-54 years % data extraction complete for  Bangladesh !\n",
      "55-64 years % data extraction complete for  Bangladesh !\n",
      "65 years and beyond % data extraction complete for  Bangladesh !\n",
      "0-14 years % data extraction complete for  Barbados !\n",
      "15-24 years % data extraction complete for  Barbados !\n",
      "25-54 years % data extraction complete for  Barbados !\n",
      "55-64 years % data extraction complete for  Barbados !\n",
      "65 years and beyond % data extraction complete for  Barbados !\n",
      "0-14 years % data extraction complete for  Belarus !\n",
      "15-24 years % data extraction complete for  Belarus !\n",
      "25-54 years % data extraction complete for  Belarus !\n",
      "55-64 years % data extraction complete for  Belarus !\n",
      "65 years and beyond % data extraction complete for  Belarus !\n",
      "0-14 years % data extraction complete for  Belgium !\n",
      "15-24 years % data extraction complete for  Belgium !\n",
      "25-54 years % data extraction complete for  Belgium !\n",
      "55-64 years % data extraction complete for  Belgium !\n",
      "65 years and beyond % data extraction complete for  Belgium !\n",
      "0-14 years % data extraction complete for  Belize !\n",
      "15-24 years % data extraction complete for  Belize !\n",
      "25-54 years % data extraction complete for  Belize !\n",
      "55-64 years % data extraction complete for  Belize !\n",
      "65 years and beyond % data extraction complete for  Belize !\n",
      "0-14 years % data extraction complete for  Benin !\n",
      "15-24 years % data extraction complete for  Benin !\n",
      "25-54 years % data extraction complete for  Benin !\n",
      "55-64 years % data extraction complete for  Benin !\n",
      "65 years and beyond % data extraction complete for  Benin !\n",
      "0-14 years % data extraction complete for  Bermuda !\n",
      "15-24 years % data extraction complete for  Bermuda !\n",
      "25-54 years % data extraction complete for  Bermuda !\n",
      "55-64 years % data extraction complete for  Bermuda !\n",
      "65 years and beyond % data extraction complete for  Bermuda !\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0-14 years % data extraction complete for  Bhutan !\n",
      "15-24 years % data extraction complete for  Bhutan !\n",
      "25-54 years % data extraction complete for  Bhutan !\n",
      "55-64 years % data extraction complete for  Bhutan !\n",
      "65 years and beyond % data extraction complete for  Bhutan !\n",
      "0-14 years % data extraction complete for  Bolivia !\n",
      "15-24 years % data extraction complete for  Bolivia !\n",
      "25-54 years % data extraction complete for  Bolivia !\n",
      "55-64 years % data extraction complete for  Bolivia !\n",
      "65 years and beyond % data extraction complete for  Bolivia !\n",
      "0-14 years % data extraction complete for  Bosnia and Herzegovina !\n",
      "15-24 years % data extraction complete for  Bosnia and Herzegovina !\n",
      "25-54 years % data extraction complete for  Bosnia and Herzegovina !\n",
      "55-64 years % data extraction complete for  Bosnia and Herzegovina !\n",
      "65 years and beyond % data extraction complete for  Bosnia and Herzegovina !\n",
      "0-14 years % data extraction complete for  Botswana !\n",
      "15-24 years % data extraction complete for  Botswana !\n",
      "25-54 years % data extraction complete for  Botswana !\n",
      "55-64 years % data extraction complete for  Botswana !\n",
      "65 years and beyond % data extraction complete for  Botswana !\n",
      "**0-14 years % data not found for  Bouvet Island !**\n",
      "**15-24 years % data not found for  Bouvet Island !**\n",
      "**25-54 years % data not found for  Bouvet Island !**\n",
      "**55-64 years % data not found for  Bouvet Island !**\n",
      "**65 years and beyond % data not found for  Bouvet Island !**\n",
      "0-14 years % data extraction complete for  Brazil !\n",
      "15-24 years % data extraction complete for  Brazil !\n",
      "25-54 years % data extraction complete for  Brazil !\n",
      "55-64 years % data extraction complete for  Brazil !\n",
      "65 years and beyond % data extraction complete for  Brazil !\n",
      "**0-14 years % data not found for  British Indian Ocean Territory !**\n",
      "**15-24 years % data not found for  British Indian Ocean Territory !**\n",
      "**25-54 years % data not found for  British Indian Ocean Territory !**\n",
      "**55-64 years % data not found for  British Indian Ocean Territory !**\n",
      "**65 years and beyond % data not found for  British Indian Ocean Territory !**\n",
      "0-14 years % data extraction complete for  British Virgin Islands !\n",
      "15-24 years % data extraction complete for  British Virgin Islands !\n",
      "25-54 years % data extraction complete for  British Virgin Islands !\n",
      "55-64 years % data extraction complete for  British Virgin Islands !\n",
      "65 years and beyond % data extraction complete for  British Virgin Islands !\n",
      "0-14 years % data extraction complete for  Brunei !\n",
      "15-24 years % data extraction complete for  Brunei !\n",
      "25-54 years % data extraction complete for  Brunei !\n",
      "55-64 years % data extraction complete for  Brunei !\n",
      "65 years and beyond % data extraction complete for  Brunei !\n",
      "0-14 years % data extraction complete for  Bulgaria !\n",
      "15-24 years % data extraction complete for  Bulgaria !\n",
      "25-54 years % data extraction complete for  Bulgaria !\n",
      "55-64 years % data extraction complete for  Bulgaria !\n",
      "65 years and beyond % data extraction complete for  Bulgaria !\n",
      "0-14 years % data extraction complete for  Burkina Faso !\n",
      "15-24 years % data extraction complete for  Burkina Faso !\n",
      "25-54 years % data extraction complete for  Burkina Faso !\n",
      "55-64 years % data extraction complete for  Burkina Faso !\n",
      "65 years and beyond % data extraction complete for  Burkina Faso !\n",
      "0-14 years % data extraction complete for  Burma !\n",
      "15-24 years % data extraction complete for  Burma !\n",
      "25-54 years % data extraction complete for  Burma !\n",
      "55-64 years % data extraction complete for  Burma !\n",
      "65 years and beyond % data extraction complete for  Burma !\n",
      "0-14 years % data extraction complete for  Burundi !\n",
      "15-24 years % data extraction complete for  Burundi !\n",
      "25-54 years % data extraction complete for  Burundi !\n",
      "55-64 years % data extraction complete for  Burundi !\n",
      "65 years and beyond % data extraction complete for  Burundi !\n",
      "0-14 years % data extraction complete for  Cabo Verde !\n",
      "15-24 years % data extraction complete for  Cabo Verde !\n",
      "25-54 years % data extraction complete for  Cabo Verde !\n",
      "55-64 years % data extraction complete for  Cabo Verde !\n",
      "65 years and beyond % data extraction complete for  Cabo Verde !\n",
      "0-14 years % data extraction complete for  Cambodia !\n",
      "15-24 years % data extraction complete for  Cambodia !\n",
      "25-54 years % data extraction complete for  Cambodia !\n",
      "55-64 years % data extraction complete for  Cambodia !\n",
      "65 years and beyond % data extraction complete for  Cambodia !\n",
      "0-14 years % data extraction complete for  Cameroon !\n",
      "15-24 years % data extraction complete for  Cameroon !\n",
      "25-54 years % data extraction complete for  Cameroon !\n",
      "55-64 years % data extraction complete for  Cameroon !\n",
      "65 years and beyond % data extraction complete for  Cameroon !\n",
      "0-14 years % data extraction complete for  Canada !\n",
      "15-24 years % data extraction complete for  Canada !\n",
      "25-54 years % data extraction complete for  Canada !\n",
      "55-64 years % data extraction complete for  Canada !\n",
      "65 years and beyond % data extraction complete for  Canada !\n",
      "0-14 years % data extraction complete for  Cayman Islands !\n",
      "15-24 years % data extraction complete for  Cayman Islands !\n",
      "25-54 years % data extraction complete for  Cayman Islands !\n",
      "55-64 years % data extraction complete for  Cayman Islands !\n",
      "65 years and beyond % data extraction complete for  Cayman Islands !\n",
      "0-14 years % data extraction complete for  Central African Republic !\n",
      "15-24 years % data extraction complete for  Central African Republic !\n",
      "25-54 years % data extraction complete for  Central African Republic !\n",
      "55-64 years % data extraction complete for  Central African Republic !\n",
      "65 years and beyond % data extraction complete for  Central African Republic !\n",
      "0-14 years % data extraction complete for  Chad !\n",
      "15-24 years % data extraction complete for  Chad !\n",
      "25-54 years % data extraction complete for  Chad !\n",
      "55-64 years % data extraction complete for  Chad !\n",
      "65 years and beyond % data extraction complete for  Chad !\n",
      "0-14 years % data extraction complete for  Chile !\n",
      "15-24 years % data extraction complete for  Chile !\n",
      "25-54 years % data extraction complete for  Chile !\n",
      "55-64 years % data extraction complete for  Chile !\n",
      "65 years and beyond % data extraction complete for  Chile !\n",
      "0-14 years % data extraction complete for  China !\n",
      "15-24 years % data extraction complete for  China !\n",
      "25-54 years % data extraction complete for  China !\n",
      "55-64 years % data extraction complete for  China !\n",
      "65 years and beyond % data extraction complete for  China !\n",
      "0-14 years % data extraction complete for  Christmas Island !\n",
      "15-24 years % data extraction complete for  Christmas Island !\n",
      "25-54 years % data extraction complete for  Christmas Island !\n",
      "55-64 years % data extraction complete for  Christmas Island !\n",
      "65 years and beyond % data extraction complete for  Christmas Island !\n",
      "**0-14 years % data not found for  Clipperton Island !**\n",
      "**15-24 years % data not found for  Clipperton Island !**\n",
      "**25-54 years % data not found for  Clipperton Island !**\n",
      "**55-64 years % data not found for  Clipperton Island !**\n",
      "**65 years and beyond % data not found for  Clipperton Island !**\n",
      "**0-14 years % data not found for  Cocos (Keeling) Islands !**\n",
      "**15-24 years % data not found for  Cocos (Keeling) Islands !**\n",
      "**25-54 years % data not found for  Cocos (Keeling) Islands !**\n",
      "**55-64 years % data not found for  Cocos (Keeling) Islands !**\n",
      "**65 years and beyond % data not found for  Cocos (Keeling) Islands !**\n",
      "0-14 years % data extraction complete for  Colombia !\n",
      "15-24 years % data extraction complete for  Colombia !\n",
      "25-54 years % data extraction complete for  Colombia !\n",
      "55-64 years % data extraction complete for  Colombia !\n",
      "65 years and beyond % data extraction complete for  Colombia !\n",
      "0-14 years % data extraction complete for  Comoros !\n",
      "15-24 years % data extraction complete for  Comoros !\n",
      "25-54 years % data extraction complete for  Comoros !\n",
      "55-64 years % data extraction complete for  Comoros !\n",
      "65 years and beyond % data extraction complete for  Comoros !\n",
      "0-14 years % data extraction complete for  Congo, Democratic Republic of the !\n",
      "15-24 years % data extraction complete for  Congo, Democratic Republic of the !\n",
      "25-54 years % data extraction complete for  Congo, Democratic Republic of the !\n",
      "55-64 years % data extraction complete for  Congo, Democratic Republic of the !\n",
      "65 years and beyond % data extraction complete for  Congo, Democratic Republic of the !\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0-14 years % data extraction complete for  Congo, Republic of the !\n",
      "15-24 years % data extraction complete for  Congo, Republic of the !\n",
      "25-54 years % data extraction complete for  Congo, Republic of the !\n",
      "55-64 years % data extraction complete for  Congo, Republic of the !\n",
      "65 years and beyond % data extraction complete for  Congo, Republic of the !\n",
      "0-14 years % data extraction complete for  Cook Islands !\n",
      "15-24 years % data extraction complete for  Cook Islands !\n",
      "25-54 years % data extraction complete for  Cook Islands !\n",
      "55-64 years % data extraction complete for  Cook Islands !\n",
      "65 years and beyond % data extraction complete for  Cook Islands !\n",
      "**0-14 years % data not found for  Coral Sea Islands !**\n",
      "**15-24 years % data not found for  Coral Sea Islands !**\n",
      "**25-54 years % data not found for  Coral Sea Islands !**\n",
      "**55-64 years % data not found for  Coral Sea Islands !**\n",
      "**65 years and beyond % data not found for  Coral Sea Islands !**\n",
      "0-14 years % data extraction complete for  Costa Rica !\n",
      "15-24 years % data extraction complete for  Costa Rica !\n",
      "25-54 years % data extraction complete for  Costa Rica !\n",
      "55-64 years % data extraction complete for  Costa Rica !\n",
      "65 years and beyond % data extraction complete for  Costa Rica !\n",
      "0-14 years % data extraction complete for  Cote d'Ivoire !\n",
      "15-24 years % data extraction complete for  Cote d'Ivoire !\n",
      "25-54 years % data extraction complete for  Cote d'Ivoire !\n",
      "55-64 years % data extraction complete for  Cote d'Ivoire !\n",
      "65 years and beyond % data extraction complete for  Cote d'Ivoire !\n",
      "0-14 years % data extraction complete for  Croatia !\n",
      "15-24 years % data extraction complete for  Croatia !\n",
      "25-54 years % data extraction complete for  Croatia !\n",
      "55-64 years % data extraction complete for  Croatia !\n",
      "65 years and beyond % data extraction complete for  Croatia !\n",
      "0-14 years % data extraction complete for  Cuba !\n",
      "15-24 years % data extraction complete for  Cuba !\n",
      "25-54 years % data extraction complete for  Cuba !\n",
      "55-64 years % data extraction complete for  Cuba !\n",
      "65 years and beyond % data extraction complete for  Cuba !\n",
      "0-14 years % data extraction complete for  Curacao !\n",
      "15-24 years % data extraction complete for  Curacao !\n",
      "25-54 years % data extraction complete for  Curacao !\n",
      "55-64 years % data extraction complete for  Curacao !\n",
      "65 years and beyond % data extraction complete for  Curacao !\n",
      "0-14 years % data extraction complete for  Cyprus !\n",
      "15-24 years % data extraction complete for  Cyprus !\n",
      "25-54 years % data extraction complete for  Cyprus !\n",
      "55-64 years % data extraction complete for  Cyprus !\n",
      "65 years and beyond % data extraction complete for  Cyprus !\n",
      "0-14 years % data extraction complete for  Czechia !\n",
      "15-24 years % data extraction complete for  Czechia !\n",
      "25-54 years % data extraction complete for  Czechia !\n",
      "55-64 years % data extraction complete for  Czechia !\n",
      "65 years and beyond % data extraction complete for  Czechia !\n",
      "0-14 years % data extraction complete for  Denmark !\n",
      "15-24 years % data extraction complete for  Denmark !\n",
      "25-54 years % data extraction complete for  Denmark !\n",
      "55-64 years % data extraction complete for  Denmark !\n",
      "65 years and beyond % data extraction complete for  Denmark !\n",
      "**0-14 years % data not found for  Dhekelia !**\n",
      "**15-24 years % data not found for  Dhekelia !**\n",
      "**25-54 years % data not found for  Dhekelia !**\n",
      "**55-64 years % data not found for  Dhekelia !**\n",
      "**65 years and beyond % data not found for  Dhekelia !**\n",
      "0-14 years % data extraction complete for  Djibouti !\n",
      "15-24 years % data extraction complete for  Djibouti !\n",
      "25-54 years % data extraction complete for  Djibouti !\n",
      "55-64 years % data extraction complete for  Djibouti !\n",
      "65 years and beyond % data extraction complete for  Djibouti !\n",
      "0-14 years % data extraction complete for  Dominica !\n",
      "15-24 years % data extraction complete for  Dominica !\n",
      "25-54 years % data extraction complete for  Dominica !\n",
      "55-64 years % data extraction complete for  Dominica !\n",
      "65 years and beyond % data extraction complete for  Dominica !\n",
      "0-14 years % data extraction complete for  Dominican Republic !\n",
      "15-24 years % data extraction complete for  Dominican Republic !\n",
      "25-54 years % data extraction complete for  Dominican Republic !\n",
      "55-64 years % data extraction complete for  Dominican Republic !\n",
      "65 years and beyond % data extraction complete for  Dominican Republic !\n",
      "0-14 years % data extraction complete for  Ecuador !\n",
      "15-24 years % data extraction complete for  Ecuador !\n",
      "25-54 years % data extraction complete for  Ecuador !\n",
      "55-64 years % data extraction complete for  Ecuador !\n",
      "65 years and beyond % data extraction complete for  Ecuador !\n",
      "0-14 years % data extraction complete for  Egypt !\n",
      "15-24 years % data extraction complete for  Egypt !\n",
      "25-54 years % data extraction complete for  Egypt !\n",
      "55-64 years % data extraction complete for  Egypt !\n",
      "65 years and beyond % data extraction complete for  Egypt !\n",
      "0-14 years % data extraction complete for  El Salvador !\n",
      "15-24 years % data extraction complete for  El Salvador !\n",
      "25-54 years % data extraction complete for  El Salvador !\n",
      "55-64 years % data extraction complete for  El Salvador !\n",
      "65 years and beyond % data extraction complete for  El Salvador !\n",
      "0-14 years % data extraction complete for  Equatorial Guinea !\n",
      "15-24 years % data extraction complete for  Equatorial Guinea !\n",
      "25-54 years % data extraction complete for  Equatorial Guinea !\n",
      "55-64 years % data extraction complete for  Equatorial Guinea !\n",
      "65 years and beyond % data extraction complete for  Equatorial Guinea !\n",
      "0-14 years % data extraction complete for  Eritrea !\n",
      "15-24 years % data extraction complete for  Eritrea !\n",
      "25-54 years % data extraction complete for  Eritrea !\n",
      "55-64 years % data extraction complete for  Eritrea !\n",
      "65 years and beyond % data extraction complete for  Eritrea !\n",
      "0-14 years % data extraction complete for  Estonia !\n",
      "15-24 years % data extraction complete for  Estonia !\n",
      "25-54 years % data extraction complete for  Estonia !\n",
      "55-64 years % data extraction complete for  Estonia !\n",
      "65 years and beyond % data extraction complete for  Estonia !\n",
      "0-14 years % data extraction complete for  Ethiopia !\n",
      "15-24 years % data extraction complete for  Ethiopia !\n",
      "25-54 years % data extraction complete for  Ethiopia !\n",
      "55-64 years % data extraction complete for  Ethiopia !\n",
      "65 years and beyond % data extraction complete for  Ethiopia !\n",
      "**0-14 years % data not found for  Falkland Islands (Islas Malvinas) !**\n",
      "**15-24 years % data not found for  Falkland Islands (Islas Malvinas) !**\n",
      "**25-54 years % data not found for  Falkland Islands (Islas Malvinas) !**\n",
      "**55-64 years % data not found for  Falkland Islands (Islas Malvinas) !**\n",
      "**65 years and beyond % data not found for  Falkland Islands (Islas Malvinas) !**\n",
      "0-14 years % data extraction complete for  Faroe Islands !\n",
      "15-24 years % data extraction complete for  Faroe Islands !\n",
      "25-54 years % data extraction complete for  Faroe Islands !\n",
      "55-64 years % data extraction complete for  Faroe Islands !\n",
      "65 years and beyond % data extraction complete for  Faroe Islands !\n",
      "0-14 years % data extraction complete for  Fiji !\n",
      "15-24 years % data extraction complete for  Fiji !\n",
      "25-54 years % data extraction complete for  Fiji !\n",
      "55-64 years % data extraction complete for  Fiji !\n",
      "65 years and beyond % data extraction complete for  Fiji !\n",
      "0-14 years % data extraction complete for  Finland !\n",
      "15-24 years % data extraction complete for  Finland !\n",
      "25-54 years % data extraction complete for  Finland !\n",
      "55-64 years % data extraction complete for  Finland !\n",
      "65 years and beyond % data extraction complete for  Finland !\n",
      "0-14 years % data extraction complete for  France !\n",
      "15-24 years % data extraction complete for  France !\n",
      "25-54 years % data extraction complete for  France !\n",
      "55-64 years % data extraction complete for  France !\n",
      "65 years and beyond % data extraction complete for  France !\n",
      "0-14 years % data extraction complete for  French Polynesia !\n",
      "15-24 years % data extraction complete for  French Polynesia !\n",
      "25-54 years % data extraction complete for  French Polynesia !\n",
      "55-64 years % data extraction complete for  French Polynesia !\n",
      "65 years and beyond % data extraction complete for  French Polynesia !\n",
      "**0-14 years % data not found for  French Southern and Antarctic Lands !**\n",
      "**15-24 years % data not found for  French Southern and Antarctic Lands !**\n",
      "**25-54 years % data not found for  French Southern and Antarctic Lands !**\n",
      "**55-64 years % data not found for  French Southern and Antarctic Lands !**\n",
      "**65 years and beyond % data not found for  French Southern and Antarctic Lands !**\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0-14 years % data extraction complete for  Gabon !\n",
      "15-24 years % data extraction complete for  Gabon !\n",
      "25-54 years % data extraction complete for  Gabon !\n",
      "55-64 years % data extraction complete for  Gabon !\n",
      "65 years and beyond % data extraction complete for  Gabon !\n",
      "0-14 years % data extraction complete for  Gambia, The !\n",
      "15-24 years % data extraction complete for  Gambia, The !\n",
      "25-54 years % data extraction complete for  Gambia, The !\n",
      "55-64 years % data extraction complete for  Gambia, The !\n",
      "65 years and beyond % data extraction complete for  Gambia, The !\n",
      "0-14 years % data extraction complete for  Gaza Strip !\n",
      "15-24 years % data extraction complete for  Gaza Strip !\n",
      "25-54 years % data extraction complete for  Gaza Strip !\n",
      "55-64 years % data extraction complete for  Gaza Strip !\n",
      "65 years and beyond % data extraction complete for  Gaza Strip !\n",
      "0-14 years % data extraction complete for  Georgia !\n",
      "15-24 years % data extraction complete for  Georgia !\n",
      "25-54 years % data extraction complete for  Georgia !\n",
      "55-64 years % data extraction complete for  Georgia !\n",
      "65 years and beyond % data extraction complete for  Georgia !\n",
      "0-14 years % data extraction complete for  Germany !\n",
      "15-24 years % data extraction complete for  Germany !\n",
      "25-54 years % data extraction complete for  Germany !\n",
      "55-64 years % data extraction complete for  Germany !\n",
      "65 years and beyond % data extraction complete for  Germany !\n",
      "0-14 years % data extraction complete for  Ghana !\n",
      "15-24 years % data extraction complete for  Ghana !\n",
      "25-54 years % data extraction complete for  Ghana !\n",
      "55-64 years % data extraction complete for  Ghana !\n",
      "65 years and beyond % data extraction complete for  Ghana !\n",
      "0-14 years % data extraction complete for  Gibraltar !\n",
      "15-24 years % data extraction complete for  Gibraltar !\n",
      "25-54 years % data extraction complete for  Gibraltar !\n",
      "55-64 years % data extraction complete for  Gibraltar !\n",
      "65 years and beyond % data extraction complete for  Gibraltar !\n",
      "0-14 years % data extraction complete for  Greece !\n",
      "15-24 years % data extraction complete for  Greece !\n",
      "25-54 years % data extraction complete for  Greece !\n",
      "55-64 years % data extraction complete for  Greece !\n",
      "65 years and beyond % data extraction complete for  Greece !\n",
      "0-14 years % data extraction complete for  Greenland !\n",
      "15-24 years % data extraction complete for  Greenland !\n",
      "25-54 years % data extraction complete for  Greenland !\n",
      "55-64 years % data extraction complete for  Greenland !\n",
      "65 years and beyond % data extraction complete for  Greenland !\n",
      "0-14 years % data extraction complete for  Grenada !\n",
      "15-24 years % data extraction complete for  Grenada !\n",
      "25-54 years % data extraction complete for  Grenada !\n",
      "55-64 years % data extraction complete for  Grenada !\n",
      "65 years and beyond % data extraction complete for  Grenada !\n",
      "0-14 years % data extraction complete for  Guam !\n",
      "15-24 years % data extraction complete for  Guam !\n",
      "25-54 years % data extraction complete for  Guam !\n",
      "55-64 years % data extraction complete for  Guam !\n",
      "65 years and beyond % data extraction complete for  Guam !\n",
      "0-14 years % data extraction complete for  Guatemala !\n",
      "15-24 years % data extraction complete for  Guatemala !\n",
      "25-54 years % data extraction complete for  Guatemala !\n",
      "55-64 years % data extraction complete for  Guatemala !\n",
      "65 years and beyond % data extraction complete for  Guatemala !\n",
      "0-14 years % data extraction complete for  Guernsey !\n",
      "15-24 years % data extraction complete for  Guernsey !\n",
      "25-54 years % data extraction complete for  Guernsey !\n",
      "55-64 years % data extraction complete for  Guernsey !\n",
      "65 years and beyond % data extraction complete for  Guernsey !\n",
      "0-14 years % data extraction complete for  Guinea !\n",
      "15-24 years % data extraction complete for  Guinea !\n",
      "25-54 years % data extraction complete for  Guinea !\n",
      "55-64 years % data extraction complete for  Guinea !\n",
      "65 years and beyond % data extraction complete for  Guinea !\n",
      "0-14 years % data extraction complete for  Guinea-Bissau !\n",
      "15-24 years % data extraction complete for  Guinea-Bissau !\n",
      "25-54 years % data extraction complete for  Guinea-Bissau !\n",
      "55-64 years % data extraction complete for  Guinea-Bissau !\n",
      "65 years and beyond % data extraction complete for  Guinea-Bissau !\n",
      "0-14 years % data extraction complete for  Guyana !\n",
      "15-24 years % data extraction complete for  Guyana !\n",
      "25-54 years % data extraction complete for  Guyana !\n",
      "55-64 years % data extraction complete for  Guyana !\n",
      "65 years and beyond % data extraction complete for  Guyana !\n",
      "0-14 years % data extraction complete for  Haiti !\n",
      "15-24 years % data extraction complete for  Haiti !\n",
      "25-54 years % data extraction complete for  Haiti !\n",
      "55-64 years % data extraction complete for  Haiti !\n",
      "65 years and beyond % data extraction complete for  Haiti !\n",
      "**0-14 years % data not found for  Heard Island and McDonald Islands !**\n",
      "**15-24 years % data not found for  Heard Island and McDonald Islands !**\n",
      "**25-54 years % data not found for  Heard Island and McDonald Islands !**\n",
      "**55-64 years % data not found for  Heard Island and McDonald Islands !**\n",
      "**65 years and beyond % data not found for  Heard Island and McDonald Islands !**\n",
      "**0-14 years % data not found for  Holy See (Vatican City) !**\n",
      "**15-24 years % data not found for  Holy See (Vatican City) !**\n",
      "**25-54 years % data not found for  Holy See (Vatican City) !**\n",
      "**55-64 years % data not found for  Holy See (Vatican City) !**\n",
      "**65 years and beyond % data not found for  Holy See (Vatican City) !**\n",
      "0-14 years % data extraction complete for  Honduras !\n",
      "15-24 years % data extraction complete for  Honduras !\n",
      "25-54 years % data extraction complete for  Honduras !\n",
      "55-64 years % data extraction complete for  Honduras !\n",
      "65 years and beyond % data extraction complete for  Honduras !\n",
      "0-14 years % data extraction complete for  Hong Kong !\n",
      "15-24 years % data extraction complete for  Hong Kong !\n",
      "25-54 years % data extraction complete for  Hong Kong !\n",
      "55-64 years % data extraction complete for  Hong Kong !\n",
      "65 years and beyond % data extraction complete for  Hong Kong !\n",
      "**0-14 years % data not found for  Howland Island !**\n",
      "**15-24 years % data not found for  Howland Island !**\n",
      "**25-54 years % data not found for  Howland Island !**\n",
      "**55-64 years % data not found for  Howland Island !**\n",
      "**65 years and beyond % data not found for  Howland Island !**\n",
      "0-14 years % data extraction complete for  Hungary !\n",
      "15-24 years % data extraction complete for  Hungary !\n",
      "25-54 years % data extraction complete for  Hungary !\n",
      "55-64 years % data extraction complete for  Hungary !\n",
      "65 years and beyond % data extraction complete for  Hungary !\n",
      "0-14 years % data extraction complete for  Iceland !\n",
      "15-24 years % data extraction complete for  Iceland !\n",
      "25-54 years % data extraction complete for  Iceland !\n",
      "55-64 years % data extraction complete for  Iceland !\n",
      "65 years and beyond % data extraction complete for  Iceland !\n",
      "0-14 years % data extraction complete for  India !\n",
      "15-24 years % data extraction complete for  India !\n",
      "25-54 years % data extraction complete for  India !\n",
      "55-64 years % data extraction complete for  India !\n",
      "65 years and beyond % data extraction complete for  India !\n",
      "**0-14 years % data not found for  Indian Ocean !**\n",
      "**15-24 years % data not found for  Indian Ocean !**\n",
      "**25-54 years % data not found for  Indian Ocean !**\n",
      "**55-64 years % data not found for  Indian Ocean !**\n",
      "**65 years and beyond % data not found for  Indian Ocean !**\n",
      "0-14 years % data extraction complete for  Indonesia !\n",
      "15-24 years % data extraction complete for  Indonesia !\n",
      "25-54 years % data extraction complete for  Indonesia !\n",
      "55-64 years % data extraction complete for  Indonesia !\n",
      "65 years and beyond % data extraction complete for  Indonesia !\n",
      "0-14 years % data extraction complete for  Iran !\n",
      "15-24 years % data extraction complete for  Iran !\n",
      "25-54 years % data extraction complete for  Iran !\n",
      "55-64 years % data extraction complete for  Iran !\n",
      "65 years and beyond % data extraction complete for  Iran !\n",
      "0-14 years % data extraction complete for  Iraq !\n",
      "15-24 years % data extraction complete for  Iraq !\n",
      "25-54 years % data extraction complete for  Iraq !\n",
      "55-64 years % data extraction complete for  Iraq !\n",
      "65 years and beyond % data extraction complete for  Iraq !\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0-14 years % data extraction complete for  Ireland !\n",
      "15-24 years % data extraction complete for  Ireland !\n",
      "25-54 years % data extraction complete for  Ireland !\n",
      "55-64 years % data extraction complete for  Ireland !\n",
      "65 years and beyond % data extraction complete for  Ireland !\n",
      "0-14 years % data extraction complete for  Isle of Man !\n",
      "15-24 years % data extraction complete for  Isle of Man !\n",
      "25-54 years % data extraction complete for  Isle of Man !\n",
      "55-64 years % data extraction complete for  Isle of Man !\n",
      "65 years and beyond % data extraction complete for  Isle of Man !\n",
      "0-14 years % data extraction complete for  Israel !\n",
      "15-24 years % data extraction complete for  Israel !\n",
      "25-54 years % data extraction complete for  Israel !\n",
      "55-64 years % data extraction complete for  Israel !\n",
      "65 years and beyond % data extraction complete for  Israel !\n",
      "0-14 years % data extraction complete for  Italy !\n",
      "15-24 years % data extraction complete for  Italy !\n",
      "25-54 years % data extraction complete for  Italy !\n",
      "55-64 years % data extraction complete for  Italy !\n",
      "65 years and beyond % data extraction complete for  Italy !\n",
      "0-14 years % data extraction complete for  Jamaica !\n",
      "15-24 years % data extraction complete for  Jamaica !\n",
      "25-54 years % data extraction complete for  Jamaica !\n",
      "55-64 years % data extraction complete for  Jamaica !\n",
      "65 years and beyond % data extraction complete for  Jamaica !\n",
      "**0-14 years % data not found for  Jan Mayen !**\n",
      "**15-24 years % data not found for  Jan Mayen !**\n",
      "**25-54 years % data not found for  Jan Mayen !**\n",
      "**55-64 years % data not found for  Jan Mayen !**\n",
      "**65 years and beyond % data not found for  Jan Mayen !**\n",
      "0-14 years % data extraction complete for  Japan !\n",
      "15-24 years % data extraction complete for  Japan !\n",
      "25-54 years % data extraction complete for  Japan !\n",
      "55-64 years % data extraction complete for  Japan !\n",
      "65 years and beyond % data extraction complete for  Japan !\n",
      "**0-14 years % data not found for  Jarvis Island !**\n",
      "**15-24 years % data not found for  Jarvis Island !**\n",
      "**25-54 years % data not found for  Jarvis Island !**\n",
      "**55-64 years % data not found for  Jarvis Island !**\n",
      "**65 years and beyond % data not found for  Jarvis Island !**\n",
      "0-14 years % data extraction complete for  Jersey !\n",
      "15-24 years % data extraction complete for  Jersey !\n",
      "25-54 years % data extraction complete for  Jersey !\n",
      "55-64 years % data extraction complete for  Jersey !\n",
      "65 years and beyond % data extraction complete for  Jersey !\n",
      "**0-14 years % data not found for  Johnston Atoll !**\n",
      "**15-24 years % data not found for  Johnston Atoll !**\n",
      "**25-54 years % data not found for  Johnston Atoll !**\n",
      "**55-64 years % data not found for  Johnston Atoll !**\n",
      "**65 years and beyond % data not found for  Johnston Atoll !**\n",
      "0-14 years % data extraction complete for  Jordan !\n",
      "15-24 years % data extraction complete for  Jordan !\n",
      "25-54 years % data extraction complete for  Jordan !\n",
      "55-64 years % data extraction complete for  Jordan !\n",
      "65 years and beyond % data extraction complete for  Jordan !\n",
      "0-14 years % data extraction complete for  Kazakhstan !\n",
      "15-24 years % data extraction complete for  Kazakhstan !\n",
      "25-54 years % data extraction complete for  Kazakhstan !\n",
      "55-64 years % data extraction complete for  Kazakhstan !\n",
      "65 years and beyond % data extraction complete for  Kazakhstan !\n",
      "0-14 years % data extraction complete for  Kenya !\n",
      "15-24 years % data extraction complete for  Kenya !\n",
      "25-54 years % data extraction complete for  Kenya !\n",
      "55-64 years % data extraction complete for  Kenya !\n",
      "65 years and beyond % data extraction complete for  Kenya !\n",
      "**0-14 years % data not found for  Kingman Reef !**\n",
      "**15-24 years % data not found for  Kingman Reef !**\n",
      "**25-54 years % data not found for  Kingman Reef !**\n",
      "**55-64 years % data not found for  Kingman Reef !**\n",
      "**65 years and beyond % data not found for  Kingman Reef !**\n",
      "0-14 years % data extraction complete for  Kiribati !\n",
      "15-24 years % data extraction complete for  Kiribati !\n",
      "25-54 years % data extraction complete for  Kiribati !\n",
      "55-64 years % data extraction complete for  Kiribati !\n",
      "65 years and beyond % data extraction complete for  Kiribati !\n",
      "0-14 years % data extraction complete for  Korea, North !\n",
      "15-24 years % data extraction complete for  Korea, North !\n",
      "25-54 years % data extraction complete for  Korea, North !\n",
      "55-64 years % data extraction complete for  Korea, North !\n",
      "65 years and beyond % data extraction complete for  Korea, North !\n",
      "0-14 years % data extraction complete for  Korea, South !\n",
      "15-24 years % data extraction complete for  Korea, South !\n",
      "25-54 years % data extraction complete for  Korea, South !\n",
      "55-64 years % data extraction complete for  Korea, South !\n",
      "65 years and beyond % data extraction complete for  Korea, South !\n",
      "0-14 years % data extraction complete for  Kosovo !\n",
      "15-24 years % data extraction complete for  Kosovo !\n",
      "25-54 years % data extraction complete for  Kosovo !\n",
      "55-64 years % data extraction complete for  Kosovo !\n",
      "65 years and beyond % data extraction complete for  Kosovo !\n",
      "0-14 years % data extraction complete for  Kuwait !\n",
      "15-24 years % data extraction complete for  Kuwait !\n",
      "25-54 years % data extraction complete for  Kuwait !\n",
      "55-64 years % data extraction complete for  Kuwait !\n",
      "65 years and beyond % data extraction complete for  Kuwait !\n",
      "0-14 years % data extraction complete for  Kyrgyzstan !\n",
      "15-24 years % data extraction complete for  Kyrgyzstan !\n",
      "25-54 years % data extraction complete for  Kyrgyzstan !\n",
      "55-64 years % data extraction complete for  Kyrgyzstan !\n",
      "65 years and beyond % data extraction complete for  Kyrgyzstan !\n",
      "0-14 years % data extraction complete for  Laos !\n",
      "15-24 years % data extraction complete for  Laos !\n",
      "25-54 years % data extraction complete for  Laos !\n",
      "55-64 years % data extraction complete for  Laos !\n",
      "65 years and beyond % data extraction complete for  Laos !\n",
      "0-14 years % data extraction complete for  Latvia !\n",
      "15-24 years % data extraction complete for  Latvia !\n",
      "25-54 years % data extraction complete for  Latvia !\n",
      "55-64 years % data extraction complete for  Latvia !\n",
      "65 years and beyond % data extraction complete for  Latvia !\n",
      "0-14 years % data extraction complete for  Lebanon !\n",
      "15-24 years % data extraction complete for  Lebanon !\n",
      "25-54 years % data extraction complete for  Lebanon !\n",
      "55-64 years % data extraction complete for  Lebanon !\n",
      "65 years and beyond % data extraction complete for  Lebanon !\n",
      "0-14 years % data extraction complete for  Lesotho !\n",
      "15-24 years % data extraction complete for  Lesotho !\n",
      "25-54 years % data extraction complete for  Lesotho !\n",
      "55-64 years % data extraction complete for  Lesotho !\n",
      "65 years and beyond % data extraction complete for  Lesotho !\n",
      "0-14 years % data extraction complete for  Liberia !\n",
      "15-24 years % data extraction complete for  Liberia !\n",
      "25-54 years % data extraction complete for  Liberia !\n",
      "55-64 years % data extraction complete for  Liberia !\n",
      "65 years and beyond % data extraction complete for  Liberia !\n",
      "0-14 years % data extraction complete for  Libya !\n",
      "15-24 years % data extraction complete for  Libya !\n",
      "25-54 years % data extraction complete for  Libya !\n",
      "55-64 years % data extraction complete for  Libya !\n",
      "65 years and beyond % data extraction complete for  Libya !\n",
      "0-14 years % data extraction complete for  Liechtenstein !\n",
      "15-24 years % data extraction complete for  Liechtenstein !\n",
      "25-54 years % data extraction complete for  Liechtenstein !\n",
      "55-64 years % data extraction complete for  Liechtenstein !\n",
      "65 years and beyond % data extraction complete for  Liechtenstein !\n",
      "0-14 years % data extraction complete for  Lithuania !\n",
      "15-24 years % data extraction complete for  Lithuania !\n",
      "25-54 years % data extraction complete for  Lithuania !\n",
      "55-64 years % data extraction complete for  Lithuania !\n",
      "65 years and beyond % data extraction complete for  Lithuania !\n",
      "0-14 years % data extraction complete for  Luxembourg !\n",
      "15-24 years % data extraction complete for  Luxembourg !\n",
      "25-54 years % data extraction complete for  Luxembourg !\n",
      "55-64 years % data extraction complete for  Luxembourg !\n",
      "65 years and beyond % data extraction complete for  Luxembourg !\n",
      "0-14 years % data extraction complete for  Macau !\n",
      "15-24 years % data extraction complete for  Macau !\n",
      "25-54 years % data extraction complete for  Macau !\n",
      "55-64 years % data extraction complete for  Macau !\n",
      "65 years and beyond % data extraction complete for  Macau !\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0-14 years % data extraction complete for  Macedonia !\n",
      "15-24 years % data extraction complete for  Macedonia !\n",
      "25-54 years % data extraction complete for  Macedonia !\n",
      "55-64 years % data extraction complete for  Macedonia !\n",
      "65 years and beyond % data extraction complete for  Macedonia !\n",
      "0-14 years % data extraction complete for  Madagascar !\n",
      "15-24 years % data extraction complete for  Madagascar !\n",
      "25-54 years % data extraction complete for  Madagascar !\n",
      "55-64 years % data extraction complete for  Madagascar !\n",
      "65 years and beyond % data extraction complete for  Madagascar !\n",
      "0-14 years % data extraction complete for  Malawi !\n",
      "15-24 years % data extraction complete for  Malawi !\n",
      "25-54 years % data extraction complete for  Malawi !\n",
      "55-64 years % data extraction complete for  Malawi !\n",
      "65 years and beyond % data extraction complete for  Malawi !\n",
      "0-14 years % data extraction complete for  Malaysia !\n",
      "15-24 years % data extraction complete for  Malaysia !\n",
      "25-54 years % data extraction complete for  Malaysia !\n",
      "55-64 years % data extraction complete for  Malaysia !\n",
      "65 years and beyond % data extraction complete for  Malaysia !\n",
      "0-14 years % data extraction complete for  Maldives !\n",
      "15-24 years % data extraction complete for  Maldives !\n",
      "25-54 years % data extraction complete for  Maldives !\n",
      "55-64 years % data extraction complete for  Maldives !\n",
      "65 years and beyond % data extraction complete for  Maldives !\n",
      "0-14 years % data extraction complete for  Mali !\n",
      "15-24 years % data extraction complete for  Mali !\n",
      "25-54 years % data extraction complete for  Mali !\n",
      "55-64 years % data extraction complete for  Mali !\n",
      "65 years and beyond % data extraction complete for  Mali !\n",
      "0-14 years % data extraction complete for  Malta !\n",
      "15-24 years % data extraction complete for  Malta !\n",
      "25-54 years % data extraction complete for  Malta !\n",
      "55-64 years % data extraction complete for  Malta !\n",
      "65 years and beyond % data extraction complete for  Malta !\n",
      "0-14 years % data extraction complete for  Marshall Islands !\n",
      "15-24 years % data extraction complete for  Marshall Islands !\n",
      "25-54 years % data extraction complete for  Marshall Islands !\n",
      "55-64 years % data extraction complete for  Marshall Islands !\n",
      "65 years and beyond % data extraction complete for  Marshall Islands !\n",
      "0-14 years % data extraction complete for  Mauritania !\n",
      "15-24 years % data extraction complete for  Mauritania !\n",
      "25-54 years % data extraction complete for  Mauritania !\n",
      "55-64 years % data extraction complete for  Mauritania !\n",
      "65 years and beyond % data extraction complete for  Mauritania !\n",
      "0-14 years % data extraction complete for  Mauritius !\n",
      "15-24 years % data extraction complete for  Mauritius !\n",
      "25-54 years % data extraction complete for  Mauritius !\n",
      "55-64 years % data extraction complete for  Mauritius !\n",
      "65 years and beyond % data extraction complete for  Mauritius !\n",
      "0-14 years % data extraction complete for  Mexico !\n",
      "15-24 years % data extraction complete for  Mexico !\n",
      "25-54 years % data extraction complete for  Mexico !\n",
      "55-64 years % data extraction complete for  Mexico !\n",
      "65 years and beyond % data extraction complete for  Mexico !\n",
      "0-14 years % data extraction complete for  Micronesia, Federated States of !\n",
      "15-24 years % data extraction complete for  Micronesia, Federated States of !\n",
      "25-54 years % data extraction complete for  Micronesia, Federated States of !\n",
      "55-64 years % data extraction complete for  Micronesia, Federated States of !\n",
      "65 years and beyond % data extraction complete for  Micronesia, Federated States of !\n",
      "**0-14 years % data not found for  Midway Islands !**\n",
      "**15-24 years % data not found for  Midway Islands !**\n",
      "**25-54 years % data not found for  Midway Islands !**\n",
      "**55-64 years % data not found for  Midway Islands !**\n",
      "**65 years and beyond % data not found for  Midway Islands !**\n",
      "0-14 years % data extraction complete for  Moldova !\n",
      "15-24 years % data extraction complete for  Moldova !\n",
      "25-54 years % data extraction complete for  Moldova !\n",
      "55-64 years % data extraction complete for  Moldova !\n",
      "65 years and beyond % data extraction complete for  Moldova !\n",
      "0-14 years % data extraction complete for  Monaco !\n",
      "15-24 years % data extraction complete for  Monaco !\n",
      "25-54 years % data extraction complete for  Monaco !\n",
      "55-64 years % data extraction complete for  Monaco !\n",
      "65 years and beyond % data extraction complete for  Monaco !\n",
      "0-14 years % data extraction complete for  Mongolia !\n",
      "15-24 years % data extraction complete for  Mongolia !\n",
      "25-54 years % data extraction complete for  Mongolia !\n",
      "55-64 years % data extraction complete for  Mongolia !\n",
      "65 years and beyond % data extraction complete for  Mongolia !\n",
      "0-14 years % data extraction complete for  Montenegro !\n",
      "15-24 years % data extraction complete for  Montenegro !\n",
      "25-54 years % data extraction complete for  Montenegro !\n",
      "55-64 years % data extraction complete for  Montenegro !\n",
      "65 years and beyond % data extraction complete for  Montenegro !\n",
      "0-14 years % data extraction complete for  Montserrat !\n",
      "15-24 years % data extraction complete for  Montserrat !\n",
      "25-54 years % data extraction complete for  Montserrat !\n",
      "55-64 years % data extraction complete for  Montserrat !\n",
      "65 years and beyond % data extraction complete for  Montserrat !\n",
      "0-14 years % data extraction complete for  Morocco !\n",
      "15-24 years % data extraction complete for  Morocco !\n",
      "25-54 years % data extraction complete for  Morocco !\n",
      "55-64 years % data extraction complete for  Morocco !\n",
      "65 years and beyond % data extraction complete for  Morocco !\n",
      "0-14 years % data extraction complete for  Mozambique !\n",
      "15-24 years % data extraction complete for  Mozambique !\n",
      "25-54 years % data extraction complete for  Mozambique !\n",
      "55-64 years % data extraction complete for  Mozambique !\n",
      "65 years and beyond % data extraction complete for  Mozambique !\n",
      "0-14 years % data extraction complete for  Namibia !\n",
      "15-24 years % data extraction complete for  Namibia !\n",
      "25-54 years % data extraction complete for  Namibia !\n",
      "55-64 years % data extraction complete for  Namibia !\n",
      "65 years and beyond % data extraction complete for  Namibia !\n",
      "0-14 years % data extraction complete for  Nauru !\n",
      "15-24 years % data extraction complete for  Nauru !\n",
      "25-54 years % data extraction complete for  Nauru !\n",
      "55-64 years % data extraction complete for  Nauru !\n",
      "65 years and beyond % data extraction complete for  Nauru !\n",
      "**0-14 years % data not found for  Navassa Island !**\n",
      "**15-24 years % data not found for  Navassa Island !**\n",
      "**25-54 years % data not found for  Navassa Island !**\n",
      "**55-64 years % data not found for  Navassa Island !**\n",
      "**65 years and beyond % data not found for  Navassa Island !**\n",
      "0-14 years % data extraction complete for  Nepal !\n",
      "15-24 years % data extraction complete for  Nepal !\n",
      "25-54 years % data extraction complete for  Nepal !\n",
      "55-64 years % data extraction complete for  Nepal !\n",
      "65 years and beyond % data extraction complete for  Nepal !\n",
      "0-14 years % data extraction complete for  Netherlands !\n",
      "15-24 years % data extraction complete for  Netherlands !\n",
      "25-54 years % data extraction complete for  Netherlands !\n",
      "55-64 years % data extraction complete for  Netherlands !\n",
      "65 years and beyond % data extraction complete for  Netherlands !\n",
      "0-14 years % data extraction complete for  New Caledonia !\n",
      "15-24 years % data extraction complete for  New Caledonia !\n",
      "25-54 years % data extraction complete for  New Caledonia !\n",
      "55-64 years % data extraction complete for  New Caledonia !\n",
      "65 years and beyond % data extraction complete for  New Caledonia !\n",
      "0-14 years % data extraction complete for  New Zealand !\n",
      "15-24 years % data extraction complete for  New Zealand !\n",
      "25-54 years % data extraction complete for  New Zealand !\n",
      "55-64 years % data extraction complete for  New Zealand !\n",
      "65 years and beyond % data extraction complete for  New Zealand !\n",
      "0-14 years % data extraction complete for  Nicaragua !\n",
      "15-24 years % data extraction complete for  Nicaragua !\n",
      "25-54 years % data extraction complete for  Nicaragua !\n",
      "55-64 years % data extraction complete for  Nicaragua !\n",
      "65 years and beyond % data extraction complete for  Nicaragua !\n",
      "0-14 years % data extraction complete for  Niger !\n",
      "15-24 years % data extraction complete for  Niger !\n",
      "25-54 years % data extraction complete for  Niger !\n",
      "55-64 years % data extraction complete for  Niger !\n",
      "65 years and beyond % data extraction complete for  Niger !\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0-14 years % data extraction complete for  Nigeria !\n",
      "15-24 years % data extraction complete for  Nigeria !\n",
      "25-54 years % data extraction complete for  Nigeria !\n",
      "55-64 years % data extraction complete for  Nigeria !\n",
      "65 years and beyond % data extraction complete for  Nigeria !\n",
      "**0-14 years % data not found for  Niue !**\n",
      "**15-24 years % data not found for  Niue !**\n",
      "**25-54 years % data not found for  Niue !**\n",
      "**55-64 years % data not found for  Niue !**\n",
      "**65 years and beyond % data not found for  Niue !**\n",
      "**0-14 years % data not found for  Norfolk Island !**\n",
      "**15-24 years % data not found for  Norfolk Island !**\n",
      "**25-54 years % data not found for  Norfolk Island !**\n",
      "**55-64 years % data not found for  Norfolk Island !**\n",
      "**65 years and beyond % data not found for  Norfolk Island !**\n",
      "0-14 years % data extraction complete for  Northern Mariana Islands !\n",
      "15-24 years % data extraction complete for  Northern Mariana Islands !\n",
      "25-54 years % data extraction complete for  Northern Mariana Islands !\n",
      "55-64 years % data extraction complete for  Northern Mariana Islands !\n",
      "65 years and beyond % data extraction complete for  Northern Mariana Islands !\n",
      "0-14 years % data extraction complete for  Norway !\n",
      "15-24 years % data extraction complete for  Norway !\n",
      "25-54 years % data extraction complete for  Norway !\n",
      "55-64 years % data extraction complete for  Norway !\n",
      "65 years and beyond % data extraction complete for  Norway !\n",
      "0-14 years % data extraction complete for  Oman !\n",
      "15-24 years % data extraction complete for  Oman !\n",
      "25-54 years % data extraction complete for  Oman !\n",
      "55-64 years % data extraction complete for  Oman !\n",
      "65 years and beyond % data extraction complete for  Oman !\n",
      "**0-14 years % data not found for  Pacific Ocean !**\n",
      "**15-24 years % data not found for  Pacific Ocean !**\n",
      "**25-54 years % data not found for  Pacific Ocean !**\n",
      "**55-64 years % data not found for  Pacific Ocean !**\n",
      "**65 years and beyond % data not found for  Pacific Ocean !**\n",
      "0-14 years % data extraction complete for  Pakistan !\n",
      "15-24 years % data extraction complete for  Pakistan !\n",
      "25-54 years % data extraction complete for  Pakistan !\n",
      "55-64 years % data extraction complete for  Pakistan !\n",
      "65 years and beyond % data extraction complete for  Pakistan !\n",
      "0-14 years % data extraction complete for  Palau !\n",
      "15-24 years % data extraction complete for  Palau !\n",
      "25-54 years % data extraction complete for  Palau !\n",
      "55-64 years % data extraction complete for  Palau !\n",
      "65 years and beyond % data extraction complete for  Palau !\n",
      "**0-14 years % data not found for  Palmyra Atoll !**\n",
      "**15-24 years % data not found for  Palmyra Atoll !**\n",
      "**25-54 years % data not found for  Palmyra Atoll !**\n",
      "**55-64 years % data not found for  Palmyra Atoll !**\n",
      "**65 years and beyond % data not found for  Palmyra Atoll !**\n",
      "0-14 years % data extraction complete for  Panama !\n",
      "15-24 years % data extraction complete for  Panama !\n",
      "25-54 years % data extraction complete for  Panama !\n",
      "55-64 years % data extraction complete for  Panama !\n",
      "65 years and beyond % data extraction complete for  Panama !\n",
      "0-14 years % data extraction complete for  Papua New Guinea !\n",
      "15-24 years % data extraction complete for  Papua New Guinea !\n",
      "25-54 years % data extraction complete for  Papua New Guinea !\n",
      "55-64 years % data extraction complete for  Papua New Guinea !\n",
      "65 years and beyond % data extraction complete for  Papua New Guinea !\n",
      "**0-14 years % data not found for  Paracel Islands !**\n",
      "**15-24 years % data not found for  Paracel Islands !**\n",
      "**25-54 years % data not found for  Paracel Islands !**\n",
      "**55-64 years % data not found for  Paracel Islands !**\n",
      "**65 years and beyond % data not found for  Paracel Islands !**\n",
      "0-14 years % data extraction complete for  Paraguay !\n",
      "15-24 years % data extraction complete for  Paraguay !\n",
      "25-54 years % data extraction complete for  Paraguay !\n",
      "55-64 years % data extraction complete for  Paraguay !\n",
      "65 years and beyond % data extraction complete for  Paraguay !\n",
      "0-14 years % data extraction complete for  Peru !\n",
      "15-24 years % data extraction complete for  Peru !\n",
      "25-54 years % data extraction complete for  Peru !\n",
      "55-64 years % data extraction complete for  Peru !\n",
      "65 years and beyond % data extraction complete for  Peru !\n",
      "0-14 years % data extraction complete for  Philippines !\n",
      "15-24 years % data extraction complete for  Philippines !\n",
      "25-54 years % data extraction complete for  Philippines !\n",
      "55-64 years % data extraction complete for  Philippines !\n",
      "65 years and beyond % data extraction complete for  Philippines !\n",
      "**0-14 years % data not found for  Pitcairn Islands !**\n",
      "**15-24 years % data not found for  Pitcairn Islands !**\n",
      "**25-54 years % data not found for  Pitcairn Islands !**\n",
      "**55-64 years % data not found for  Pitcairn Islands !**\n",
      "**65 years and beyond % data not found for  Pitcairn Islands !**\n",
      "0-14 years % data extraction complete for  Poland !\n",
      "15-24 years % data extraction complete for  Poland !\n",
      "25-54 years % data extraction complete for  Poland !\n",
      "55-64 years % data extraction complete for  Poland !\n",
      "65 years and beyond % data extraction complete for  Poland !\n",
      "0-14 years % data extraction complete for  Portugal !\n",
      "15-24 years % data extraction complete for  Portugal !\n",
      "25-54 years % data extraction complete for  Portugal !\n",
      "55-64 years % data extraction complete for  Portugal !\n",
      "65 years and beyond % data extraction complete for  Portugal !\n",
      "0-14 years % data extraction complete for  Puerto Rico !\n",
      "15-24 years % data extraction complete for  Puerto Rico !\n",
      "25-54 years % data extraction complete for  Puerto Rico !\n",
      "55-64 years % data extraction complete for  Puerto Rico !\n",
      "65 years and beyond % data extraction complete for  Puerto Rico !\n",
      "0-14 years % data extraction complete for  Qatar !\n",
      "15-24 years % data extraction complete for  Qatar !\n",
      "25-54 years % data extraction complete for  Qatar !\n",
      "55-64 years % data extraction complete for  Qatar !\n",
      "65 years and beyond % data extraction complete for  Qatar !\n",
      "0-14 years % data extraction complete for  Romania !\n",
      "15-24 years % data extraction complete for  Romania !\n",
      "25-54 years % data extraction complete for  Romania !\n",
      "55-64 years % data extraction complete for  Romania !\n",
      "65 years and beyond % data extraction complete for  Romania !\n",
      "0-14 years % data extraction complete for  Russia !\n",
      "15-24 years % data extraction complete for  Russia !\n",
      "25-54 years % data extraction complete for  Russia !\n",
      "55-64 years % data extraction complete for  Russia !\n",
      "65 years and beyond % data extraction complete for  Russia !\n",
      "0-14 years % data extraction complete for  Rwanda !\n",
      "15-24 years % data extraction complete for  Rwanda !\n",
      "25-54 years % data extraction complete for  Rwanda !\n",
      "55-64 years % data extraction complete for  Rwanda !\n",
      "65 years and beyond % data extraction complete for  Rwanda !\n",
      "0-14 years % data extraction complete for  Saint Barthelemy !\n",
      "15-24 years % data extraction complete for  Saint Barthelemy !\n",
      "25-54 years % data extraction complete for  Saint Barthelemy !\n",
      "55-64 years % data extraction complete for  Saint Barthelemy !\n",
      "65 years and beyond % data extraction complete for  Saint Barthelemy !\n",
      "0-14 years % data extraction complete for  Saint Helena, Ascension, and Tristan da Cunha !\n",
      "15-24 years % data extraction complete for  Saint Helena, Ascension, and Tristan da Cunha !\n",
      "25-54 years % data extraction complete for  Saint Helena, Ascension, and Tristan da Cunha !\n",
      "55-64 years % data extraction complete for  Saint Helena, Ascension, and Tristan da Cunha !\n",
      "65 years and beyond % data extraction complete for  Saint Helena, Ascension, and Tristan da Cunha !\n",
      "0-14 years % data extraction complete for  Saint Kitts and Nevis !\n",
      "15-24 years % data extraction complete for  Saint Kitts and Nevis !\n",
      "25-54 years % data extraction complete for  Saint Kitts and Nevis !\n",
      "55-64 years % data extraction complete for  Saint Kitts and Nevis !\n",
      "65 years and beyond % data extraction complete for  Saint Kitts and Nevis !\n",
      "0-14 years % data extraction complete for  Saint Lucia !\n",
      "15-24 years % data extraction complete for  Saint Lucia !\n",
      "25-54 years % data extraction complete for  Saint Lucia !\n",
      "55-64 years % data extraction complete for  Saint Lucia !\n",
      "65 years and beyond % data extraction complete for  Saint Lucia !\n",
      "0-14 years % data extraction complete for  Saint Martin !\n",
      "15-24 years % data extraction complete for  Saint Martin !\n",
      "25-54 years % data extraction complete for  Saint Martin !\n",
      "55-64 years % data extraction complete for  Saint Martin !\n",
      "65 years and beyond % data extraction complete for  Saint Martin !\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0-14 years % data extraction complete for  Saint Pierre and Miquelon !\n",
      "15-24 years % data extraction complete for  Saint Pierre and Miquelon !\n",
      "25-54 years % data extraction complete for  Saint Pierre and Miquelon !\n",
      "55-64 years % data extraction complete for  Saint Pierre and Miquelon !\n",
      "65 years and beyond % data extraction complete for  Saint Pierre and Miquelon !\n",
      "0-14 years % data extraction complete for  Saint Vincent and the Grenadines !\n",
      "15-24 years % data extraction complete for  Saint Vincent and the Grenadines !\n",
      "25-54 years % data extraction complete for  Saint Vincent and the Grenadines !\n",
      "55-64 years % data extraction complete for  Saint Vincent and the Grenadines !\n",
      "65 years and beyond % data extraction complete for  Saint Vincent and the Grenadines !\n",
      "0-14 years % data extraction complete for  Samoa !\n",
      "15-24 years % data extraction complete for  Samoa !\n",
      "25-54 years % data extraction complete for  Samoa !\n",
      "55-64 years % data extraction complete for  Samoa !\n",
      "65 years and beyond % data extraction complete for  Samoa !\n",
      "0-14 years % data extraction complete for  San Marino !\n",
      "15-24 years % data extraction complete for  San Marino !\n",
      "25-54 years % data extraction complete for  San Marino !\n",
      "55-64 years % data extraction complete for  San Marino !\n",
      "65 years and beyond % data extraction complete for  San Marino !\n",
      "0-14 years % data extraction complete for  Sao Tome and Principe !\n",
      "15-24 years % data extraction complete for  Sao Tome and Principe !\n",
      "25-54 years % data extraction complete for  Sao Tome and Principe !\n",
      "55-64 years % data extraction complete for  Sao Tome and Principe !\n",
      "65 years and beyond % data extraction complete for  Sao Tome and Principe !\n",
      "0-14 years % data extraction complete for  Saudi Arabia !\n",
      "15-24 years % data extraction complete for  Saudi Arabia !\n",
      "25-54 years % data extraction complete for  Saudi Arabia !\n",
      "55-64 years % data extraction complete for  Saudi Arabia !\n",
      "65 years and beyond % data extraction complete for  Saudi Arabia !\n",
      "0-14 years % data extraction complete for  Senegal !\n",
      "15-24 years % data extraction complete for  Senegal !\n",
      "25-54 years % data extraction complete for  Senegal !\n",
      "55-64 years % data extraction complete for  Senegal !\n",
      "65 years and beyond % data extraction complete for  Senegal !\n",
      "0-14 years % data extraction complete for  Serbia !\n",
      "15-24 years % data extraction complete for  Serbia !\n",
      "25-54 years % data extraction complete for  Serbia !\n",
      "55-64 years % data extraction complete for  Serbia !\n",
      "65 years and beyond % data extraction complete for  Serbia !\n",
      "0-14 years % data extraction complete for  Seychelles !\n",
      "15-24 years % data extraction complete for  Seychelles !\n",
      "25-54 years % data extraction complete for  Seychelles !\n",
      "55-64 years % data extraction complete for  Seychelles !\n",
      "65 years and beyond % data extraction complete for  Seychelles !\n",
      "0-14 years % data extraction complete for  Sierra Leone !\n",
      "15-24 years % data extraction complete for  Sierra Leone !\n",
      "25-54 years % data extraction complete for  Sierra Leone !\n",
      "55-64 years % data extraction complete for  Sierra Leone !\n",
      "65 years and beyond % data extraction complete for  Sierra Leone !\n",
      "0-14 years % data extraction complete for  Singapore !\n",
      "15-24 years % data extraction complete for  Singapore !\n",
      "25-54 years % data extraction complete for  Singapore !\n",
      "55-64 years % data extraction complete for  Singapore !\n",
      "65 years and beyond % data extraction complete for  Singapore !\n",
      "0-14 years % data extraction complete for  Sint Maarten !\n",
      "15-24 years % data extraction complete for  Sint Maarten !\n",
      "25-54 years % data extraction complete for  Sint Maarten !\n",
      "55-64 years % data extraction complete for  Sint Maarten !\n",
      "65 years and beyond % data extraction complete for  Sint Maarten !\n",
      "0-14 years % data extraction complete for  Slovakia !\n",
      "15-24 years % data extraction complete for  Slovakia !\n",
      "25-54 years % data extraction complete for  Slovakia !\n",
      "55-64 years % data extraction complete for  Slovakia !\n",
      "65 years and beyond % data extraction complete for  Slovakia !\n",
      "0-14 years % data extraction complete for  Slovenia !\n",
      "15-24 years % data extraction complete for  Slovenia !\n",
      "25-54 years % data extraction complete for  Slovenia !\n",
      "55-64 years % data extraction complete for  Slovenia !\n",
      "65 years and beyond % data extraction complete for  Slovenia !\n",
      "0-14 years % data extraction complete for  Solomon Islands !\n",
      "15-24 years % data extraction complete for  Solomon Islands !\n",
      "25-54 years % data extraction complete for  Solomon Islands !\n",
      "55-64 years % data extraction complete for  Solomon Islands !\n",
      "65 years and beyond % data extraction complete for  Solomon Islands !\n",
      "0-14 years % data extraction complete for  Somalia !\n",
      "15-24 years % data extraction complete for  Somalia !\n",
      "25-54 years % data extraction complete for  Somalia !\n",
      "55-64 years % data extraction complete for  Somalia !\n",
      "65 years and beyond % data extraction complete for  Somalia !\n",
      "0-14 years % data extraction complete for  South Africa !\n",
      "15-24 years % data extraction complete for  South Africa !\n",
      "25-54 years % data extraction complete for  South Africa !\n",
      "55-64 years % data extraction complete for  South Africa !\n",
      "65 years and beyond % data extraction complete for  South Africa !\n",
      "**0-14 years % data not found for  Southern Ocean !**\n",
      "**15-24 years % data not found for  Southern Ocean !**\n",
      "**25-54 years % data not found for  Southern Ocean !**\n",
      "**55-64 years % data not found for  Southern Ocean !**\n",
      "**65 years and beyond % data not found for  Southern Ocean !**\n",
      "**0-14 years % data not found for  South Georgia and South Sandwich Islands !**\n",
      "**15-24 years % data not found for  South Georgia and South Sandwich Islands !**\n",
      "**25-54 years % data not found for  South Georgia and South Sandwich Islands !**\n",
      "**55-64 years % data not found for  South Georgia and South Sandwich Islands !**\n",
      "**65 years and beyond % data not found for  South Georgia and South Sandwich Islands !**\n",
      "0-14 years % data extraction complete for  South Sudan !\n",
      "15-24 years % data extraction complete for  South Sudan !\n",
      "25-54 years % data extraction complete for  South Sudan !\n",
      "55-64 years % data extraction complete for  South Sudan !\n",
      "65 years and beyond % data extraction complete for  South Sudan !\n",
      "0-14 years % data extraction complete for  Spain !\n",
      "15-24 years % data extraction complete for  Spain !\n",
      "25-54 years % data extraction complete for  Spain !\n",
      "55-64 years % data extraction complete for  Spain !\n",
      "65 years and beyond % data extraction complete for  Spain !\n",
      "**0-14 years % data not found for  Spratly Islands !**\n",
      "**15-24 years % data not found for  Spratly Islands !**\n",
      "**25-54 years % data not found for  Spratly Islands !**\n",
      "**55-64 years % data not found for  Spratly Islands !**\n",
      "**65 years and beyond % data not found for  Spratly Islands !**\n",
      "0-14 years % data extraction complete for  Sri Lanka !\n",
      "15-24 years % data extraction complete for  Sri Lanka !\n",
      "25-54 years % data extraction complete for  Sri Lanka !\n",
      "55-64 years % data extraction complete for  Sri Lanka !\n",
      "65 years and beyond % data extraction complete for  Sri Lanka !\n",
      "0-14 years % data extraction complete for  Sudan !\n",
      "15-24 years % data extraction complete for  Sudan !\n",
      "25-54 years % data extraction complete for  Sudan !\n",
      "55-64 years % data extraction complete for  Sudan !\n",
      "65 years and beyond % data extraction complete for  Sudan !\n",
      "0-14 years % data extraction complete for  Suriname !\n",
      "15-24 years % data extraction complete for  Suriname !\n",
      "25-54 years % data extraction complete for  Suriname !\n",
      "55-64 years % data extraction complete for  Suriname !\n",
      "65 years and beyond % data extraction complete for  Suriname !\n",
      "**0-14 years % data not found for  Svalbard !**\n",
      "**15-24 years % data not found for  Svalbard !**\n",
      "**25-54 years % data not found for  Svalbard !**\n",
      "**55-64 years % data not found for  Svalbard !**\n",
      "**65 years and beyond % data not found for  Svalbard !**\n",
      "0-14 years % data extraction complete for  Swaziland !\n",
      "15-24 years % data extraction complete for  Swaziland !\n",
      "25-54 years % data extraction complete for  Swaziland !\n",
      "55-64 years % data extraction complete for  Swaziland !\n",
      "65 years and beyond % data extraction complete for  Swaziland !\n",
      "0-14 years % data extraction complete for  Sweden !\n",
      "15-24 years % data extraction complete for  Sweden !\n",
      "25-54 years % data extraction complete for  Sweden !\n",
      "55-64 years % data extraction complete for  Sweden !\n",
      "65 years and beyond % data extraction complete for  Sweden !\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0-14 years % data extraction complete for  Switzerland !\n",
      "15-24 years % data extraction complete for  Switzerland !\n",
      "25-54 years % data extraction complete for  Switzerland !\n",
      "55-64 years % data extraction complete for  Switzerland !\n",
      "65 years and beyond % data extraction complete for  Switzerland !\n",
      "0-14 years % data extraction complete for  Syria !\n",
      "15-24 years % data extraction complete for  Syria !\n",
      "25-54 years % data extraction complete for  Syria !\n",
      "55-64 years % data extraction complete for  Syria !\n",
      "65 years and beyond % data extraction complete for  Syria !\n",
      "0-14 years % data extraction complete for  Taiwan !\n",
      "15-24 years % data extraction complete for  Taiwan !\n",
      "25-54 years % data extraction complete for  Taiwan !\n",
      "55-64 years % data extraction complete for  Taiwan !\n",
      "65 years and beyond % data extraction complete for  Taiwan !\n",
      "0-14 years % data extraction complete for  Tajikistan !\n",
      "15-24 years % data extraction complete for  Tajikistan !\n",
      "25-54 years % data extraction complete for  Tajikistan !\n",
      "55-64 years % data extraction complete for  Tajikistan !\n",
      "65 years and beyond % data extraction complete for  Tajikistan !\n",
      "0-14 years % data extraction complete for  Tanzania !\n",
      "15-24 years % data extraction complete for  Tanzania !\n",
      "25-54 years % data extraction complete for  Tanzania !\n",
      "55-64 years % data extraction complete for  Tanzania !\n",
      "65 years and beyond % data extraction complete for  Tanzania !\n",
      "0-14 years % data extraction complete for  Thailand !\n",
      "15-24 years % data extraction complete for  Thailand !\n",
      "25-54 years % data extraction complete for  Thailand !\n",
      "55-64 years % data extraction complete for  Thailand !\n",
      "65 years and beyond % data extraction complete for  Thailand !\n",
      "0-14 years % data extraction complete for  Timor-Leste !\n",
      "15-24 years % data extraction complete for  Timor-Leste !\n",
      "25-54 years % data extraction complete for  Timor-Leste !\n",
      "55-64 years % data extraction complete for  Timor-Leste !\n",
      "65 years and beyond % data extraction complete for  Timor-Leste !\n",
      "0-14 years % data extraction complete for  Togo !\n",
      "15-24 years % data extraction complete for  Togo !\n",
      "25-54 years % data extraction complete for  Togo !\n",
      "55-64 years % data extraction complete for  Togo !\n",
      "65 years and beyond % data extraction complete for  Togo !\n",
      "**0-14 years % data not found for  Tokelau !**\n",
      "**15-24 years % data not found for  Tokelau !**\n",
      "**25-54 years % data not found for  Tokelau !**\n",
      "**55-64 years % data not found for  Tokelau !**\n",
      "**65 years and beyond % data not found for  Tokelau !**\n",
      "0-14 years % data extraction complete for  Tonga !\n",
      "15-24 years % data extraction complete for  Tonga !\n",
      "25-54 years % data extraction complete for  Tonga !\n",
      "55-64 years % data extraction complete for  Tonga !\n",
      "65 years and beyond % data extraction complete for  Tonga !\n",
      "0-14 years % data extraction complete for  Trinidad and Tobago !\n",
      "15-24 years % data extraction complete for  Trinidad and Tobago !\n",
      "25-54 years % data extraction complete for  Trinidad and Tobago !\n",
      "55-64 years % data extraction complete for  Trinidad and Tobago !\n",
      "65 years and beyond % data extraction complete for  Trinidad and Tobago !\n",
      "0-14 years % data extraction complete for  Tunisia !\n",
      "15-24 years % data extraction complete for  Tunisia !\n",
      "25-54 years % data extraction complete for  Tunisia !\n",
      "55-64 years % data extraction complete for  Tunisia !\n",
      "65 years and beyond % data extraction complete for  Tunisia !\n",
      "0-14 years % data extraction complete for  Turkey !\n",
      "15-24 years % data extraction complete for  Turkey !\n",
      "25-54 years % data extraction complete for  Turkey !\n",
      "55-64 years % data extraction complete for  Turkey !\n",
      "65 years and beyond % data extraction complete for  Turkey !\n",
      "0-14 years % data extraction complete for  Turkmenistan !\n",
      "15-24 years % data extraction complete for  Turkmenistan !\n",
      "25-54 years % data extraction complete for  Turkmenistan !\n",
      "55-64 years % data extraction complete for  Turkmenistan !\n",
      "65 years and beyond % data extraction complete for  Turkmenistan !\n",
      "0-14 years % data extraction complete for  Turks and Caicos Islands !\n",
      "15-24 years % data extraction complete for  Turks and Caicos Islands !\n",
      "25-54 years % data extraction complete for  Turks and Caicos Islands !\n",
      "55-64 years % data extraction complete for  Turks and Caicos Islands !\n",
      "65 years and beyond % data extraction complete for  Turks and Caicos Islands !\n",
      "0-14 years % data extraction complete for  Tuvalu !\n",
      "15-24 years % data extraction complete for  Tuvalu !\n",
      "25-54 years % data extraction complete for  Tuvalu !\n",
      "55-64 years % data extraction complete for  Tuvalu !\n",
      "65 years and beyond % data extraction complete for  Tuvalu !\n",
      "0-14 years % data extraction complete for  Uganda !\n",
      "15-24 years % data extraction complete for  Uganda !\n",
      "25-54 years % data extraction complete for  Uganda !\n",
      "55-64 years % data extraction complete for  Uganda !\n",
      "65 years and beyond % data extraction complete for  Uganda !\n",
      "0-14 years % data extraction complete for  Ukraine !\n",
      "15-24 years % data extraction complete for  Ukraine !\n",
      "25-54 years % data extraction complete for  Ukraine !\n",
      "55-64 years % data extraction complete for  Ukraine !\n",
      "65 years and beyond % data extraction complete for  Ukraine !\n",
      "0-14 years % data extraction complete for  United Arab Emirates !\n",
      "15-24 years % data extraction complete for  United Arab Emirates !\n",
      "25-54 years % data extraction complete for  United Arab Emirates !\n",
      "55-64 years % data extraction complete for  United Arab Emirates !\n",
      "65 years and beyond % data extraction complete for  United Arab Emirates !\n",
      "0-14 years % data extraction complete for  United Kingdom !\n",
      "15-24 years % data extraction complete for  United Kingdom !\n",
      "25-54 years % data extraction complete for  United Kingdom !\n",
      "55-64 years % data extraction complete for  United Kingdom !\n",
      "65 years and beyond % data extraction complete for  United Kingdom !\n",
      "0-14 years % data extraction complete for  United States !\n",
      "15-24 years % data extraction complete for  United States !\n",
      "25-54 years % data extraction complete for  United States !\n",
      "55-64 years % data extraction complete for  United States !\n",
      "65 years and beyond % data extraction complete for  United States !\n",
      "**0-14 years % data not found for  United States Pacific Island Wildlife Refuges !**\n",
      "**15-24 years % data not found for  United States Pacific Island Wildlife Refuges !**\n",
      "**25-54 years % data not found for  United States Pacific Island Wildlife Refuges !**\n",
      "**55-64 years % data not found for  United States Pacific Island Wildlife Refuges !**\n",
      "**65 years and beyond % data not found for  United States Pacific Island Wildlife Refuges !**\n",
      "0-14 years % data extraction complete for  Uruguay !\n",
      "15-24 years % data extraction complete for  Uruguay !\n",
      "25-54 years % data extraction complete for  Uruguay !\n",
      "55-64 years % data extraction complete for  Uruguay !\n",
      "65 years and beyond % data extraction complete for  Uruguay !\n",
      "0-14 years % data extraction complete for  Uzbekistan !\n",
      "15-24 years % data extraction complete for  Uzbekistan !\n",
      "25-54 years % data extraction complete for  Uzbekistan !\n",
      "55-64 years % data extraction complete for  Uzbekistan !\n",
      "65 years and beyond % data extraction complete for  Uzbekistan !\n",
      "0-14 years % data extraction complete for  Vanuatu !\n",
      "15-24 years % data extraction complete for  Vanuatu !\n",
      "25-54 years % data extraction complete for  Vanuatu !\n",
      "55-64 years % data extraction complete for  Vanuatu !\n",
      "65 years and beyond % data extraction complete for  Vanuatu !\n",
      "0-14 years % data extraction complete for  Venezuela !\n",
      "15-24 years % data extraction complete for  Venezuela !\n",
      "25-54 years % data extraction complete for  Venezuela !\n",
      "55-64 years % data extraction complete for  Venezuela !\n",
      "65 years and beyond % data extraction complete for  Venezuela !\n",
      "0-14 years % data extraction complete for  Vietnam !\n",
      "15-24 years % data extraction complete for  Vietnam !\n",
      "25-54 years % data extraction complete for  Vietnam !\n",
      "55-64 years % data extraction complete for  Vietnam !\n",
      "65 years and beyond % data extraction complete for  Vietnam !\n",
      "0-14 years % data extraction complete for  Virgin Islands !\n",
      "15-24 years % data extraction complete for  Virgin Islands !\n",
      "25-54 years % data extraction complete for  Virgin Islands !\n",
      "55-64 years % data extraction complete for  Virgin Islands !\n",
      "65 years and beyond % data extraction complete for  Virgin Islands !\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "**0-14 years % data not found for  Wake Island !**\n",
      "**15-24 years % data not found for  Wake Island !**\n",
      "**25-54 years % data not found for  Wake Island !**\n",
      "**55-64 years % data not found for  Wake Island !**\n",
      "**65 years and beyond % data not found for  Wake Island !**\n",
      "0-14 years % data extraction complete for  Wallis and Futuna !\n",
      "15-24 years % data extraction complete for  Wallis and Futuna !\n",
      "25-54 years % data extraction complete for  Wallis and Futuna !\n",
      "55-64 years % data extraction complete for  Wallis and Futuna !\n",
      "65 years and beyond % data extraction complete for  Wallis and Futuna !\n",
      "0-14 years % data extraction complete for  West Bank !\n",
      "15-24 years % data extraction complete for  West Bank !\n",
      "25-54 years % data extraction complete for  West Bank !\n",
      "55-64 years % data extraction complete for  West Bank !\n",
      "65 years and beyond % data extraction complete for  West Bank !\n",
      "0-14 years % data extraction complete for  Western Sahara !\n",
      "15-24 years % data extraction complete for  Western Sahara !\n",
      "25-54 years % data extraction complete for  Western Sahara !\n",
      "55-64 years % data extraction complete for  Western Sahara !\n",
      "65 years and beyond % data extraction complete for  Western Sahara !\n",
      "0-14 years % data extraction complete for  Yemen !\n",
      "15-24 years % data extraction complete for  Yemen !\n",
      "25-54 years % data extraction complete for  Yemen !\n",
      "55-64 years % data extraction complete for  Yemen !\n",
      "65 years and beyond % data extraction complete for  Yemen !\n",
      "0-14 years % data extraction complete for  Zambia !\n",
      "15-24 years % data extraction complete for  Zambia !\n",
      "25-54 years % data extraction complete for  Zambia !\n",
      "55-64 years % data extraction complete for  Zambia !\n",
      "65 years and beyond % data extraction complete for  Zambia !\n",
      "0-14 years % data extraction complete for  Zimbabwe !\n",
      "15-24 years % data extraction complete for  Zimbabwe !\n",
      "25-54 years % data extraction complete for  Zimbabwe !\n",
      "55-64 years % data extraction complete for  Zimbabwe !\n",
      "65 years and beyond % data extraction complete for  Zimbabwe !\n"
     ]
    }
   ],
   "source": [
    "# Base URL\n",
    "urlbase = 'https://www.cia.gov/library/publications/the-world-factbook/geos/'\n",
    "demographics1=[]\n",
    "demographics2=[]\n",
    "demographics3=[]\n",
    "demographics4=[]\n",
    "demographics5=[]\n",
    "\n",
    "offset = len('65 years and over: ')\n",
    "\n",
    "# Iterate over every country\n",
    "for i in range(1,len(country_names)-1):\n",
    "    country_html=country_codes[i]+'.html'\n",
    "    url_to_get=urlbase+country_html\n",
    "    # Read the HTML from the URL and pass on to BeautifulSoup\n",
    "    html = urllib.request.urlopen(url_to_get, context=ctx).read()\n",
    "    soup = BeautifulSoup(html, 'html.parser')\n",
    "    \n",
    "    txt=soup.get_text()\n",
    "    pos1=txt.find('0-14 years: ')\n",
    "    pos2=txt.find('15-24 years: ')\n",
    "    pos3=txt.find('25-54 years: ')\n",
    "    pos4=txt.find('55-64 years: ')\n",
    "    pos5=txt.find('65 years and over: ')\n",
    "    \n",
    "    if pos1==-1:\n",
    "        print(f\"**0-14 years % data not found for {country_names[i]}!**\")\n",
    "        demographics1.append(np.nan)\n",
    "    else:\n",
    "        text=txt[pos1+12:pos1+18]\n",
    "        end=re.search('%',text).start()\n",
    "        a=float((txt[pos1+12:pos1+12+end]))\n",
    "        demographics1.append(a)\n",
    "        print(f\"0-14 years % data extraction complete for {country_names[i]}!\")\n",
    "    \n",
    "    if pos2==-1:\n",
    "        print(f\"**15-24 years % data not found for {country_names[i]}!**\")\n",
    "        demographics2.append(np.nan)\n",
    "    else:\n",
    "        text=txt[pos2+13:pos2+19]\n",
    "        end=re.search('%',text).start()\n",
    "        a=float((txt[pos2+13:pos2+13+end]))\n",
    "        demographics2.append(a)\n",
    "        print(f\"15-24 years % data extraction complete for {country_names[i]}!\")\n",
    "        \n",
    "    if pos3==-1:\n",
    "        print(f\"**25-54 years % data not found for {country_names[i]}!**\")\n",
    "        demographics3.append(np.nan)\n",
    "    else:\n",
    "        text=txt[pos3+13:pos3+19]\n",
    "        end=re.search('%',text).start()\n",
    "        a=float((txt[pos3+13:pos3+13+end]))\n",
    "        demographics3.append(a)\n",
    "        print(f\"25-54 years % data extraction complete for {country_names[i]}!\")\n",
    "    \n",
    "    if pos4==-1:\n",
    "        print(f\"**55-64 years % data not found for {country_names[i]}!**\")\n",
    "        demographics4.append(np.nan)\n",
    "    else:\n",
    "        text=txt[pos4+13:pos4+19]\n",
    "        end=re.search('%',text).start()\n",
    "        a=float((txt[pos4+13:pos4+13+end]))\n",
    "        demographics4.append(a)\n",
    "        print(f\"55-64 years % data extraction complete for {country_names[i]}!\")\n",
    "    \n",
    "    \n",
    "    if pos5==-1:\n",
    "        print(f\"**65 years and beyond % data not found for {country_names[i]}!**\")\n",
    "        demographics5.append(np.nan)\n",
    "    else:\n",
    "        text=txt[pos5+offset:pos5+offset+6]\n",
    "        end=re.search('%',text).start()\n",
    "        a=float((txt[pos5+offset:pos5+offset+end]))\n",
    "        demographics5.append(a)\n",
    "        print(f\"65 years and beyond % data extraction complete for {country_names[i]}!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0-14 years old %</th>\n",
       "      <th>15-24 years %</th>\n",
       "      <th>25-54 years %</th>\n",
       "      <th>55-64 years %</th>\n",
       "      <th>65 years and above %</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COUNTRY</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Afghanistan</th>\n",
       "      <td>40.92</td>\n",
       "      <td>22.22</td>\n",
       "      <td>30.35</td>\n",
       "      <td>3.92</td>\n",
       "      <td>2.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>18.05</td>\n",
       "      <td>17.47</td>\n",
       "      <td>41.06</td>\n",
       "      <td>11.54</td>\n",
       "      <td>11.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>29.31</td>\n",
       "      <td>15.30</td>\n",
       "      <td>42.93</td>\n",
       "      <td>6.81</td>\n",
       "      <td>5.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>American Samoa</th>\n",
       "      <td>30.28</td>\n",
       "      <td>18.79</td>\n",
       "      <td>36.38</td>\n",
       "      <td>8.58</td>\n",
       "      <td>5.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andorra</th>\n",
       "      <td>14.40</td>\n",
       "      <td>9.64</td>\n",
       "      <td>46.18</td>\n",
       "      <td>14.16</td>\n",
       "      <td>15.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Angola</th>\n",
       "      <td>48.12</td>\n",
       "      <td>18.25</td>\n",
       "      <td>28.03</td>\n",
       "      <td>3.26</td>\n",
       "      <td>2.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Anguilla</th>\n",
       "      <td>22.21</td>\n",
       "      <td>14.08</td>\n",
       "      <td>43.82</td>\n",
       "      <td>11.06</td>\n",
       "      <td>8.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Antigua and Barbuda</th>\n",
       "      <td>23.09</td>\n",
       "      <td>16.83</td>\n",
       "      <td>42.19</td>\n",
       "      <td>9.83</td>\n",
       "      <td>8.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Argentina</th>\n",
       "      <td>24.59</td>\n",
       "      <td>15.28</td>\n",
       "      <td>39.38</td>\n",
       "      <td>9.13</td>\n",
       "      <td>11.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Armenia</th>\n",
       "      <td>18.94</td>\n",
       "      <td>12.89</td>\n",
       "      <td>43.43</td>\n",
       "      <td>13.41</td>\n",
       "      <td>11.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aruba</th>\n",
       "      <td>17.64</td>\n",
       "      <td>12.78</td>\n",
       "      <td>41.72</td>\n",
       "      <td>14.28</td>\n",
       "      <td>13.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <td>17.80</td>\n",
       "      <td>12.79</td>\n",
       "      <td>41.45</td>\n",
       "      <td>11.83</td>\n",
       "      <td>16.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Austria</th>\n",
       "      <td>14.01</td>\n",
       "      <td>11.07</td>\n",
       "      <td>42.42</td>\n",
       "      <td>13.23</td>\n",
       "      <td>19.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Azerbaijan</th>\n",
       "      <td>22.95</td>\n",
       "      <td>14.84</td>\n",
       "      <td>45.39</td>\n",
       "      <td>10.17</td>\n",
       "      <td>6.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bahamas, The</th>\n",
       "      <td>22.55</td>\n",
       "      <td>16.40</td>\n",
       "      <td>44.14</td>\n",
       "      <td>9.16</td>\n",
       "      <td>7.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bahrain</th>\n",
       "      <td>19.08</td>\n",
       "      <td>15.65</td>\n",
       "      <td>56.04</td>\n",
       "      <td>6.28</td>\n",
       "      <td>2.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bangladesh</th>\n",
       "      <td>27.76</td>\n",
       "      <td>19.36</td>\n",
       "      <td>39.73</td>\n",
       "      <td>6.93</td>\n",
       "      <td>6.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Barbados</th>\n",
       "      <td>17.97</td>\n",
       "      <td>12.74</td>\n",
       "      <td>44.06</td>\n",
       "      <td>13.43</td>\n",
       "      <td>11.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belarus</th>\n",
       "      <td>15.78</td>\n",
       "      <td>10.29</td>\n",
       "      <td>44.76</td>\n",
       "      <td>14.21</td>\n",
       "      <td>14.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belgium</th>\n",
       "      <td>17.16</td>\n",
       "      <td>11.34</td>\n",
       "      <td>40.05</td>\n",
       "      <td>12.86</td>\n",
       "      <td>18.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belize</th>\n",
       "      <td>33.95</td>\n",
       "      <td>20.55</td>\n",
       "      <td>36.62</td>\n",
       "      <td>4.99</td>\n",
       "      <td>3.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Benin</th>\n",
       "      <td>42.65</td>\n",
       "      <td>20.44</td>\n",
       "      <td>30.44</td>\n",
       "      <td>3.61</td>\n",
       "      <td>2.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bermuda</th>\n",
       "      <td>17.04</td>\n",
       "      <td>12.00</td>\n",
       "      <td>37.24</td>\n",
       "      <td>15.73</td>\n",
       "      <td>17.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bhutan</th>\n",
       "      <td>25.80</td>\n",
       "      <td>18.81</td>\n",
       "      <td>43.07</td>\n",
       "      <td>6.03</td>\n",
       "      <td>6.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bolivia</th>\n",
       "      <td>31.85</td>\n",
       "      <td>19.46</td>\n",
       "      <td>37.48</td>\n",
       "      <td>5.90</td>\n",
       "      <td>5.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bosnia and Herzegovina</th>\n",
       "      <td>13.29</td>\n",
       "      <td>11.58</td>\n",
       "      <td>45.88</td>\n",
       "      <td>14.83</td>\n",
       "      <td>14.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Botswana</th>\n",
       "      <td>31.95</td>\n",
       "      <td>18.91</td>\n",
       "      <td>38.45</td>\n",
       "      <td>5.46</td>\n",
       "      <td>5.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brazil</th>\n",
       "      <td>22.33</td>\n",
       "      <td>16.36</td>\n",
       "      <td>43.86</td>\n",
       "      <td>9.12</td>\n",
       "      <td>8.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>British Virgin Islands</th>\n",
       "      <td>16.70</td>\n",
       "      <td>13.37</td>\n",
       "      <td>49.37</td>\n",
       "      <td>11.60</td>\n",
       "      <td>8.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brunei</th>\n",
       "      <td>23.12</td>\n",
       "      <td>17.05</td>\n",
       "      <td>46.75</td>\n",
       "      <td>8.23</td>\n",
       "      <td>4.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Taiwan</th>\n",
       "      <td>12.88</td>\n",
       "      <td>12.88</td>\n",
       "      <td>46.41</td>\n",
       "      <td>14.12</td>\n",
       "      <td>13.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tajikistan</th>\n",
       "      <td>32.33</td>\n",
       "      <td>18.61</td>\n",
       "      <td>40.12</td>\n",
       "      <td>5.62</td>\n",
       "      <td>3.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tanzania</th>\n",
       "      <td>43.74</td>\n",
       "      <td>19.86</td>\n",
       "      <td>29.88</td>\n",
       "      <td>3.51</td>\n",
       "      <td>3.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thailand</th>\n",
       "      <td>16.93</td>\n",
       "      <td>14.17</td>\n",
       "      <td>46.32</td>\n",
       "      <td>12.00</td>\n",
       "      <td>10.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Timor-Leste</th>\n",
       "      <td>40.91</td>\n",
       "      <td>20.32</td>\n",
       "      <td>29.95</td>\n",
       "      <td>4.94</td>\n",
       "      <td>3.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Togo</th>\n",
       "      <td>40.29</td>\n",
       "      <td>19.20</td>\n",
       "      <td>32.79</td>\n",
       "      <td>4.31</td>\n",
       "      <td>3.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tonga</th>\n",
       "      <td>33.87</td>\n",
       "      <td>19.65</td>\n",
       "      <td>34.30</td>\n",
       "      <td>5.76</td>\n",
       "      <td>6.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Trinidad and Tobago</th>\n",
       "      <td>19.29</td>\n",
       "      <td>11.88</td>\n",
       "      <td>45.56</td>\n",
       "      <td>12.61</td>\n",
       "      <td>10.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tunisia</th>\n",
       "      <td>25.15</td>\n",
       "      <td>13.99</td>\n",
       "      <td>43.38</td>\n",
       "      <td>9.54</td>\n",
       "      <td>7.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Turkey</th>\n",
       "      <td>24.68</td>\n",
       "      <td>15.99</td>\n",
       "      <td>43.21</td>\n",
       "      <td>8.58</td>\n",
       "      <td>7.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Turkmenistan</th>\n",
       "      <td>25.79</td>\n",
       "      <td>18.39</td>\n",
       "      <td>43.18</td>\n",
       "      <td>7.90</td>\n",
       "      <td>4.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Turks and Caicos Islands</th>\n",
       "      <td>21.74</td>\n",
       "      <td>13.99</td>\n",
       "      <td>53.17</td>\n",
       "      <td>6.54</td>\n",
       "      <td>4.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tuvalu</th>\n",
       "      <td>29.29</td>\n",
       "      <td>19.26</td>\n",
       "      <td>36.66</td>\n",
       "      <td>8.77</td>\n",
       "      <td>6.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uganda</th>\n",
       "      <td>48.05</td>\n",
       "      <td>21.10</td>\n",
       "      <td>26.30</td>\n",
       "      <td>2.57</td>\n",
       "      <td>1.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ukraine</th>\n",
       "      <td>15.76</td>\n",
       "      <td>9.86</td>\n",
       "      <td>44.29</td>\n",
       "      <td>13.80</td>\n",
       "      <td>16.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Arab Emirates</th>\n",
       "      <td>21.01</td>\n",
       "      <td>13.51</td>\n",
       "      <td>61.14</td>\n",
       "      <td>3.27</td>\n",
       "      <td>1.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>17.53</td>\n",
       "      <td>11.90</td>\n",
       "      <td>40.55</td>\n",
       "      <td>11.98</td>\n",
       "      <td>18.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>18.73</td>\n",
       "      <td>13.27</td>\n",
       "      <td>39.45</td>\n",
       "      <td>12.91</td>\n",
       "      <td>15.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uruguay</th>\n",
       "      <td>20.17</td>\n",
       "      <td>15.69</td>\n",
       "      <td>39.34</td>\n",
       "      <td>10.56</td>\n",
       "      <td>14.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uzbekistan</th>\n",
       "      <td>23.88</td>\n",
       "      <td>18.52</td>\n",
       "      <td>44.49</td>\n",
       "      <td>7.85</td>\n",
       "      <td>5.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vanuatu</th>\n",
       "      <td>35.51</td>\n",
       "      <td>20.02</td>\n",
       "      <td>35.06</td>\n",
       "      <td>5.42</td>\n",
       "      <td>3.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Venezuela</th>\n",
       "      <td>27.36</td>\n",
       "      <td>17.03</td>\n",
       "      <td>40.53</td>\n",
       "      <td>7.98</td>\n",
       "      <td>7.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vietnam</th>\n",
       "      <td>23.55</td>\n",
       "      <td>16.23</td>\n",
       "      <td>45.56</td>\n",
       "      <td>8.55</td>\n",
       "      <td>6.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Virgin Islands</th>\n",
       "      <td>20.41</td>\n",
       "      <td>11.19</td>\n",
       "      <td>37.07</td>\n",
       "      <td>13.78</td>\n",
       "      <td>17.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wallis and Futuna</th>\n",
       "      <td>21.83</td>\n",
       "      <td>16.30</td>\n",
       "      <td>42.08</td>\n",
       "      <td>9.79</td>\n",
       "      <td>10.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>West Bank</th>\n",
       "      <td>36.50</td>\n",
       "      <td>21.37</td>\n",
       "      <td>34.10</td>\n",
       "      <td>4.54</td>\n",
       "      <td>3.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Western Sahara</th>\n",
       "      <td>37.24</td>\n",
       "      <td>19.53</td>\n",
       "      <td>34.33</td>\n",
       "      <td>5.03</td>\n",
       "      <td>3.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yemen</th>\n",
       "      <td>39.83</td>\n",
       "      <td>21.21</td>\n",
       "      <td>32.27</td>\n",
       "      <td>3.94</td>\n",
       "      <td>2.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zambia</th>\n",
       "      <td>46.03</td>\n",
       "      <td>20.00</td>\n",
       "      <td>28.72</td>\n",
       "      <td>2.93</td>\n",
       "      <td>2.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zimbabwe</th>\n",
       "      <td>38.90</td>\n",
       "      <td>20.47</td>\n",
       "      <td>31.90</td>\n",
       "      <td>4.27</td>\n",
       "      <td>4.46</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>229 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                            0-14 years old %  15-24 years %  25-54 years %  \\\n",
       "COUNTRY                                                                      \n",
       " Afghanistan                           40.92          22.22          30.35   \n",
       " Albania                               18.05          17.47          41.06   \n",
       " Algeria                               29.31          15.30          42.93   \n",
       " American Samoa                        30.28          18.79          36.38   \n",
       " Andorra                               14.40           9.64          46.18   \n",
       " Angola                                48.12          18.25          28.03   \n",
       " Anguilla                              22.21          14.08          43.82   \n",
       " Antigua and Barbuda                   23.09          16.83          42.19   \n",
       " Argentina                             24.59          15.28          39.38   \n",
       " Armenia                               18.94          12.89          43.43   \n",
       " Aruba                                 17.64          12.78          41.72   \n",
       " Australia                             17.80          12.79          41.45   \n",
       " Austria                               14.01          11.07          42.42   \n",
       " Azerbaijan                            22.95          14.84          45.39   \n",
       " Bahamas, The                          22.55          16.40          44.14   \n",
       " Bahrain                               19.08          15.65          56.04   \n",
       " Bangladesh                            27.76          19.36          39.73   \n",
       " Barbados                              17.97          12.74          44.06   \n",
       " Belarus                               15.78          10.29          44.76   \n",
       " Belgium                               17.16          11.34          40.05   \n",
       " Belize                                33.95          20.55          36.62   \n",
       " Benin                                 42.65          20.44          30.44   \n",
       " Bermuda                               17.04          12.00          37.24   \n",
       " Bhutan                                25.80          18.81          43.07   \n",
       " Bolivia                               31.85          19.46          37.48   \n",
       " Bosnia and Herzegovina                13.29          11.58          45.88   \n",
       " Botswana                              31.95          18.91          38.45   \n",
       " Brazil                                22.33          16.36          43.86   \n",
       " British Virgin Islands                16.70          13.37          49.37   \n",
       " Brunei                                23.12          17.05          46.75   \n",
       "...                                      ...            ...            ...   \n",
       " Taiwan                                12.88          12.88          46.41   \n",
       " Tajikistan                            32.33          18.61          40.12   \n",
       " Tanzania                              43.74          19.86          29.88   \n",
       " Thailand                              16.93          14.17          46.32   \n",
       " Timor-Leste                           40.91          20.32          29.95   \n",
       " Togo                                  40.29          19.20          32.79   \n",
       " Tonga                                 33.87          19.65          34.30   \n",
       " Trinidad and Tobago                   19.29          11.88          45.56   \n",
       " Tunisia                               25.15          13.99          43.38   \n",
       " Turkey                                24.68          15.99          43.21   \n",
       " Turkmenistan                          25.79          18.39          43.18   \n",
       " Turks and Caicos Islands              21.74          13.99          53.17   \n",
       " Tuvalu                                29.29          19.26          36.66   \n",
       " Uganda                                48.05          21.10          26.30   \n",
       " Ukraine                               15.76           9.86          44.29   \n",
       " United Arab Emirates                  21.01          13.51          61.14   \n",
       " United Kingdom                        17.53          11.90          40.55   \n",
       " United States                         18.73          13.27          39.45   \n",
       " Uruguay                               20.17          15.69          39.34   \n",
       " Uzbekistan                            23.88          18.52          44.49   \n",
       " Vanuatu                               35.51          20.02          35.06   \n",
       " Venezuela                             27.36          17.03          40.53   \n",
       " Vietnam                               23.55          16.23          45.56   \n",
       " Virgin Islands                        20.41          11.19          37.07   \n",
       " Wallis and Futuna                     21.83          16.30          42.08   \n",
       " West Bank                             36.50          21.37          34.10   \n",
       " Western Sahara                        37.24          19.53          34.33   \n",
       " Yemen                                 39.83          21.21          32.27   \n",
       " Zambia                                46.03          20.00          28.72   \n",
       " Zimbabwe                              38.90          20.47          31.90   \n",
       "\n",
       "                            55-64 years %  65 years and above %  \n",
       "COUNTRY                                                          \n",
       " Afghanistan                         3.92                  2.59  \n",
       " Albania                            11.54                 11.89  \n",
       " Algeria                             6.81                  5.65  \n",
       " American Samoa                      8.58                  5.97  \n",
       " Andorra                            14.16                 15.61  \n",
       " Angola                              3.26                  2.34  \n",
       " Anguilla                           11.06                  8.84  \n",
       " Antigua and Barbuda                 9.83                  8.06  \n",
       " Argentina                           9.13                 11.62  \n",
       " Armenia                            13.41                 11.33  \n",
       " Aruba                              14.28                 13.59  \n",
       " Australia                          11.83                 16.14  \n",
       " Austria                            13.23                 19.26  \n",
       " Azerbaijan                         10.17                  6.64  \n",
       " Bahamas, The                        9.16                  7.75  \n",
       " Bahrain                             6.28                  2.95  \n",
       " Bangladesh                          6.93                  6.23  \n",
       " Barbados                           13.43                 11.81  \n",
       " Belarus                            14.21                 14.95  \n",
       " Belgium                            12.86                 18.58  \n",
       " Belize                              4.99                  3.89  \n",
       " Benin                               3.61                  2.87  \n",
       " Bermuda                            15.73                 17.98  \n",
       " Bhutan                              6.03                  6.29  \n",
       " Bolivia                             5.90                  5.30  \n",
       " Bosnia and Herzegovina             14.83                 14.43  \n",
       " Botswana                            5.46                  5.23  \n",
       " Brazil                              9.12                  8.33  \n",
       " British Virgin Islands             11.60                  8.97  \n",
       " Brunei                              8.23                  4.84  \n",
       "...                                   ...                   ...  \n",
       " Taiwan                             14.12                 13.72  \n",
       " Tajikistan                          5.62                  3.32  \n",
       " Tanzania                            3.51                  3.02  \n",
       " Thailand                           12.00                 10.58  \n",
       " Timor-Leste                         4.94                  3.87  \n",
       " Togo                                4.31                  3.41  \n",
       " Tonga                               5.76                  6.42  \n",
       " Trinidad and Tobago                12.61                 10.65  \n",
       " Tunisia                             9.54                  7.95  \n",
       " Turkey                              8.58                  7.53  \n",
       " Turkmenistan                        7.90                  4.74  \n",
       " Turks and Caicos Islands            6.54                  4.57  \n",
       " Tuvalu                              8.77                  6.02  \n",
       " Uganda                              2.57                  1.98  \n",
       " Ukraine                            13.80                 16.30  \n",
       " United Arab Emirates                3.27                  1.07  \n",
       " United Kingdom                     11.98                 18.04  \n",
       " United States                      12.91                 15.63  \n",
       " Uruguay                            10.56                 14.25  \n",
       " Uzbekistan                          7.85                  5.25  \n",
       " Vanuatu                             5.42                  3.99  \n",
       " Venezuela                           7.98                  7.09  \n",
       " Vietnam                             8.55                  6.12  \n",
       " Virgin Islands                     13.78                 17.55  \n",
       " Wallis and Futuna                   9.79                 10.01  \n",
       " West Bank                           4.54                  3.49  \n",
       " Western Sahara                      5.03                  3.87  \n",
       " Yemen                               3.94                  2.75  \n",
       " Zambia                              2.93                  2.33  \n",
       " Zimbabwe                            4.27                  4.46  \n",
       "\n",
       "[229 rows x 5 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data={\n",
    "    '0-14 years old %':demographics1,\n",
    "    '15-24 years %':demographics2,\n",
    "    '25-54 years %':demographics3,\n",
    "    '55-64 years %':demographics4,\n",
    "    '65 years and above %':demographics5\n",
    "}\n",
    "#df1=pd.DataFrame(data=data,index=country_names[1:5])\n",
    "df_demo=pd.DataFrame(data=data,index=country_names[1:len(country_codes)-1])\n",
    "df_demo.index.name='COUNTRY'\n",
    "df_demo.dropna(inplace=True)\n",
    "df_demo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Save the data in an Excel file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a Pandas Excel writer using XlsxWriter as the engine.\n",
    "writer = pd.ExcelWriter('Demo1.xlsx', engine='xlsxwriter')\n",
    "\n",
    "# Convert the dataframe to an XlsxWriter Excel object.\n",
    "df_demo.to_excel(writer, sheet_name='Demographics')\n",
    "\n",
    "# Close the Pandas Excel writer and output the Excel file.\n",
    "writer.save()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x23072c10438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2307277dfd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x230743d8e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x230731cb518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x230748a1eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(5):\n",
    "    df_demo.hist(df_demo.columns[i],bins=25,figsize=(8,5),color='orange',edgecolor='k',alpha=0.7)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x23072c10048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_demo.boxplot(figsize=(9,5))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Download all text data and save in a pickle file for easy later access"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finished loading data for  Afghanistan \n",
      "Finished loading data for  Akrotiri \n",
      "Finished loading data for  Albania \n",
      "Finished loading data for  Algeria \n",
      "Finished loading data for  American Samoa \n",
      "Finished loading data for  Andorra \n",
      "Finished loading data for  Angola \n",
      "Finished loading data for  Anguilla \n",
      "Finished loading data for  Antarctica \n",
      "Finished loading data for  Antigua and Barbuda \n",
      "Finished loading data for  Arctic Ocean \n",
      "Finished loading data for  Argentina \n",
      "Finished loading data for  Armenia \n",
      "Finished loading data for  Aruba \n",
      "Finished loading data for  Ashmore and Cartier Islands \n",
      "Finished loading data for  Atlantic Ocean \n",
      "Finished loading data for  Australia \n",
      "Finished loading data for  Austria \n",
      "Finished loading data for  Azerbaijan \n",
      "Finished loading data for  Bahamas, The \n",
      "Finished loading data for  Bahrain \n",
      "Finished loading data for  Baker Island \n",
      "Finished loading data for  Bangladesh \n",
      "Finished loading data for  Barbados \n",
      "Finished loading data for  Belarus \n",
      "Finished loading data for  Belgium \n",
      "Finished loading data for  Belize \n",
      "Finished loading data for  Benin \n",
      "Finished loading data for  Bermuda \n",
      "Finished loading data for  Bhutan \n",
      "Finished loading data for  Bolivia \n",
      "Finished loading data for  Bosnia and Herzegovina \n",
      "Finished loading data for  Botswana \n",
      "Finished loading data for  Bouvet Island \n",
      "Finished loading data for  Brazil \n",
      "Finished loading data for  British Indian Ocean Territory \n",
      "Finished loading data for  British Virgin Islands \n",
      "Finished loading data for  Brunei \n",
      "Finished loading data for  Bulgaria \n",
      "Finished loading data for  Burkina Faso \n",
      "Finished loading data for  Burma \n",
      "Finished loading data for  Burundi \n",
      "Finished loading data for  Cabo Verde \n",
      "Finished loading data for  Cambodia \n",
      "Finished loading data for  Cameroon \n",
      "Finished loading data for  Canada \n",
      "Finished loading data for  Cayman Islands \n",
      "Finished loading data for  Central African Republic \n",
      "Finished loading data for  Chad \n",
      "Finished loading data for  Chile \n",
      "Finished loading data for  China \n",
      "Finished loading data for  Christmas Island \n",
      "Finished loading data for  Clipperton Island \n",
      "Finished loading data for  Cocos (Keeling) Islands \n",
      "Finished loading data for  Colombia \n",
      "Finished loading data for  Comoros \n",
      "Finished loading data for  Congo, Democratic Republic of the \n",
      "Finished loading data for  Congo, Republic of the \n",
      "Finished loading data for  Cook Islands \n",
      "Finished loading data for  Coral Sea Islands \n",
      "Finished loading data for  Costa Rica \n",
      "Finished loading data for  Cote d'Ivoire \n",
      "Finished loading data for  Croatia \n",
      "Finished loading data for  Cuba \n",
      "Finished loading data for  Curacao \n",
      "Finished loading data for  Cyprus \n",
      "Finished loading data for  Czechia \n",
      "Finished loading data for  Denmark \n",
      "Finished loading data for  Dhekelia \n",
      "Finished loading data for  Djibouti \n",
      "Finished loading data for  Dominica \n",
      "Finished loading data for  Dominican Republic \n",
      "Finished loading data for  Ecuador \n",
      "Finished loading data for  Egypt \n",
      "Finished loading data for  El Salvador \n",
      "Finished loading data for  Equatorial Guinea \n",
      "Finished loading data for  Eritrea \n",
      "Finished loading data for  Estonia \n",
      "Finished loading data for  Ethiopia \n",
      "Finished loading data for  Falkland Islands (Islas Malvinas) \n",
      "Finished loading data for  Faroe Islands \n",
      "Finished loading data for  Fiji \n",
      "Finished loading data for  Finland \n",
      "Finished loading data for  France \n",
      "Finished loading data for  French Polynesia \n",
      "Finished loading data for  French Southern and Antarctic Lands \n",
      "Finished loading data for  Gabon \n",
      "Finished loading data for  Gambia, The \n",
      "Finished loading data for  Gaza Strip \n",
      "Finished loading data for  Georgia \n",
      "Finished loading data for  Germany \n",
      "Finished loading data for  Ghana \n",
      "Finished loading data for  Gibraltar \n",
      "Finished loading data for  Greece \n",
      "Finished loading data for  Greenland \n",
      "Finished loading data for  Grenada \n",
      "Finished loading data for  Guam \n",
      "Finished loading data for  Guatemala \n",
      "Finished loading data for  Guernsey \n",
      "Finished loading data for  Guinea \n",
      "Finished loading data for  Guinea-Bissau \n",
      "Finished loading data for  Guyana \n",
      "Finished loading data for  Haiti \n",
      "Finished loading data for  Heard Island and McDonald Islands \n",
      "Finished loading data for  Holy See (Vatican City) \n",
      "Finished loading data for  Honduras \n",
      "Finished loading data for  Hong Kong \n",
      "Finished loading data for  Howland Island \n",
      "Finished loading data for  Hungary \n",
      "Finished loading data for  Iceland \n",
      "Finished loading data for  India \n",
      "Finished loading data for  Indian Ocean \n",
      "Finished loading data for  Indonesia \n",
      "Finished loading data for  Iran \n",
      "Finished loading data for  Iraq \n",
      "Finished loading data for  Ireland \n",
      "Finished loading data for  Isle of Man \n",
      "Finished loading data for  Israel \n",
      "Finished loading data for  Italy \n",
      "Finished loading data for  Jamaica \n",
      "Finished loading data for  Jan Mayen \n",
      "Finished loading data for  Japan \n",
      "Finished loading data for  Jarvis Island \n",
      "Finished loading data for  Jersey \n",
      "Finished loading data for  Johnston Atoll \n",
      "Finished loading data for  Jordan \n",
      "Finished loading data for  Kazakhstan \n",
      "Finished loading data for  Kenya \n",
      "Finished loading data for  Kingman Reef \n",
      "Finished loading data for  Kiribati \n",
      "Finished loading data for  Korea, North \n",
      "Finished loading data for  Korea, South \n",
      "Finished loading data for  Kosovo \n",
      "Finished loading data for  Kuwait \n",
      "Finished loading data for  Kyrgyzstan \n",
      "Finished loading data for  Laos \n",
      "Finished loading data for  Latvia \n",
      "Finished loading data for  Lebanon \n",
      "Finished loading data for  Lesotho \n",
      "Finished loading data for  Liberia \n",
      "Finished loading data for  Libya \n",
      "Finished loading data for  Liechtenstein \n",
      "Finished loading data for  Lithuania \n",
      "Finished loading data for  Luxembourg \n",
      "Finished loading data for  Macau \n",
      "Finished loading data for  Macedonia \n",
      "Finished loading data for  Madagascar \n",
      "Finished loading data for  Malawi \n",
      "Finished loading data for  Malaysia \n",
      "Finished loading data for  Maldives \n",
      "Finished loading data for  Mali \n",
      "Finished loading data for  Malta \n",
      "Finished loading data for  Marshall Islands \n",
      "Finished loading data for  Mauritania \n",
      "Finished loading data for  Mauritius \n",
      "Finished loading data for  Mexico \n",
      "Finished loading data for  Micronesia, Federated States of \n",
      "Finished loading data for  Midway Islands \n",
      "Finished loading data for  Moldova \n",
      "Finished loading data for  Monaco \n",
      "Finished loading data for  Mongolia \n",
      "Finished loading data for  Montenegro \n",
      "Finished loading data for  Montserrat \n",
      "Finished loading data for  Morocco \n",
      "Finished loading data for  Mozambique \n",
      "Finished loading data for  Namibia \n",
      "Finished loading data for  Nauru \n",
      "Finished loading data for  Navassa Island \n",
      "Finished loading data for  Nepal \n",
      "Finished loading data for  Netherlands \n",
      "Finished loading data for  New Caledonia \n",
      "Finished loading data for  New Zealand \n",
      "Finished loading data for  Nicaragua \n",
      "Finished loading data for  Niger \n",
      "Finished loading data for  Nigeria \n",
      "Finished loading data for  Niue \n",
      "Finished loading data for  Norfolk Island \n",
      "Finished loading data for  Northern Mariana Islands \n",
      "Finished loading data for  Norway \n",
      "Finished loading data for  Oman \n",
      "Finished loading data for  Pacific Ocean \n",
      "Finished loading data for  Pakistan \n",
      "Finished loading data for  Palau \n",
      "Finished loading data for  Palmyra Atoll \n",
      "Finished loading data for  Panama \n",
      "Finished loading data for  Papua New Guinea \n",
      "Finished loading data for  Paracel Islands \n",
      "Finished loading data for  Paraguay \n",
      "Finished loading data for  Peru \n",
      "Finished loading data for  Philippines \n",
      "Finished loading data for  Pitcairn Islands \n",
      "Finished loading data for  Poland \n",
      "Finished loading data for  Portugal \n",
      "Finished loading data for  Puerto Rico \n",
      "Finished loading data for  Qatar \n",
      "Finished loading data for  Romania \n",
      "Finished loading data for  Russia \n",
      "Finished loading data for  Rwanda \n",
      "Finished loading data for  Saint Barthelemy \n",
      "Finished loading data for  Saint Helena, Ascension, and Tristan da Cunha \n",
      "Finished loading data for  Saint Kitts and Nevis \n",
      "Finished loading data for  Saint Lucia \n",
      "Finished loading data for  Saint Martin \n",
      "Finished loading data for  Saint Pierre and Miquelon \n",
      "Finished loading data for  Saint Vincent and the Grenadines \n",
      "Finished loading data for  Samoa \n",
      "Finished loading data for  San Marino \n",
      "Finished loading data for  Sao Tome and Principe \n",
      "Finished loading data for  Saudi Arabia \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finished loading data for  Senegal \n",
      "Finished loading data for  Serbia \n",
      "Finished loading data for  Seychelles \n",
      "Finished loading data for  Sierra Leone \n",
      "Finished loading data for  Singapore \n",
      "Finished loading data for  Sint Maarten \n",
      "Finished loading data for  Slovakia \n",
      "Finished loading data for  Slovenia \n",
      "Finished loading data for  Solomon Islands \n",
      "Finished loading data for  Somalia \n",
      "Finished loading data for  South Africa \n",
      "Finished loading data for  Southern Ocean \n",
      "Finished loading data for  South Georgia and South Sandwich Islands \n",
      "Finished loading data for  South Sudan \n",
      "Finished loading data for  Spain \n",
      "Finished loading data for  Spratly Islands \n",
      "Finished loading data for  Sri Lanka \n",
      "Finished loading data for  Sudan \n",
      "Finished loading data for  Suriname \n",
      "Finished loading data for  Svalbard \n",
      "Finished loading data for  Swaziland \n",
      "Finished loading data for  Sweden \n",
      "Finished loading data for  Switzerland \n",
      "Finished loading data for  Syria \n",
      "Finished loading data for  Taiwan \n",
      "Finished loading data for  Tajikistan \n",
      "Finished loading data for  Tanzania \n",
      "Finished loading data for  Thailand \n",
      "Finished loading data for  Timor-Leste \n",
      "Finished loading data for  Togo \n",
      "Finished loading data for  Tokelau \n",
      "Finished loading data for  Tonga \n",
      "Finished loading data for  Trinidad and Tobago \n",
      "Finished loading data for  Tunisia \n",
      "Finished loading data for  Turkey \n",
      "Finished loading data for  Turkmenistan \n",
      "Finished loading data for  Turks and Caicos Islands \n",
      "Finished loading data for  Tuvalu \n",
      "Finished loading data for  Uganda \n",
      "Finished loading data for  Ukraine \n",
      "Finished loading data for  United Arab Emirates \n",
      "Finished loading data for  United Kingdom \n",
      "Finished loading data for  United States \n",
      "Finished loading data for  United States Pacific Island Wildlife Refuges \n",
      "Finished loading data for  Uruguay \n",
      "Finished loading data for  Uzbekistan \n",
      "Finished loading data for  Vanuatu \n",
      "Finished loading data for  Venezuela \n",
      "Finished loading data for  Vietnam \n",
      "Finished loading data for  Virgin Islands \n",
      "Finished loading data for  Wake Island \n",
      "Finished loading data for  Wallis and Futuna \n",
      "Finished loading data for  West Bank \n",
      "Finished loading data for  Western Sahara \n",
      "Finished loading data for  Yemen \n",
      "Finished loading data for  Zambia \n",
      "Finished loading data for  Zimbabwe \n",
      "\n",
      "**Finished downloading all text data!**\n"
     ]
    }
   ],
   "source": [
    "# Base URL\n",
    "urlbase = 'https://www.cia.gov/library/publications/the-world-factbook/geos/'\n",
    "# Empty data dictionary\n",
    "text_data=dict()\n",
    "\n",
    "# Iterate over every country\n",
    "for i in range(1,len(country_names)-1):\n",
    "    country_html=country_codes[i]+'.html'\n",
    "    url_to_get=urlbase+country_html\n",
    "    # Read the HTML from the URL and pass on to BeautifulSoup\n",
    "    html = urllib.request.urlopen(url_to_get, context=ctx).read()\n",
    "    soup = BeautifulSoup(html, 'html.parser')\n",
    "    txt=soup.get_text()\n",
    "    text_data[country_names[i]]=txt\n",
    "    print(f\"Finished loading data for {country_names[i]}\")\n",
    "    \n",
    "print (\"\\n**Finished downloading all text data!**\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "pickle.dump(text_data,open(\"text_data_CIA_Factobook.p\", \"wb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [],
   "source": [
    "text_data = pickle.load(open(\"text_data_CIA_Factobook.p\", \"rb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total length 12558614\n",
      "Average length 47212.83458646617\n"
     ]
    }
   ],
   "source": [
    "avg = 0\n",
    "total=0\n",
    "for k,v in text_data.items():\n",
    "    total+=len(v)\n",
    "print(\"Total length\",total)\n",
    "avg=total/len(text_data)\n",
    "print(\"Average length\",avg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys([' Afghanistan ', ' Akrotiri ', ' Albania ', ' Algeria ', ' American Samoa ', ' Andorra ', ' Angola ', ' Anguilla ', ' Antarctica ', ' Antigua and Barbuda ', ' Arctic Ocean ', ' Argentina ', ' Armenia ', ' Aruba ', ' Ashmore and Cartier Islands ', ' Atlantic Ocean ', ' Australia ', ' Austria ', ' Azerbaijan ', ' Bahamas, The ', ' Bahrain ', ' Baker Island ', ' Bangladesh ', ' Barbados ', ' Belarus ', ' Belgium ', ' Belize ', ' Benin ', ' Bermuda ', ' Bhutan ', ' Bolivia ', ' Bosnia and Herzegovina ', ' Botswana ', ' Bouvet Island ', ' Brazil ', ' British Indian Ocean Territory ', ' British Virgin Islands ', ' Brunei ', ' Bulgaria ', ' Burkina Faso ', ' Burma ', ' Burundi ', ' Cabo Verde ', ' Cambodia ', ' Cameroon ', ' Canada ', ' Cayman Islands ', ' Central African Republic ', ' Chad ', ' Chile ', ' China ', ' Christmas Island ', ' Clipperton Island ', ' Cocos (Keeling) Islands ', ' Colombia ', ' Comoros ', ' Congo, Democratic Republic of the ', ' Congo, Republic of the ', ' Cook Islands ', ' Coral Sea Islands ', ' Costa Rica ', \" Cote d'Ivoire \", ' Croatia ', ' Cuba ', ' Curacao ', ' Cyprus ', ' Czechia ', ' Denmark ', ' Dhekelia ', ' Djibouti ', ' Dominica ', ' Dominican Republic ', ' Ecuador ', ' Egypt ', ' El Salvador ', ' Equatorial Guinea ', ' Eritrea ', ' Estonia ', ' Ethiopia ', ' Falkland Islands (Islas Malvinas) ', ' Faroe Islands ', ' Fiji ', ' Finland ', ' France ', ' French Polynesia ', ' French Southern and Antarctic Lands ', ' Gabon ', ' Gambia, The ', ' Gaza Strip ', ' Georgia ', ' Germany ', ' Ghana ', ' Gibraltar ', ' Greece ', ' Greenland ', ' Grenada ', ' Guam ', ' Guatemala ', ' Guernsey ', ' Guinea ', ' Guinea-Bissau ', ' Guyana ', ' Haiti ', ' Heard Island and McDonald Islands ', ' Holy See (Vatican City) ', ' Honduras ', ' Hong Kong ', ' Howland Island ', ' Hungary ', ' Iceland ', ' India ', ' Indian Ocean ', ' Indonesia ', ' Iran ', ' Iraq ', ' Ireland ', ' Isle of Man ', ' Israel ', ' Italy ', ' Jamaica ', ' Jan Mayen ', ' Japan ', ' Jarvis Island ', ' Jersey ', ' Johnston Atoll ', ' Jordan ', ' Kazakhstan ', ' Kenya ', ' Kingman Reef ', ' Kiribati ', ' Korea, North ', ' Korea, South ', ' Kosovo ', ' Kuwait ', ' Kyrgyzstan ', ' Laos ', ' Latvia ', ' Lebanon ', ' Lesotho ', ' Liberia ', ' Libya ', ' Liechtenstein ', ' Lithuania ', ' Luxembourg ', ' Macau ', ' Macedonia ', ' Madagascar ', ' Malawi ', ' Malaysia ', ' Maldives ', ' Mali ', ' Malta ', ' Marshall Islands ', ' Mauritania ', ' Mauritius ', ' Mexico ', ' Micronesia, Federated States of ', ' Midway Islands ', ' Moldova ', ' Monaco ', ' Mongolia ', ' Montenegro ', ' Montserrat ', ' Morocco ', ' Mozambique ', ' Namibia ', ' Nauru ', ' Navassa Island ', ' Nepal ', ' Netherlands ', ' New Caledonia ', ' New Zealand ', ' Nicaragua ', ' Niger ', ' Nigeria ', ' Niue ', ' Norfolk Island ', ' Northern Mariana Islands ', ' Norway ', ' Oman ', ' Pacific Ocean ', ' Pakistan ', ' Palau ', ' Palmyra Atoll ', ' Panama ', ' Papua New Guinea ', ' Paracel Islands ', ' Paraguay ', ' Peru ', ' Philippines ', ' Pitcairn Islands ', ' Poland ', ' Portugal ', ' Puerto Rico ', ' Qatar ', ' Romania ', ' Russia ', ' Rwanda ', ' Saint Barthelemy ', ' Saint Helena, Ascension, and Tristan da Cunha ', ' Saint Kitts and Nevis ', ' Saint Lucia ', ' Saint Martin ', ' Saint Pierre and Miquelon ', ' Saint Vincent and the Grenadines ', ' Samoa ', ' San Marino ', ' Sao Tome and Principe ', ' Saudi Arabia ', ' Senegal ', ' Serbia ', ' Seychelles ', ' Sierra Leone ', ' Singapore ', ' Sint Maarten ', ' Slovakia ', ' Slovenia ', ' Solomon Islands ', ' Somalia ', ' South Africa ', ' Southern Ocean ', ' South Georgia and South Sandwich Islands ', ' South Sudan ', ' Spain ', ' Spratly Islands ', ' Sri Lanka ', ' Sudan ', ' Suriname ', ' Svalbard ', ' Swaziland ', ' Sweden ', ' Switzerland ', ' Syria ', ' Taiwan ', ' Tajikistan ', ' Tanzania ', ' Thailand ', ' Timor-Leste ', ' Togo ', ' Tokelau ', ' Tonga ', ' Trinidad and Tobago ', ' Tunisia ', ' Turkey ', ' Turkmenistan ', ' Turks and Caicos Islands ', ' Tuvalu ', ' Uganda ', ' Ukraine ', ' United Arab Emirates ', ' United Kingdom ', ' United States ', ' United States Pacific Island Wildlife Refuges ', ' Uruguay ', ' Uzbekistan ', ' Vanuatu ', ' Venezuela ', ' Vietnam ', ' Virgin Islands ', ' Wake Island ', ' Wallis and Futuna ', ' West Bank ', ' Western Sahara ', ' Yemen ', ' Zambia ', ' Zimbabwe '])"
      ]
     },
     "execution_count": 224,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "text_data.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Function to convert a string with a comma (e.g. a GDP value) to a float"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [],
   "source": [
    "def convert_float(string):\n",
    "    if string.isnumeric():\n",
    "        return float(string)\n",
    "    if string[0].isdigit():\n",
    "        if ',' not in string and '.' in string:\n",
    "            return float(string)\n",
    "        if ',' not in string and '.' not in string:\n",
    "            idx=string.find(' ')\n",
    "            result = string[:idx]\n",
    "            return float(result)\n",
    "        idx1=string.find(',')\n",
    "        idx2=string.find(' ')\n",
    "        result = string[:idx1]+string[idx1+1:idx2]\n",
    "        return float(result)\n",
    "    else:\n",
    "        return (-1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Extract Total GDP (PPP) (in billion $)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "69.51\n",
      "**Could not find GDP data!**\n",
      "35.87\n",
      "629.3\n",
      "**Could not find GDP data!**\n",
      "3.327\n",
      "192\n",
      "**Could not find GDP data!**\n",
      "**Could not find GDP data!**\n",
      "2.39\n",
      "**Could not find GDP data!**\n",
      "911.5\n",
      "27.21\n",
      "2.516\n",
      "**Could not find GDP data!**\n",
      "**Could not find GDP data!**\n",
      "1.235\n",
      "434.1\n",
      "166.8\n",
      "9.339\n",
      "69.77\n",
      "**Could not find GDP data!**\n",
      "686.5\n",
      "4.919\n",
      "175.9\n",
      "526.4\n",
      "3.23\n",
      "25.29\n",
      "5.198\n",
      "7.011\n",
      "83.5\n",
      "43.85\n",
      "39.55\n",
      "**Could not find GDP data!**\n",
      "3.219\n",
      "**Could not find GDP data!**\n",
      "**Could not find GDP data!**\n",
      "32.91\n",
      "152.4\n",
      "35.68\n",
      "330.9\n",
      "7.985\n",
      "3.734\n",
      "64.21\n",
      "81.55\n",
      "1.764\n",
      "2.507\n",
      "3.395\n",
      "29.64\n",
      "452.1\n",
      "23.12\n",
      "NA\n",
      "Agricu\n",
      "**Could not find GDP data!**\n",
      "**Could not find GDP data!**\n",
      "712.5\n",
      "1.323\n",
      "67.99\n",
      "29.16\n",
      "**Could not find GDP data!**\n",
      "**Could not find GDP data!**\n",
      "85.2\n",
      "96.27\n",
      "100.2\n",
      "132.9\n",
      "3.128\n",
      "31.19\n",
      "372.6\n",
      "285.5\n",
      "**Could not find GDP data!**\n",
      "3.64\n",
      "**Could not find GDP data!**\n",
      "172.6\n",
      "188.5\n",
      "1.199\n",
      "56.9\n",
      "29.38\n",
      "9.631\n",
      "41.2\n",
      "195.8\n",
      "**Could not find GDP data!**\n",
      "2.001\n",
      "8.647\n",
      "242.4\n",
      "2.826\n",
      "5.49\n",
      "**Could not find GDP data!**\n",
      "36.75\n",
      "3.582\n",
      "**Could not find GDP data!**\n",
      "39.32\n",
      "4.15\n",
      "130.2\n",
      "2.044\n",
      "299.5\n",
      "2.173\n",
      "1.59\n",
      "4.882\n",
      "138.3\n",
      "3.465\n",
      "26.45\n",
      "3.071\n",
      "6.367\n",
      "19.88\n",
      "**Could not find GDP data!**\n",
      "NA\n",
      "Indu\n",
      "45.68\n",
      "453\n",
      "**Could not find GDP data!**\n",
      "283.6\n",
      "17.73\n",
      "9.447\n",
      "**Could not find GDP data!**\n",
      "3.243\n",
      "1.631\n",
      "660.7\n",
      "344.8\n",
      "7.428\n",
      "315.6\n",
      "2.307\n",
      "26.2\n",
      "**Could not find GDP data!**\n",
      "5.405\n",
      "**Could not find GDP data!**\n",
      "5.08\n",
      "**Could not find GDP data!**\n",
      "89.05\n",
      "474.3\n",
      "163.4\n",
      "**Could not find GDP data!**\n",
      "**Could not find GDP data!**\n",
      "40\n",
      "2.027\n",
      "19.38\n",
      "302.5\n",
      "22.64\n",
      "49.21\n",
      "53.47\n",
      "87.89\n",
      "7.448\n",
      "3.906\n",
      "63.14\n",
      "4.978\n",
      "90.63\n",
      "64.39\n",
      "73.58\n",
      "31.55\n",
      "39.81\n",
      "22.47\n",
      "926.1\n",
      "6.896\n",
      "40.98\n",
      "18.53\n",
      "**Could not find GDP data!**\n",
      "17.37\n",
      "27.44\n",
      "2.406\n",
      "**Could not find GDP data!**\n",
      "**Could not find GDP data!**\n",
      "20.07\n",
      "7.672\n",
      "38.4\n",
      "10.86\n",
      "**Could not find GDP data!**\n",
      "300.1\n",
      "37.39\n",
      "27.02\n",
      "**Could not find GDP data!**\n",
      "**Could not find GDP data!**\n",
      "78.55\n",
      "915.2\n",
      "10.77\n",
      "185.7\n",
      "36.22\n",
      "21.62\n",
      "1.118\n",
      "**Could not find GDP data!**\n",
      "NA\n",
      "Agricu\n",
      "**Could not find GDP data!**\n",
      "375.9\n",
      "187.9\n",
      "**Could not find GDP data!**\n",
      "1.056\n",
      "**Could not find GDP data!**\n",
      "**Could not find GDP data!**\n",
      "99.43\n",
      "30.84\n",
      "**Could not find GDP data!**\n",
      "68.05\n",
      "424.6\n",
      "874.5\n",
      "NA\n",
      "Agricu\n",
      "1.111\n",
      "311.3\n",
      "127.3\n",
      "341.7\n",
      "474\n",
      "4\n",
      "24.61\n",
      "**Could not find GDP data!**\n",
      "**Could not find GDP data!**\n",
      "1.528\n",
      "2.384\n",
      "**Could not find GDP data!**\n",
      "**Could not find GDP data!**\n",
      "1.281\n",
      "1.13\n",
      "2.083\n",
      "**Could not find GDP data!**\n",
      "1.789\n",
      "43.07\n",
      "106.6\n",
      "2.712\n",
      "11.75\n",
      "513.7\n",
      "**Could not find GDP data!**\n",
      "178.7\n",
      "70.36\n",
      "1.317\n",
      "17.47\n",
      "757.3\n",
      "**Could not find GDP data!**\n",
      "**Could not find GDP data!**\n",
      "19.75\n",
      "1.769\n",
      "**Could not find GDP data!**\n",
      "278.2\n",
      "186.8\n",
      "7.928\n",
      "**Could not find GDP data!**\n",
      "11.34\n",
      "521.7\n",
      "516.7\n",
      "50.28\n",
      "1.175\n",
      "27.67\n",
      "162.8\n",
      "1.229\n",
      "6.211\n",
      "12.43\n",
      "**Could not find GDP data!**\n",
      "**Could not find GDP data!**\n",
      "42.78\n",
      "135.9\n",
      "2.133\n",
      "103.5\n",
      "**Could not find GDP data!**\n",
      "**Could not find GDP data!**\n",
      "88.61\n",
      "366.4\n",
      "691.9\n",
      "2.88\n",
      "19.36\n",
      "**Could not find GDP data!**\n",
      "78.41\n",
      "221.7\n",
      "**Could not find GDP data!**\n",
      "389.4\n",
      "643.9\n",
      "3.792\n",
      "**Could not find GDP data!**\n",
      "**Could not find GDP data!**\n",
      "21.22\n",
      "**Could not find GDP data!**\n",
      "68.95\n",
      "68.9\n",
      "33.87\n",
      "\n",
      "Finished finding all GDP (Purchasing Power Parity) (in billion $) data\n"
     ]
    }
   ],
   "source": [
    "# Initialize dictionary for holding the data\n",
    "Total_GDP_PPP = {}\n",
    "# Iterate over every country\n",
    "for i in range(1,len(country_names)-1):\n",
    "    country= country_names[i]\n",
    "    txt=text_data[country]       \n",
    "    pos = txt.find('GDP (purchasing power parity):')\n",
    "    if pos!=-1: \n",
    "        pos= pos+len('GDP (purchasing power parity):')\n",
    "        string = txt[pos+1:pos+15]\n",
    "        start = re.search('\\$',string)\n",
    "        end = re.search('[b,t]',string)\n",
    "        if (start!=None and end!=None):\n",
    "            start=start.start()\n",
    "            end=end.start()\n",
    "            a=string[start+1:start+end-1]\n",
    "            print(a)\n",
    "            a = convert_float(a)\n",
    "            if (string[end]=='t'):\n",
    "                # If the GDP was in trillions, multiply it by 1000\n",
    "                a=1000*a\n",
    "            #print(f\"Total GDP (PPP) of {country}: {a} billion\")\n",
    "            # Insert the data in the dictionary\n",
    "            Total_GDP_PPP[country]=a\n",
    "        else:\n",
    "            print(\"**Could not find GDP data!**\")\n",
    "    else:\n",
    "        print(\"**Could not find GDP data!**\")\n",
    "\n",
    "print (\"\\nFinished finding all GDP (Purchasing Power Parity) (in billion $) data\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Extract GDP/capita (PPP) for every country wherever possible"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GDP/capita (PPP) of  Afghanistan : 1900.0 dollars\n",
      "**Could not find GDP/capita data!**\n",
      "GDP/capita (PPP) of  Albania : 12500.0 dollars\n",
      "GDP/capita (PPP) of  Algeria : 15100.0 dollars\n",
      "GDP/capita (PPP) of  American Samoa : 13000.0 dollars\n",
      "GDP/capita (PPP) of  Andorra : 49900.0 dollars\n",
      "GDP/capita (PPP) of  Angola : 6800.0 dollars\n",
      "GDP/capita (PPP) of  Anguilla : 12200.0 dollars\n",
      "**Could not find GDP/capita data!**\n",
      "GDP/capita (PPP) of  Antigua and Barbuda : 26300.0 dollars\n",
      "**Could not find GDP/capita data!**\n",
      "GDP/capita (PPP) of  Argentina : 20700.0 dollars\n",
      "GDP/capita (PPP) of  Armenia : 9100.0 dollars\n",
      "GDP/capita (PPP) of  Aruba : 25300.0 dollars\n",
      "**Could not find GDP/capita data!**\n",
      "**Could not find GDP/capita data!**\n",
      "GDP/capita (PPP) of  Australia : 49900.0 dollars\n",
      "GDP/capita (PPP) of  Austria : 49200.0 dollars\n",
      "GDP/capita (PPP) of  Azerbaijan : 17400.0 dollars\n",
      "GDP/capita (PPP) of  Bahamas, The : 25100.0 dollars\n",
      "GDP/capita (PPP) of  Bahrain : 51800.0 dollars\n",
      "**Could not find GDP/capita data!**\n",
      "GDP/capita (PPP) of  Bangladesh : 4200.0 dollars\n",
      "GDP/capita (PPP) of  Barbados : 17500.0 dollars\n",
      "GDP/capita (PPP) of  Belarus : 18600.0 dollars\n",
      "GDP/capita (PPP) of  Belgium : 46300.0 dollars\n",
      "GDP/capita (PPP) of  Belize : 8300.0 dollars\n",
      "GDP/capita (PPP) of  Benin : 2200.0 dollars\n",
      "GDP/capita (PPP) of  Bermuda : 85700.0 dollars\n",
      "GDP/capita (PPP) of  Bhutan : 8700.0 dollars\n",
      "GDP/capita (PPP) of  Bolivia : 7500.0 dollars\n",
      "GDP/capita (PPP) of  Bosnia and Herzegovina : 11400.0 dollars\n",
      "GDP/capita (PPP) of  Botswana : 18100.0 dollars\n",
      "**Could not find GDP/capita data!**\n",
      "GDP/capita (PPP) of  Brazil : 15500.0 dollars\n",
      "**Could not find GDP/capita data!**\n",
      "GDP/capita (PPP) of  British Virgin Islands : 42300.0 dollars\n",
      "GDP/capita (PPP) of  Brunei : 76700.0 dollars\n",
      "GDP/capita (PPP) of  Bulgaria : 21600.0 dollars\n",
      "GDP/capita (PPP) of  Burkina Faso : 1900.0 dollars\n",
      "GDP/capita (PPP) of  Burma : 6300.0 dollars\n",
      "GDP/capita (PPP) of  Burundi : 800.0 dollars\n",
      "GDP/capita (PPP) of  Cabo Verde : 6900.0 dollars\n",
      "GDP/capita (PPP) of  Cambodia : 4000.0 dollars\n",
      "GDP/capita (PPP) of  Cameroon : 3400.0 dollars\n",
      "GDP/capita (PPP) of  Canada : 48100.0 dollars\n",
      "GDP/capita (PPP) of  Cayman Islands : 43800.0 dollars\n",
      "GDP/capita (PPP) of  Central African Republic : 700.0 dollars\n",
      "GDP/capita (PPP) of  Chad : 2400.0 dollars\n",
      "GDP/capita (PPP) of  Chile : 24600.0 dollars\n",
      "GDP/capita (PPP) of  China : 16600.0 dollars\n",
      "**Could not find GDP/capita data!**\n",
      "**Could not find GDP/capita data!**\n",
      "**Could not find GDP/capita data!**\n",
      "GDP/capita (PPP) of  Colombia : 14500.0 dollars\n",
      "GDP/capita (PPP) of  Comoros : 1600.0 dollars\n",
      "GDP/capita (PPP) of  Congo, Democratic Republic of the : 800.0 dollars\n",
      "GDP/capita (PPP) of  Congo, Republic of the : 6700.0 dollars\n",
      "GDP/capita (PPP) of  Cook Islands : 12300.0 dollars\n",
      "**Could not find GDP/capita data!**\n",
      "GDP/capita (PPP) of  Costa Rica : 17200.0 dollars\n",
      "GDP/capita (PPP) of  Cote d'Ivoire : 3900.0 dollars\n",
      "GDP/capita (PPP) of  Croatia : 24100.0 dollars\n",
      "GDP/capita (PPP) of  Cuba : 11900.0 dollars\n",
      "GDP/capita (PPP) of  Curacao : 15000.0 dollars\n",
      "GDP/capita (PPP) of  Cyprus : 36600.0 dollars\n",
      "GDP/capita (PPP) of  Czechia : 35200.0 dollars\n",
      "GDP/capita (PPP) of  Denmark : 49600.0 dollars\n",
      "**Could not find GDP/capita data!**\n",
      "GDP/capita (PPP) of  Djibouti : 3600.0 dollars\n",
      "GDP/capita (PPP) of  Dominica : 12000.0 dollars\n",
      "GDP/capita (PPP) of  Dominican Republic : 17000.0 dollars\n",
      "GDP/capita (PPP) of  Ecuador : 11200.0 dollars\n",
      "GDP/capita (PPP) of  Egypt : 13000.0 dollars\n",
      "GDP/capita (PPP) of  El Salvador : 8900.0 dollars\n",
      "GDP/capita (PPP) of  Equatorial Guinea : 34900.0 dollars\n",
      "GDP/capita (PPP) of  Eritrea : 1400.0 dollars\n",
      "GDP/capita (PPP) of  Estonia : 31500.0 dollars\n",
      "GDP/capita (PPP) of  Ethiopia : 2100.0 dollars\n",
      "GDP/capita (PPP) of  Falkland Islands (Islas Malvinas) : 96200.0 dollars\n",
      "GDP/capita (PPP) of  Faroe Islands : 40000.0 dollars\n",
      "GDP/capita (PPP) of  Fiji : 9900.0 dollars\n",
      "GDP/capita (PPP) of  Finland : 44000.0 dollars\n",
      "GDP/capita (PPP) of  France : 43600.0 dollars\n",
      "GDP/capita (PPP) of  French Polynesia : 17000.0 dollars\n",
      "**Could not find GDP/capita data!**\n",
      "GDP/capita (PPP) of  Gabon : 19300.0 dollars\n",
      "GDP/capita (PPP) of  Gambia, The : 1700.0 dollars\n",
      "**Could not find GDP/capita data!**\n",
      "GDP/capita (PPP) of  Georgia : 10600.0 dollars\n",
      "GDP/capita (PPP) of  Germany : 50200.0 dollars\n",
      "GDP/capita (PPP) of  Ghana : 4600.0 dollars\n",
      "GDP/capita (PPP) of  Gibraltar : 61700.0 dollars\n",
      "GDP/capita (PPP) of  Greece : 27800.0 dollars\n",
      "GDP/capita (PPP) of  Greenland : 37600.0 dollars\n",
      "GDP/capita (PPP) of  Grenada : 14700.0 dollars\n",
      "GDP/capita (PPP) of  Guam : 30500.0 dollars\n",
      "GDP/capita (PPP) of  Guatemala : 8200.0 dollars\n",
      "GDP/capita (PPP) of  Guernsey : 52500.0 dollars\n",
      "GDP/capita (PPP) of  Guinea : 2000.0 dollars\n",
      "GDP/capita (PPP) of  Guinea-Bissau : 1800.0 dollars\n",
      "GDP/capita (PPP) of  Guyana : 8300.0 dollars\n",
      "GDP/capita (PPP) of  Haiti : 1800.0 dollars\n",
      "**Could not find GDP/capita data!**\n",
      "**Could not find GDP/capita data!**\n",
      "GDP/capita (PPP) of  Honduras : 5500.0 dollars\n",
      "GDP/capita (PPP) of  Hong Kong : 61000.0 dollars\n",
      "**Could not find GDP/capita data!**\n",
      "GDP/capita (PPP) of  Hungary : 28900.0 dollars\n",
      "GDP/capita (PPP) of  Iceland : 52100.0 dollars\n",
      "GDP/capita (PPP) of  India : 7200.0 dollars\n",
      "**Could not find GDP/capita data!**\n",
      "GDP/capita (PPP) of  Indonesia : 12400.0 dollars\n",
      "GDP/capita (PPP) of  Iran : 20000.0 dollars\n",
      "GDP/capita (PPP) of  Iraq : 17000.0 dollars\n",
      "GDP/capita (PPP) of  Ireland : 72600.0 dollars\n",
      "GDP/capita (PPP) of  Isle of Man : 84600.0 dollars\n",
      "GDP/capita (PPP) of  Israel : 36200.0 dollars\n",
      "GDP/capita (PPP) of  Italy : 38000.0 dollars\n",
      "GDP/capita (PPP) of  Jamaica : 9200.0 dollars\n",
      "**Could not find GDP/capita data!**\n",
      "GDP/capita (PPP) of  Japan : 42700.0 dollars\n",
      "**Could not find GDP/capita data!**\n",
      "GDP/capita (PPP) of  Jersey : 49500.0 dollars\n",
      "**Could not find GDP/capita data!**\n",
      "GDP/capita (PPP) of  Jordan : 12500.0 dollars\n",
      "GDP/capita (PPP) of  Kazakhstan : 26100.0 dollars\n",
      "GDP/capita (PPP) of  Kenya : 3500.0 dollars\n",
      "**Could not find GDP/capita data!**\n",
      "GDP/capita (PPP) of  Kiribati : 1900.0 dollars\n",
      "GDP/capita (PPP) of  Korea, North : 1700.0 dollars\n",
      "GDP/capita (PPP) of  Korea, South : 39400.0 dollars\n",
      "GDP/capita (PPP) of  Kosovo : 10400.0 dollars\n",
      "GDP/capita (PPP) of  Kuwait : 69700.0 dollars\n",
      "GDP/capita (PPP) of  Kyrgyzstan : 3700.0 dollars\n",
      "GDP/capita (PPP) of  Laos : 7400.0 dollars\n",
      "GDP/capita (PPP) of  Latvia : 27300.0 dollars\n",
      "GDP/capita (PPP) of  Lebanon : 19500.0 dollars\n",
      "GDP/capita (PPP) of  Lesotho : 3900.0 dollars\n",
      "GDP/capita (PPP) of  Liberia : 900.0 dollars\n",
      "GDP/capita (PPP) of  Libya : 9800.0 dollars\n",
      "GDP/capita (PPP) of  Liechtenstein : 139100.0 dollars\n",
      "GDP/capita (PPP) of  Lithuania : 31900.0 dollars\n",
      "GDP/capita (PPP) of  Luxembourg : 109100.0 dollars\n",
      "GDP/capita (PPP) of  Macau : 114400.0 dollars\n",
      "GDP/capita (PPP) of  Macedonia : 15200.0 dollars\n",
      "GDP/capita (PPP) of  Madagascar : 1600.0 dollars\n",
      "GDP/capita (PPP) of  Malawi : 1200.0 dollars\n",
      "GDP/capita (PPP) of  Malaysia : 28900.0 dollars\n",
      "GDP/capita (PPP) of  Maldives : 19200.0 dollars\n",
      "GDP/capita (PPP) of  Mali : 2200.0 dollars\n",
      "GDP/capita (PPP) of  Malta : 42500.0 dollars\n",
      "GDP/capita (PPP) of  Marshall Islands : 3400.0 dollars\n",
      "GDP/capita (PPP) of  Mauritania : 4500.0 dollars\n",
      "GDP/capita (PPP) of  Mauritius : 21600.0 dollars\n",
      "GDP/capita (PPP) of  Mexico : 19500.0 dollars\n",
      "GDP/capita (PPP) of  Micronesia, Federated States of : 3400.0 dollars\n",
      "**Could not find GDP/capita data!**\n",
      "GDP/capita (PPP) of  Moldova : 5700.0 dollars\n",
      "GDP/capita (PPP) of  Monaco : 115700.0 dollars\n",
      "GDP/capita (PPP) of  Mongolia : 12600.0 dollars\n",
      "GDP/capita (PPP) of  Montenegro : 17400.0 dollars\n",
      "GDP/capita (PPP) of  Montserrat : 8500.0 dollars\n",
      "GDP/capita (PPP) of  Morocco : 8600.0 dollars\n",
      "GDP/capita (PPP) of  Mozambique : 1300.0 dollars\n",
      "GDP/capita (PPP) of  Namibia : 11500.0 dollars\n",
      "GDP/capita (PPP) of  Nauru : 12200.0 dollars\n",
      "**Could not find GDP/capita data!**\n",
      "GDP/capita (PPP) of  Nepal : 2700.0 dollars\n",
      "GDP/capita (PPP) of  Netherlands : 53600.0 dollars\n",
      "GDP/capita (PPP) of  New Caledonia : 31100.0 dollars\n",
      "GDP/capita (PPP) of  New Zealand : 38500.0 dollars\n",
      "GDP/capita (PPP) of  Nicaragua : 5800.0 dollars\n",
      "GDP/capita (PPP) of  Niger : 1200.0 dollars\n",
      "GDP/capita (PPP) of  Nigeria : 5900.0 dollars\n",
      "GDP/capita (PPP) of  Niue : 5800.0 dollars\n",
      "**Could not find GDP/capita data!**\n",
      "GDP/capita (PPP) of  Northern Mariana Islands : 13300.0 dollars\n",
      "GDP/capita (PPP) of  Norway : 70600.0 dollars\n",
      "GDP/capita (PPP) of  Oman : 45500.0 dollars\n",
      "**Could not find GDP/capita data!**\n",
      "GDP/capita (PPP) of  Pakistan : 5400.0 dollars\n",
      "GDP/capita (PPP) of  Palau : 16700.0 dollars\n",
      "**Could not find GDP/capita data!**\n",
      "GDP/capita (PPP) of  Panama : 24300.0 dollars\n",
      "GDP/capita (PPP) of  Papua New Guinea : 3800.0 dollars\n",
      "**Could not find GDP/capita data!**\n",
      "GDP/capita (PPP) of  Paraguay : 9800.0 dollars\n",
      "GDP/capita (PPP) of  Peru : 13300.0 dollars\n",
      "GDP/capita (PPP) of  Philippines : 8200.0 dollars\n",
      "**Could not find GDP/capita data!**\n",
      "GDP/capita (PPP) of  Poland : 29300.0 dollars\n",
      "GDP/capita (PPP) of  Portugal : 30300.0 dollars\n",
      "GDP/capita (PPP) of  Puerto Rico : 37900.0 dollars\n",
      "GDP/capita (PPP) of  Qatar : 124900.0 dollars\n",
      "GDP/capita (PPP) of  Romania : 24000.0 dollars\n",
      "GDP/capita (PPP) of  Russia : 27900.0 dollars\n",
      "GDP/capita (PPP) of  Rwanda : 2100.0 dollars\n",
      "**Could not find GDP/capita data!**\n",
      "GDP/capita (PPP) of  Saint Helena, Ascension, and Tristan da Cunha : 7800.0 dollars\n",
      "GDP/capita (PPP) of  Saint Kitts and Nevis : 26800.0 dollars\n",
      "GDP/capita (PPP) of  Saint Lucia : 13500.0 dollars\n",
      "GDP/capita (PPP) of  Saint Martin : 19300.0 dollars\n",
      "GDP/capita (PPP) of  Saint Pierre and Miquelon : 34900.0 dollars\n",
      "GDP/capita (PPP) of  Saint Vincent and the Grenadines : 11600.0 dollars\n",
      "GDP/capita (PPP) of  Samoa : 5700.0 dollars\n",
      "GDP/capita (PPP) of  San Marino : 59500.0 dollars\n",
      "GDP/capita (PPP) of  Sao Tome and Principe : 3200.0 dollars\n",
      "GDP/capita (PPP) of  Saudi Arabia : 55300.0 dollars\n",
      "GDP/capita (PPP) of  Senegal : 2700.0 dollars\n",
      "GDP/capita (PPP) of  Serbia : 15200.0 dollars\n",
      "GDP/capita (PPP) of  Seychelles : 28900.0 dollars\n",
      "GDP/capita (PPP) of  Sierra Leone : 1800.0 dollars\n",
      "GDP/capita (PPP) of  Singapore : 90500.0 dollars\n",
      "GDP/capita (PPP) of  Sint Maarten : 66800.0 dollars\n",
      "GDP/capita (PPP) of  Slovakia : 32900.0 dollars\n",
      "GDP/capita (PPP) of  Slovenia : 34100.0 dollars\n",
      "GDP/capita (PPP) of  Solomon Islands : 2100.0 dollars\n",
      "**Could not find GDP/capita data!**\n",
      "GDP/capita (PPP) of  South Africa : 13400.0 dollars\n",
      "**Could not find GDP/capita data!**\n",
      "**Could not find GDP/capita data!**\n",
      "GDP/capita (PPP) of  South Sudan : 1500.0 dollars\n",
      "GDP/capita (PPP) of  Spain : 38200.0 dollars\n",
      "**Could not find GDP/capita data!**\n",
      "GDP/capita (PPP) of  Sri Lanka : 13000.0 dollars\n",
      "GDP/capita (PPP) of  Sudan : 4600.0 dollars\n",
      "GDP/capita (PPP) of  Suriname : 13900.0 dollars\n",
      "**Could not find GDP/capita data!**\n",
      "GDP/capita (PPP) of  Swaziland : 9900.0 dollars\n",
      "GDP/capita (PPP) of  Sweden : 51300.0 dollars\n",
      "GDP/capita (PPP) of  Switzerland : 61400.0 dollars\n",
      "GDP/capita (PPP) of  Syria : 2900.0 dollars\n",
      "GDP/capita (PPP) of  Taiwan : 49800.0 dollars\n",
      "GDP/capita (PPP) of  Tajikistan : 3100.0 dollars\n",
      "GDP/capita (PPP) of  Tanzania : 3300.0 dollars\n",
      "GDP/capita (PPP) of  Thailand : 17800.0 dollars\n",
      "GDP/capita (PPP) of  Timor-Leste : 5000.0 dollars\n",
      "GDP/capita (PPP) of  Togo : 1600.0 dollars\n",
      "GDP/capita (PPP) of  Tokelau : 1000.0 dollars\n",
      "GDP/capita (PPP) of  Tonga : 5600.0 dollars\n",
      "GDP/capita (PPP) of  Trinidad and Tobago : 31200.0 dollars\n",
      "GDP/capita (PPP) of  Tunisia : 12000.0 dollars\n",
      "GDP/capita (PPP) of  Turkey : 26500.0 dollars\n",
      "GDP/capita (PPP) of  Turkmenistan : 18700.0 dollars\n",
      "GDP/capita (PPP) of  Turks and Caicos Islands : 29100.0 dollars\n",
      "GDP/capita (PPP) of  Tuvalu : 3800.0 dollars\n",
      "GDP/capita (PPP) of  Uganda : 2400.0 dollars\n",
      "GDP/capita (PPP) of  Ukraine : 8700.0 dollars\n",
      "GDP/capita (PPP) of  United Arab Emirates : 68200.0 dollars\n",
      "GDP/capita (PPP) of  United Kingdom : 43600.0 dollars\n",
      "GDP/capita (PPP) of  United States : 59500.0 dollars\n",
      "**Could not find GDP/capita data!**\n",
      "GDP/capita (PPP) of  Uruguay : 22400.0 dollars\n",
      "GDP/capita (PPP) of  Uzbekistan : 7000.0 dollars\n",
      "GDP/capita (PPP) of  Vanuatu : 2800.0 dollars\n",
      "GDP/capita (PPP) of  Venezuela : 12400.0 dollars\n",
      "GDP/capita (PPP) of  Vietnam : 6900.0 dollars\n",
      "GDP/capita (PPP) of  Virgin Islands : 36100.0 dollars\n",
      "**Could not find GDP/capita data!**\n",
      "GDP/capita (PPP) of  Wallis and Futuna : 3800.0 dollars\n",
      "GDP/capita (PPP) of  West Bank : 4300.0 dollars\n",
      "GDP/capita (PPP) of  Western Sahara : 2500.0 dollars\n",
      "GDP/capita (PPP) of  Yemen : 2300.0 dollars\n",
      "GDP/capita (PPP) of  Zambia : 4000.0 dollars\n",
      "GDP/capita (PPP) of  Zimbabwe : 2300.0 dollars\n",
      "\n",
      "Finished finding all GDP/capita data\n"
     ]
    }
   ],
   "source": [
    "# Initialize dictionary for holding the data\n",
    "GDP_PPP = {}\n",
    "# Iterate over every country\n",
    "for i in range(1,len(country_names)-1):\n",
    "    country= country_names[i]\n",
    "    txt=text_data[country]       \n",
    "    pos = txt.find('GDP - per capita (PPP):')\n",
    "    if pos!=-1:\n",
    "        pos= pos+len('GDP - per capita (PPP):')\n",
    "        string = txt[pos+1:pos+11]\n",
    "        start = re.search('\\$',string)\n",
    "        end = re.search('\\S',string)\n",
    "        if (start!=None and end!=None):\n",
    "            start=start.start()\n",
    "            end=end.start()\n",
    "            a=string[start+1:start+end-1]\n",
    "            #print(a)\n",
    "            a = convert_float(a)\n",
    "            if (a!=-1.0):\n",
    "                print(f\"GDP/capita (PPP) of {country}: {a} dollars\")\n",
    "                # Insert the data in the dictionary\n",
    "                GDP_PPP[country]=a\n",
    "            else:\n",
    "                print(\"**Could not find GDP/capita data!**\")\n",
    "        else:\n",
    "            print(\"**Could not find GDP/capita data!**\")\n",
    "    else:\n",
    "        print(\"**Could not find GDP/capita data!**\")\n",
    "\n",
    "print (\"\\nFinished finding all GDP/capita data\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_GDP=pd.Series(GDP_PPP).to_frame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_GDP.columns=['GDP (PPP)']\n",
    "df_GDP.index.name='COUNTRY'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GDP (PPP)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COUNTRY</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Afghanistan</th>\n",
       "      <td>1900.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>12500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>15100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>American Samoa</th>\n",
       "      <td>13000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andorra</th>\n",
       "      <td>49900.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Angola</th>\n",
       "      <td>6800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Anguilla</th>\n",
       "      <td>12200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Antigua and Barbuda</th>\n",
       "      <td>26300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Argentina</th>\n",
       "      <td>20700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Armenia</th>\n",
       "      <td>9100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aruba</th>\n",
       "      <td>25300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <td>49900.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Austria</th>\n",
       "      <td>49200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Azerbaijan</th>\n",
       "      <td>17400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bahamas, The</th>\n",
       "      <td>25100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bahrain</th>\n",
       "      <td>51800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bangladesh</th>\n",
       "      <td>4200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Barbados</th>\n",
       "      <td>17500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belarus</th>\n",
       "      <td>18600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belgium</th>\n",
       "      <td>46300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belize</th>\n",
       "      <td>8300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Benin</th>\n",
       "      <td>2200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bermuda</th>\n",
       "      <td>85700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bhutan</th>\n",
       "      <td>8700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bolivia</th>\n",
       "      <td>7500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bosnia and Herzegovina</th>\n",
       "      <td>11400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Botswana</th>\n",
       "      <td>18100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brazil</th>\n",
       "      <td>15500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>British Virgin Islands</th>\n",
       "      <td>42300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brunei</th>\n",
       "      <td>76700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tajikistan</th>\n",
       "      <td>3100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tanzania</th>\n",
       "      <td>3300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thailand</th>\n",
       "      <td>17800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Timor-Leste</th>\n",
       "      <td>5000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Togo</th>\n",
       "      <td>1600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tokelau</th>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tonga</th>\n",
       "      <td>5600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Trinidad and Tobago</th>\n",
       "      <td>31200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tunisia</th>\n",
       "      <td>12000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Turkey</th>\n",
       "      <td>26500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Turkmenistan</th>\n",
       "      <td>18700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Turks and Caicos Islands</th>\n",
       "      <td>29100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tuvalu</th>\n",
       "      <td>3800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uganda</th>\n",
       "      <td>2400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ukraine</th>\n",
       "      <td>8700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Arab Emirates</th>\n",
       "      <td>68200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>43600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>59500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uruguay</th>\n",
       "      <td>22400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uzbekistan</th>\n",
       "      <td>7000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vanuatu</th>\n",
       "      <td>2800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Venezuela</th>\n",
       "      <td>12400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vietnam</th>\n",
       "      <td>6900.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Virgin Islands</th>\n",
       "      <td>36100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wallis and Futuna</th>\n",
       "      <td>3800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>West Bank</th>\n",
       "      <td>4300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Western Sahara</th>\n",
       "      <td>2500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yemen</th>\n",
       "      <td>2300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zambia</th>\n",
       "      <td>4000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zimbabwe</th>\n",
       "      <td>2300.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>228 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                            GDP (PPP)\n",
       "COUNTRY                              \n",
       " Afghanistan                   1900.0\n",
       " Albania                      12500.0\n",
       " Algeria                      15100.0\n",
       " American Samoa               13000.0\n",
       " Andorra                      49900.0\n",
       " Angola                        6800.0\n",
       " Anguilla                     12200.0\n",
       " Antigua and Barbuda          26300.0\n",
       " Argentina                    20700.0\n",
       " Armenia                       9100.0\n",
       " Aruba                        25300.0\n",
       " Australia                    49900.0\n",
       " Austria                      49200.0\n",
       " Azerbaijan                   17400.0\n",
       " Bahamas, The                 25100.0\n",
       " Bahrain                      51800.0\n",
       " Bangladesh                    4200.0\n",
       " Barbados                     17500.0\n",
       " Belarus                      18600.0\n",
       " Belgium                      46300.0\n",
       " Belize                        8300.0\n",
       " Benin                         2200.0\n",
       " Bermuda                      85700.0\n",
       " Bhutan                        8700.0\n",
       " Bolivia                       7500.0\n",
       " Bosnia and Herzegovina       11400.0\n",
       " Botswana                     18100.0\n",
       " Brazil                       15500.0\n",
       " British Virgin Islands       42300.0\n",
       " Brunei                       76700.0\n",
       "...                               ...\n",
       " Tajikistan                    3100.0\n",
       " Tanzania                      3300.0\n",
       " Thailand                     17800.0\n",
       " Timor-Leste                   5000.0\n",
       " Togo                          1600.0\n",
       " Tokelau                       1000.0\n",
       " Tonga                         5600.0\n",
       " Trinidad and Tobago          31200.0\n",
       " Tunisia                      12000.0\n",
       " Turkey                       26500.0\n",
       " Turkmenistan                 18700.0\n",
       " Turks and Caicos Islands     29100.0\n",
       " Tuvalu                        3800.0\n",
       " Uganda                        2400.0\n",
       " Ukraine                       8700.0\n",
       " United Arab Emirates         68200.0\n",
       " United Kingdom               43600.0\n",
       " United States                59500.0\n",
       " Uruguay                      22400.0\n",
       " Uzbekistan                    7000.0\n",
       " Vanuatu                       2800.0\n",
       " Venezuela                    12400.0\n",
       " Vietnam                       6900.0\n",
       " Virgin Islands               36100.0\n",
       " Wallis and Futuna             3800.0\n",
       " West Bank                     4300.0\n",
       " Western Sahara                2500.0\n",
       " Yemen                         2300.0\n",
       " Zambia                        4000.0\n",
       " Zimbabwe                      2300.0\n",
       "\n",
       "[228 rows x 1 columns]"
      ]
     },
     "execution_count": 230,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_GDP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_Total_GDP=pd.Series(Total_GDP_PPP).to_frame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_Total_GDP.columns=['Total GDP (PPP)']\n",
    "df_Total_GDP.index.name='COUNTRY'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_combined = df_demo.join(df_GDP)\n",
    "df_combined.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_combined = df_combined.join(df_Total_GDP)\n",
    "df_combined.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0-14 years old %</th>\n",
       "      <th>15-24 years %</th>\n",
       "      <th>25-54 years %</th>\n",
       "      <th>55-64 years %</th>\n",
       "      <th>65 years and above %</th>\n",
       "      <th>GDP (PPP)</th>\n",
       "      <th>Total GDP (PPP)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COUNTRY</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Afghanistan</th>\n",
       "      <td>40.92</td>\n",
       "      <td>22.22</td>\n",
       "      <td>30.35</td>\n",
       "      <td>3.92</td>\n",
       "      <td>2.59</td>\n",
       "      <td>1900.0</td>\n",
       "      <td>69.510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>18.05</td>\n",
       "      <td>17.47</td>\n",
       "      <td>41.06</td>\n",
       "      <td>11.54</td>\n",
       "      <td>11.89</td>\n",
       "      <td>12500.0</td>\n",
       "      <td>35.870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>29.31</td>\n",
       "      <td>15.30</td>\n",
       "      <td>42.93</td>\n",
       "      <td>6.81</td>\n",
       "      <td>5.65</td>\n",
       "      <td>15100.0</td>\n",
       "      <td>629.300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andorra</th>\n",
       "      <td>14.40</td>\n",
       "      <td>9.64</td>\n",
       "      <td>46.18</td>\n",
       "      <td>14.16</td>\n",
       "      <td>15.61</td>\n",
       "      <td>49900.0</td>\n",
       "      <td>3.327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Angola</th>\n",
       "      <td>48.12</td>\n",
       "      <td>18.25</td>\n",
       "      <td>28.03</td>\n",
       "      <td>3.26</td>\n",
       "      <td>2.34</td>\n",
       "      <td>6800.0</td>\n",
       "      <td>192.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Antigua and Barbuda</th>\n",
       "      <td>23.09</td>\n",
       "      <td>16.83</td>\n",
       "      <td>42.19</td>\n",
       "      <td>9.83</td>\n",
       "      <td>8.06</td>\n",
       "      <td>26300.0</td>\n",
       "      <td>2.390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Argentina</th>\n",
       "      <td>24.59</td>\n",
       "      <td>15.28</td>\n",
       "      <td>39.38</td>\n",
       "      <td>9.13</td>\n",
       "      <td>11.62</td>\n",
       "      <td>20700.0</td>\n",
       "      <td>911.500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Armenia</th>\n",
       "      <td>18.94</td>\n",
       "      <td>12.89</td>\n",
       "      <td>43.43</td>\n",
       "      <td>13.41</td>\n",
       "      <td>11.33</td>\n",
       "      <td>9100.0</td>\n",
       "      <td>27.210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aruba</th>\n",
       "      <td>17.64</td>\n",
       "      <td>12.78</td>\n",
       "      <td>41.72</td>\n",
       "      <td>14.28</td>\n",
       "      <td>13.59</td>\n",
       "      <td>25300.0</td>\n",
       "      <td>2.516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <td>17.80</td>\n",
       "      <td>12.79</td>\n",
       "      <td>41.45</td>\n",
       "      <td>11.83</td>\n",
       "      <td>16.14</td>\n",
       "      <td>49900.0</td>\n",
       "      <td>1235.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Austria</th>\n",
       "      <td>14.01</td>\n",
       "      <td>11.07</td>\n",
       "      <td>42.42</td>\n",
       "      <td>13.23</td>\n",
       "      <td>19.26</td>\n",
       "      <td>49200.0</td>\n",
       "      <td>434.100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Azerbaijan</th>\n",
       "      <td>22.95</td>\n",
       "      <td>14.84</td>\n",
       "      <td>45.39</td>\n",
       "      <td>10.17</td>\n",
       "      <td>6.64</td>\n",
       "      <td>17400.0</td>\n",
       "      <td>166.800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bahamas, The</th>\n",
       "      <td>22.55</td>\n",
       "      <td>16.40</td>\n",
       "      <td>44.14</td>\n",
       "      <td>9.16</td>\n",
       "      <td>7.75</td>\n",
       "      <td>25100.0</td>\n",
       "      <td>9.339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bahrain</th>\n",
       "      <td>19.08</td>\n",
       "      <td>15.65</td>\n",
       "      <td>56.04</td>\n",
       "      <td>6.28</td>\n",
       "      <td>2.95</td>\n",
       "      <td>51800.0</td>\n",
       "      <td>69.770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bangladesh</th>\n",
       "      <td>27.76</td>\n",
       "      <td>19.36</td>\n",
       "      <td>39.73</td>\n",
       "      <td>6.93</td>\n",
       "      <td>6.23</td>\n",
       "      <td>4200.0</td>\n",
       "      <td>686.500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Barbados</th>\n",
       "      <td>17.97</td>\n",
       "      <td>12.74</td>\n",
       "      <td>44.06</td>\n",
       "      <td>13.43</td>\n",
       "      <td>11.81</td>\n",
       "      <td>17500.0</td>\n",
       "      <td>4.919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belarus</th>\n",
       "      <td>15.78</td>\n",
       "      <td>10.29</td>\n",
       "      <td>44.76</td>\n",
       "      <td>14.21</td>\n",
       "      <td>14.95</td>\n",
       "      <td>18600.0</td>\n",
       "      <td>175.900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belgium</th>\n",
       "      <td>17.16</td>\n",
       "      <td>11.34</td>\n",
       "      <td>40.05</td>\n",
       "      <td>12.86</td>\n",
       "      <td>18.58</td>\n",
       "      <td>46300.0</td>\n",
       "      <td>526.400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belize</th>\n",
       "      <td>33.95</td>\n",
       "      <td>20.55</td>\n",
       "      <td>36.62</td>\n",
       "      <td>4.99</td>\n",
       "      <td>3.89</td>\n",
       "      <td>8300.0</td>\n",
       "      <td>3.230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Benin</th>\n",
       "      <td>42.65</td>\n",
       "      <td>20.44</td>\n",
       "      <td>30.44</td>\n",
       "      <td>3.61</td>\n",
       "      <td>2.87</td>\n",
       "      <td>2200.0</td>\n",
       "      <td>25.290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bermuda</th>\n",
       "      <td>17.04</td>\n",
       "      <td>12.00</td>\n",
       "      <td>37.24</td>\n",
       "      <td>15.73</td>\n",
       "      <td>17.98</td>\n",
       "      <td>85700.0</td>\n",
       "      <td>5.198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bhutan</th>\n",
       "      <td>25.80</td>\n",
       "      <td>18.81</td>\n",
       "      <td>43.07</td>\n",
       "      <td>6.03</td>\n",
       "      <td>6.29</td>\n",
       "      <td>8700.0</td>\n",
       "      <td>7.011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bolivia</th>\n",
       "      <td>31.85</td>\n",
       "      <td>19.46</td>\n",
       "      <td>37.48</td>\n",
       "      <td>5.90</td>\n",
       "      <td>5.30</td>\n",
       "      <td>7500.0</td>\n",
       "      <td>83.500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bosnia and Herzegovina</th>\n",
       "      <td>13.29</td>\n",
       "      <td>11.58</td>\n",
       "      <td>45.88</td>\n",
       "      <td>14.83</td>\n",
       "      <td>14.43</td>\n",
       "      <td>11400.0</td>\n",
       "      <td>43.850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Botswana</th>\n",
       "      <td>31.95</td>\n",
       "      <td>18.91</td>\n",
       "      <td>38.45</td>\n",
       "      <td>5.46</td>\n",
       "      <td>5.23</td>\n",
       "      <td>18100.0</td>\n",
       "      <td>39.550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brazil</th>\n",
       "      <td>22.33</td>\n",
       "      <td>16.36</td>\n",
       "      <td>43.86</td>\n",
       "      <td>9.12</td>\n",
       "      <td>8.33</td>\n",
       "      <td>15500.0</td>\n",
       "      <td>3219.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brunei</th>\n",
       "      <td>23.12</td>\n",
       "      <td>17.05</td>\n",
       "      <td>46.75</td>\n",
       "      <td>8.23</td>\n",
       "      <td>4.84</td>\n",
       "      <td>76700.0</td>\n",
       "      <td>32.910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bulgaria</th>\n",
       "      <td>14.58</td>\n",
       "      <td>9.58</td>\n",
       "      <td>43.21</td>\n",
       "      <td>13.35</td>\n",
       "      <td>19.28</td>\n",
       "      <td>21600.0</td>\n",
       "      <td>152.400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Burkina Faso</th>\n",
       "      <td>44.88</td>\n",
       "      <td>20.07</td>\n",
       "      <td>29.42</td>\n",
       "      <td>3.20</td>\n",
       "      <td>2.43</td>\n",
       "      <td>1900.0</td>\n",
       "      <td>35.680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Burma</th>\n",
       "      <td>26.85</td>\n",
       "      <td>17.75</td>\n",
       "      <td>42.36</td>\n",
       "      <td>7.52</td>\n",
       "      <td>5.53</td>\n",
       "      <td>6300.0</td>\n",
       "      <td>330.900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sudan</th>\n",
       "      <td>38.68</td>\n",
       "      <td>21.04</td>\n",
       "      <td>32.77</td>\n",
       "      <td>4.24</td>\n",
       "      <td>3.27</td>\n",
       "      <td>4600.0</td>\n",
       "      <td>186.800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Suriname</th>\n",
       "      <td>24.62</td>\n",
       "      <td>17.44</td>\n",
       "      <td>44.40</td>\n",
       "      <td>7.54</td>\n",
       "      <td>6.01</td>\n",
       "      <td>13900.0</td>\n",
       "      <td>7.928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Swaziland</th>\n",
       "      <td>35.01</td>\n",
       "      <td>22.12</td>\n",
       "      <td>34.60</td>\n",
       "      <td>4.30</td>\n",
       "      <td>3.97</td>\n",
       "      <td>9900.0</td>\n",
       "      <td>11.340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sweden</th>\n",
       "      <td>17.43</td>\n",
       "      <td>11.31</td>\n",
       "      <td>39.42</td>\n",
       "      <td>11.58</td>\n",
       "      <td>20.26</td>\n",
       "      <td>51300.0</td>\n",
       "      <td>521.700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Switzerland</th>\n",
       "      <td>15.16</td>\n",
       "      <td>10.88</td>\n",
       "      <td>43.21</td>\n",
       "      <td>12.60</td>\n",
       "      <td>18.15</td>\n",
       "      <td>61400.0</td>\n",
       "      <td>516.700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Syria</th>\n",
       "      <td>31.62</td>\n",
       "      <td>19.54</td>\n",
       "      <td>39.22</td>\n",
       "      <td>5.41</td>\n",
       "      <td>4.21</td>\n",
       "      <td>2900.0</td>\n",
       "      <td>50.280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Taiwan</th>\n",
       "      <td>12.88</td>\n",
       "      <td>12.88</td>\n",
       "      <td>46.41</td>\n",
       "      <td>14.12</td>\n",
       "      <td>13.72</td>\n",
       "      <td>49800.0</td>\n",
       "      <td>1175.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tajikistan</th>\n",
       "      <td>32.33</td>\n",
       "      <td>18.61</td>\n",
       "      <td>40.12</td>\n",
       "      <td>5.62</td>\n",
       "      <td>3.32</td>\n",
       "      <td>3100.0</td>\n",
       "      <td>27.670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tanzania</th>\n",
       "      <td>43.74</td>\n",
       "      <td>19.86</td>\n",
       "      <td>29.88</td>\n",
       "      <td>3.51</td>\n",
       "      <td>3.02</td>\n",
       "      <td>3300.0</td>\n",
       "      <td>162.800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thailand</th>\n",
       "      <td>16.93</td>\n",
       "      <td>14.17</td>\n",
       "      <td>46.32</td>\n",
       "      <td>12.00</td>\n",
       "      <td>10.58</td>\n",
       "      <td>17800.0</td>\n",
       "      <td>1229.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Timor-Leste</th>\n",
       "      <td>40.91</td>\n",
       "      <td>20.32</td>\n",
       "      <td>29.95</td>\n",
       "      <td>4.94</td>\n",
       "      <td>3.87</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>6.211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Togo</th>\n",
       "      <td>40.29</td>\n",
       "      <td>19.20</td>\n",
       "      <td>32.79</td>\n",
       "      <td>4.31</td>\n",
       "      <td>3.41</td>\n",
       "      <td>1600.0</td>\n",
       "      <td>12.430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Trinidad and Tobago</th>\n",
       "      <td>19.29</td>\n",
       "      <td>11.88</td>\n",
       "      <td>45.56</td>\n",
       "      <td>12.61</td>\n",
       "      <td>10.65</td>\n",
       "      <td>31200.0</td>\n",
       "      <td>42.780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tunisia</th>\n",
       "      <td>25.15</td>\n",
       "      <td>13.99</td>\n",
       "      <td>43.38</td>\n",
       "      <td>9.54</td>\n",
       "      <td>7.95</td>\n",
       "      <td>12000.0</td>\n",
       "      <td>135.900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Turkey</th>\n",
       "      <td>24.68</td>\n",
       "      <td>15.99</td>\n",
       "      <td>43.21</td>\n",
       "      <td>8.58</td>\n",
       "      <td>7.53</td>\n",
       "      <td>26500.0</td>\n",
       "      <td>2133.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Turkmenistan</th>\n",
       "      <td>25.79</td>\n",
       "      <td>18.39</td>\n",
       "      <td>43.18</td>\n",
       "      <td>7.90</td>\n",
       "      <td>4.74</td>\n",
       "      <td>18700.0</td>\n",
       "      <td>103.500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uganda</th>\n",
       "      <td>48.05</td>\n",
       "      <td>21.10</td>\n",
       "      <td>26.30</td>\n",
       "      <td>2.57</td>\n",
       "      <td>1.98</td>\n",
       "      <td>2400.0</td>\n",
       "      <td>88.610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ukraine</th>\n",
       "      <td>15.76</td>\n",
       "      <td>9.86</td>\n",
       "      <td>44.29</td>\n",
       "      <td>13.80</td>\n",
       "      <td>16.30</td>\n",
       "      <td>8700.0</td>\n",
       "      <td>366.400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Arab Emirates</th>\n",
       "      <td>21.01</td>\n",
       "      <td>13.51</td>\n",
       "      <td>61.14</td>\n",
       "      <td>3.27</td>\n",
       "      <td>1.07</td>\n",
       "      <td>68200.0</td>\n",
       "      <td>691.900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>17.53</td>\n",
       "      <td>11.90</td>\n",
       "      <td>40.55</td>\n",
       "      <td>11.98</td>\n",
       "      <td>18.04</td>\n",
       "      <td>43600.0</td>\n",
       "      <td>2880.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>18.73</td>\n",
       "      <td>13.27</td>\n",
       "      <td>39.45</td>\n",
       "      <td>12.91</td>\n",
       "      <td>15.63</td>\n",
       "      <td>59500.0</td>\n",
       "      <td>19360.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uruguay</th>\n",
       "      <td>20.17</td>\n",
       "      <td>15.69</td>\n",
       "      <td>39.34</td>\n",
       "      <td>10.56</td>\n",
       "      <td>14.25</td>\n",
       "      <td>22400.0</td>\n",
       "      <td>78.410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uzbekistan</th>\n",
       "      <td>23.88</td>\n",
       "      <td>18.52</td>\n",
       "      <td>44.49</td>\n",
       "      <td>7.85</td>\n",
       "      <td>5.25</td>\n",
       "      <td>7000.0</td>\n",
       "      <td>221.700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Venezuela</th>\n",
       "      <td>27.36</td>\n",
       "      <td>17.03</td>\n",
       "      <td>40.53</td>\n",
       "      <td>7.98</td>\n",
       "      <td>7.09</td>\n",
       "      <td>12400.0</td>\n",
       "      <td>389.400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vietnam</th>\n",
       "      <td>23.55</td>\n",
       "      <td>16.23</td>\n",
       "      <td>45.56</td>\n",
       "      <td>8.55</td>\n",
       "      <td>6.12</td>\n",
       "      <td>6900.0</td>\n",
       "      <td>643.900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Virgin Islands</th>\n",
       "      <td>20.41</td>\n",
       "      <td>11.19</td>\n",
       "      <td>37.07</td>\n",
       "      <td>13.78</td>\n",
       "      <td>17.55</td>\n",
       "      <td>36100.0</td>\n",
       "      <td>3.792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>West Bank</th>\n",
       "      <td>36.50</td>\n",
       "      <td>21.37</td>\n",
       "      <td>34.10</td>\n",
       "      <td>4.54</td>\n",
       "      <td>3.49</td>\n",
       "      <td>4300.0</td>\n",
       "      <td>21.220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yemen</th>\n",
       "      <td>39.83</td>\n",
       "      <td>21.21</td>\n",
       "      <td>32.27</td>\n",
       "      <td>3.94</td>\n",
       "      <td>2.75</td>\n",
       "      <td>2300.0</td>\n",
       "      <td>68.950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zambia</th>\n",
       "      <td>46.03</td>\n",
       "      <td>20.00</td>\n",
       "      <td>28.72</td>\n",
       "      <td>2.93</td>\n",
       "      <td>2.33</td>\n",
       "      <td>4000.0</td>\n",
       "      <td>68.900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zimbabwe</th>\n",
       "      <td>38.90</td>\n",
       "      <td>20.47</td>\n",
       "      <td>31.90</td>\n",
       "      <td>4.27</td>\n",
       "      <td>4.46</td>\n",
       "      <td>2300.0</td>\n",
       "      <td>33.870</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>202 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                          0-14 years old %  15-24 years %  25-54 years %  \\\n",
       "COUNTRY                                                                    \n",
       " Afghanistan                         40.92          22.22          30.35   \n",
       " Albania                             18.05          17.47          41.06   \n",
       " Algeria                             29.31          15.30          42.93   \n",
       " Andorra                             14.40           9.64          46.18   \n",
       " Angola                              48.12          18.25          28.03   \n",
       " Antigua and Barbuda                 23.09          16.83          42.19   \n",
       " Argentina                           24.59          15.28          39.38   \n",
       " Armenia                             18.94          12.89          43.43   \n",
       " Aruba                               17.64          12.78          41.72   \n",
       " Australia                           17.80          12.79          41.45   \n",
       " Austria                             14.01          11.07          42.42   \n",
       " Azerbaijan                          22.95          14.84          45.39   \n",
       " Bahamas, The                        22.55          16.40          44.14   \n",
       " Bahrain                             19.08          15.65          56.04   \n",
       " Bangladesh                          27.76          19.36          39.73   \n",
       " Barbados                            17.97          12.74          44.06   \n",
       " Belarus                             15.78          10.29          44.76   \n",
       " Belgium                             17.16          11.34          40.05   \n",
       " Belize                              33.95          20.55          36.62   \n",
       " Benin                               42.65          20.44          30.44   \n",
       " Bermuda                             17.04          12.00          37.24   \n",
       " Bhutan                              25.80          18.81          43.07   \n",
       " Bolivia                             31.85          19.46          37.48   \n",
       " Bosnia and Herzegovina              13.29          11.58          45.88   \n",
       " Botswana                            31.95          18.91          38.45   \n",
       " Brazil                              22.33          16.36          43.86   \n",
       " Brunei                              23.12          17.05          46.75   \n",
       " Bulgaria                            14.58           9.58          43.21   \n",
       " Burkina Faso                        44.88          20.07          29.42   \n",
       " Burma                               26.85          17.75          42.36   \n",
       "...                                    ...            ...            ...   \n",
       " Sudan                               38.68          21.04          32.77   \n",
       " Suriname                            24.62          17.44          44.40   \n",
       " Swaziland                           35.01          22.12          34.60   \n",
       " Sweden                              17.43          11.31          39.42   \n",
       " Switzerland                         15.16          10.88          43.21   \n",
       " Syria                               31.62          19.54          39.22   \n",
       " Taiwan                              12.88          12.88          46.41   \n",
       " Tajikistan                          32.33          18.61          40.12   \n",
       " Tanzania                            43.74          19.86          29.88   \n",
       " Thailand                            16.93          14.17          46.32   \n",
       " Timor-Leste                         40.91          20.32          29.95   \n",
       " Togo                                40.29          19.20          32.79   \n",
       " Trinidad and Tobago                 19.29          11.88          45.56   \n",
       " Tunisia                             25.15          13.99          43.38   \n",
       " Turkey                              24.68          15.99          43.21   \n",
       " Turkmenistan                        25.79          18.39          43.18   \n",
       " Uganda                              48.05          21.10          26.30   \n",
       " Ukraine                             15.76           9.86          44.29   \n",
       " United Arab Emirates                21.01          13.51          61.14   \n",
       " United Kingdom                      17.53          11.90          40.55   \n",
       " United States                       18.73          13.27          39.45   \n",
       " Uruguay                             20.17          15.69          39.34   \n",
       " Uzbekistan                          23.88          18.52          44.49   \n",
       " Venezuela                           27.36          17.03          40.53   \n",
       " Vietnam                             23.55          16.23          45.56   \n",
       " Virgin Islands                      20.41          11.19          37.07   \n",
       " West Bank                           36.50          21.37          34.10   \n",
       " Yemen                               39.83          21.21          32.27   \n",
       " Zambia                              46.03          20.00          28.72   \n",
       " Zimbabwe                            38.90          20.47          31.90   \n",
       "\n",
       "                          55-64 years %  65 years and above %  GDP (PPP)  \\\n",
       "COUNTRY                                                                    \n",
       " Afghanistan                       3.92                  2.59     1900.0   \n",
       " Albania                          11.54                 11.89    12500.0   \n",
       " Algeria                           6.81                  5.65    15100.0   \n",
       " Andorra                          14.16                 15.61    49900.0   \n",
       " Angola                            3.26                  2.34     6800.0   \n",
       " Antigua and Barbuda               9.83                  8.06    26300.0   \n",
       " Argentina                         9.13                 11.62    20700.0   \n",
       " Armenia                          13.41                 11.33     9100.0   \n",
       " Aruba                            14.28                 13.59    25300.0   \n",
       " Australia                        11.83                 16.14    49900.0   \n",
       " Austria                          13.23                 19.26    49200.0   \n",
       " Azerbaijan                       10.17                  6.64    17400.0   \n",
       " Bahamas, The                      9.16                  7.75    25100.0   \n",
       " Bahrain                           6.28                  2.95    51800.0   \n",
       " Bangladesh                        6.93                  6.23     4200.0   \n",
       " Barbados                         13.43                 11.81    17500.0   \n",
       " Belarus                          14.21                 14.95    18600.0   \n",
       " Belgium                          12.86                 18.58    46300.0   \n",
       " Belize                            4.99                  3.89     8300.0   \n",
       " Benin                             3.61                  2.87     2200.0   \n",
       " Bermuda                          15.73                 17.98    85700.0   \n",
       " Bhutan                            6.03                  6.29     8700.0   \n",
       " Bolivia                           5.90                  5.30     7500.0   \n",
       " Bosnia and Herzegovina           14.83                 14.43    11400.0   \n",
       " Botswana                          5.46                  5.23    18100.0   \n",
       " Brazil                            9.12                  8.33    15500.0   \n",
       " Brunei                            8.23                  4.84    76700.0   \n",
       " Bulgaria                         13.35                 19.28    21600.0   \n",
       " Burkina Faso                      3.20                  2.43     1900.0   \n",
       " Burma                             7.52                  5.53     6300.0   \n",
       "...                                 ...                   ...        ...   \n",
       " Sudan                             4.24                  3.27     4600.0   \n",
       " Suriname                          7.54                  6.01    13900.0   \n",
       " Swaziland                         4.30                  3.97     9900.0   \n",
       " Sweden                           11.58                 20.26    51300.0   \n",
       " Switzerland                      12.60                 18.15    61400.0   \n",
       " Syria                             5.41                  4.21     2900.0   \n",
       " Taiwan                           14.12                 13.72    49800.0   \n",
       " Tajikistan                        5.62                  3.32     3100.0   \n",
       " Tanzania                          3.51                  3.02     3300.0   \n",
       " Thailand                         12.00                 10.58    17800.0   \n",
       " Timor-Leste                       4.94                  3.87     5000.0   \n",
       " Togo                              4.31                  3.41     1600.0   \n",
       " Trinidad and Tobago              12.61                 10.65    31200.0   \n",
       " Tunisia                           9.54                  7.95    12000.0   \n",
       " Turkey                            8.58                  7.53    26500.0   \n",
       " Turkmenistan                      7.90                  4.74    18700.0   \n",
       " Uganda                            2.57                  1.98     2400.0   \n",
       " Ukraine                          13.80                 16.30     8700.0   \n",
       " United Arab Emirates              3.27                  1.07    68200.0   \n",
       " United Kingdom                   11.98                 18.04    43600.0   \n",
       " United States                    12.91                 15.63    59500.0   \n",
       " Uruguay                          10.56                 14.25    22400.0   \n",
       " Uzbekistan                        7.85                  5.25     7000.0   \n",
       " Venezuela                         7.98                  7.09    12400.0   \n",
       " Vietnam                           8.55                  6.12     6900.0   \n",
       " Virgin Islands                   13.78                 17.55    36100.0   \n",
       " West Bank                         4.54                  3.49     4300.0   \n",
       " Yemen                             3.94                  2.75     2300.0   \n",
       " Zambia                            2.93                  2.33     4000.0   \n",
       " Zimbabwe                          4.27                  4.46     2300.0   \n",
       "\n",
       "                          Total GDP (PPP)  \n",
       "COUNTRY                                    \n",
       " Afghanistan                       69.510  \n",
       " Albania                           35.870  \n",
       " Algeria                          629.300  \n",
       " Andorra                            3.327  \n",
       " Angola                           192.000  \n",
       " Antigua and Barbuda                2.390  \n",
       " Argentina                        911.500  \n",
       " Armenia                           27.210  \n",
       " Aruba                              2.516  \n",
       " Australia                       1235.000  \n",
       " Austria                          434.100  \n",
       " Azerbaijan                       166.800  \n",
       " Bahamas, The                       9.339  \n",
       " Bahrain                           69.770  \n",
       " Bangladesh                       686.500  \n",
       " Barbados                           4.919  \n",
       " Belarus                          175.900  \n",
       " Belgium                          526.400  \n",
       " Belize                             3.230  \n",
       " Benin                             25.290  \n",
       " Bermuda                            5.198  \n",
       " Bhutan                             7.011  \n",
       " Bolivia                           83.500  \n",
       " Bosnia and Herzegovina            43.850  \n",
       " Botswana                          39.550  \n",
       " Brazil                          3219.000  \n",
       " Brunei                            32.910  \n",
       " Bulgaria                         152.400  \n",
       " Burkina Faso                      35.680  \n",
       " Burma                            330.900  \n",
       "...                                   ...  \n",
       " Sudan                            186.800  \n",
       " Suriname                           7.928  \n",
       " Swaziland                         11.340  \n",
       " Sweden                           521.700  \n",
       " Switzerland                      516.700  \n",
       " Syria                             50.280  \n",
       " Taiwan                          1175.000  \n",
       " Tajikistan                        27.670  \n",
       " Tanzania                         162.800  \n",
       " Thailand                        1229.000  \n",
       " Timor-Leste                        6.211  \n",
       " Togo                              12.430  \n",
       " Trinidad and Tobago               42.780  \n",
       " Tunisia                          135.900  \n",
       " Turkey                          2133.000  \n",
       " Turkmenistan                     103.500  \n",
       " Uganda                            88.610  \n",
       " Ukraine                          366.400  \n",
       " United Arab Emirates             691.900  \n",
       " United Kingdom                  2880.000  \n",
       " United States                  19360.000  \n",
       " Uruguay                           78.410  \n",
       " Uzbekistan                       221.700  \n",
       " Venezuela                        389.400  \n",
       " Vietnam                          643.900  \n",
       " Virgin Islands                     3.792  \n",
       " West Bank                         21.220  \n",
       " Yemen                             68.950  \n",
       " Zambia                            68.900  \n",
       " Zimbabwe                          33.870  \n",
       "\n",
       "[202 rows x 7 columns]"
      ]
     },
     "execution_count": 235,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_combined"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### GDP growth rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GDP growth rate of  Afghanistan : 2.5\n",
      "**Could not find GDP growth rate data!**\n",
      "GDP growth rate of  Albania : 3.7\n",
      "GDP growth rate of  Algeria : 1.5\n",
      "GDP growth rate of  American Samoa : -2.4\n",
      "GDP growth rate of  Andorra : -1.1\n",
      "GDP growth rate of  Angola : 1.5\n",
      "GDP growth rate of  Anguilla : -8.5\n",
      "**Could not find GDP growth rate data!**\n",
      "GDP growth rate of  Antigua and Barbuda : 2.7\n",
      "**Could not find GDP growth rate data!**\n",
      "GDP growth rate of  Argentina : 2.5\n",
      "GDP growth rate of  Armenia : 3.5\n",
      "GDP growth rate of  Aruba : 2.4\n",
      "**Could not find GDP growth rate data!**\n",
      "**Could not find GDP growth rate data!**\n",
      "GDP growth rate of  Australia : 2.2\n",
      "GDP growth rate of  Austria : 2.3\n",
      "GDP growth rate of  Azerbaijan : -1.0\n",
      "GDP growth rate of  Bahamas, The : 1.8\n",
      "GDP growth rate of  Bahrain : 2.5\n",
      "**Could not find GDP growth rate data!**\n",
      "GDP growth rate of  Bangladesh : 7.1\n",
      "GDP growth rate of  Barbados : 0.9\n",
      "GDP growth rate of  Belarus : 0.7\n",
      "GDP growth rate of  Belgium : 1.6\n",
      "GDP growth rate of  Belize : 2.5\n",
      "GDP growth rate of  Benin : 5.4\n",
      "GDP growth rate of  Bermuda : -2.5\n",
      "GDP growth rate of  Bhutan : 5.9\n",
      "GDP growth rate of  Bolivia : 4.2\n",
      "GDP growth rate of  Bosnia and Herzegovina : 2.5\n",
      "GDP growth rate of  Botswana : 4.5\n",
      "**Could not find GDP growth rate data!**\n",
      "GDP growth rate of  Brazil : 0.7\n",
      "**Could not find GDP growth rate data!**\n",
      "GDP growth rate of  British Virgin Islands : 1.3\n",
      "GDP growth rate of  Brunei : -1.3\n",
      "GDP growth rate of  Bulgaria : 3.6\n",
      "GDP growth rate of  Burkina Faso : 6.4\n",
      "GDP growth rate of  Burma : 7.2\n",
      "GDP growth rate of  Burundi : 0.0\n",
      "GDP growth rate of  Cabo Verde : 4.0\n",
      "GDP growth rate of  Cambodia : 6.9\n",
      "GDP growth rate of  Cameroon : 4.0\n",
      "GDP growth rate of  Canada : 3.0\n",
      "GDP growth rate of  Cayman Islands : 1.7\n",
      "GDP growth rate of  Central African Republic : 4.7\n",
      "GDP growth rate of  Chad : 0.6\n",
      "GDP growth rate of  Chile : 1.4\n",
      "GDP growth rate of  China : 6.8\n",
      "**Could not find GDP growth rate data!**\n",
      "**Could not find GDP growth rate data!**\n",
      "GDP growth rate of  Cocos (Keeling) Islands : 1.0\n",
      "GDP growth rate of  Colombia : 1.7\n",
      "GDP growth rate of  Comoros : 3.3\n",
      "GDP growth rate of  Congo, Democratic Republic of the : 2.8\n",
      "GDP growth rate of  Congo, Republic of the : -3.6\n",
      "GDP growth rate of  Cook Islands : 0.1\n",
      "**Could not find GDP growth rate data!**\n",
      "GDP growth rate of  Costa Rica : 3.8\n",
      "GDP growth rate of  Cote d'Ivoire : 7.6\n",
      "GDP growth rate of  Croatia : 2.9\n",
      "GDP growth rate of  Cuba : -0.9\n",
      "GDP growth rate of  Curacao : 3.6\n",
      "GDP growth rate of  Cyprus : 3.4\n",
      "GDP growth rate of  Czechia : 3.5\n",
      "GDP growth rate of  Denmark : 1.9\n",
      "**Could not find GDP growth rate data!**\n",
      "GDP growth rate of  Djibouti : 7.0\n",
      "GDP growth rate of  Dominica : 3.9\n",
      "GDP growth rate of  Dominican Republic : 4.8\n",
      "GDP growth rate of  Ecuador : 0.2\n",
      "GDP growth rate of  Egypt : 4.1\n",
      "GDP growth rate of  El Salvador : 2.3\n",
      "GDP growth rate of  Equatorial Guinea : -7.4\n",
      "GDP growth rate of  Eritrea : 3.3\n",
      "GDP growth rate of  Estonia : 4.0\n",
      "GDP growth rate of  Ethiopia : 8.5\n",
      "**Could not find GDP growth rate data!**\n",
      "GDP growth rate of  Faroe Islands : 5.9\n",
      "GDP growth rate of  Fiji : 3.8\n",
      "GDP growth rate of  Finland : 2.8\n",
      "GDP growth rate of  France : 1.6\n",
      "GDP growth rate of  French Polynesia : 2.0\n",
      "**Could not find GDP growth rate data!**\n",
      "GDP growth rate of  Gabon : 1.0\n",
      "GDP growth rate of  Gambia, The : 3.0\n",
      "GDP growth rate of  Gaza Strip : -15.2\n",
      "GDP growth rate of  Georgia : 4.0\n",
      "GDP growth rate of  Germany : 2.1\n",
      "GDP growth rate of  Ghana : 5.9\n",
      "**Could not find GDP growth rate data!**\n",
      "GDP growth rate of  Greece : 1.8\n",
      "GDP growth rate of  Greenland : 1.7\n",
      "GDP growth rate of  Grenada : 2.5\n",
      "GDP growth rate of  Guam : 0.6\n",
      "GDP growth rate of  Guatemala : 3.2\n",
      "GDP growth rate of  Guernsey : 0.4\n",
      "GDP growth rate of  Guinea : 6.7\n",
      "GDP growth rate of  Guinea-Bissau : 5.0\n",
      "GDP growth rate of  Guyana : 3.5\n",
      "GDP growth rate of  Haiti : 1.0\n",
      "**Could not find GDP growth rate data!**\n",
      "**Could not find GDP growth rate data!**\n",
      "GDP growth rate of  Honduras : 4.0\n",
      "GDP growth rate of  Hong Kong : 3.5\n",
      "**Could not find GDP growth rate data!**\n",
      "GDP growth rate of  Hungary : 3.2\n",
      "GDP growth rate of  Iceland : 5.5\n",
      "GDP growth rate of  India : 6.7\n",
      "**Could not find GDP growth rate data!**\n",
      "GDP growth rate of  Indonesia : 5.2\n",
      "GDP growth rate of  Iran : 3.5\n",
      "GDP growth rate of  Iraq : -0.4\n",
      "GDP growth rate of  Ireland : 4.1\n",
      "GDP growth rate of  Isle of Man : 2.2\n",
      "GDP growth rate of  Israel : 3.1\n",
      "GDP growth rate of  Italy : 1.5\n",
      "GDP growth rate of  Jamaica : 1.7\n",
      "**Could not find GDP growth rate data!**\n",
      "GDP growth rate of  Japan : 1.5\n",
      "**Could not find GDP growth rate data!**\n",
      "GDP growth rate of  Jersey : 2.0\n",
      "**Could not find GDP growth rate data!**\n",
      "GDP growth rate of  Jordan : 2.3\n",
      "GDP growth rate of  Kazakhstan : 3.3\n",
      "GDP growth rate of  Kenya : 5.0\n",
      "**Could not find GDP growth rate data!**\n",
      "GDP growth rate of  Kiribati : 2.8\n",
      "GDP growth rate of  Korea, North : -1.1\n",
      "GDP growth rate of  Korea, South : 3.0\n",
      "GDP growth rate of  Kosovo : 3.5\n",
      "GDP growth rate of  Kuwait : -2.1\n",
      "GDP growth rate of  Kyrgyzstan : 3.5\n",
      "GDP growth rate of  Laos : 6.9\n",
      "GDP growth rate of  Latvia : 3.8\n",
      "GDP growth rate of  Lebanon : 1.5\n",
      "GDP growth rate of  Lesotho : 4.6\n",
      "GDP growth rate of  Liberia : 2.6\n",
      "GDP growth rate of  Libya : 55.1\n",
      "GDP growth rate of  Liechtenstein : 1.8\n",
      "GDP growth rate of  Lithuania : 3.5\n",
      "GDP growth rate of  Luxembourg : 3.9\n",
      "GDP growth rate of  Macau : 13.4\n",
      "GDP growth rate of  Macedonia : 2.5\n",
      "GDP growth rate of  Madagascar : 4.3\n",
      "GDP growth rate of  Malawi : 4.5\n",
      "GDP growth rate of  Malaysia : 5.4\n",
      "GDP growth rate of  Maldives : 4.6\n",
      "GDP growth rate of  Mali : 5.3\n",
      "GDP growth rate of  Malta : 5.1\n",
      "GDP growth rate of  Marshall Islands : 1.9\n",
      "GDP growth rate of  Mauritania : 3.8\n",
      "GDP growth rate of  Mauritius : 3.9\n",
      "GDP growth rate of  Mexico : 2.1\n",
      "GDP growth rate of  Micronesia, Federated States of : 2.0\n",
      "**Could not find GDP growth rate data!**\n",
      "GDP growth rate of  Moldova : 4.0\n",
      "GDP growth rate of  Monaco : 5.4\n",
      "GDP growth rate of  Mongolia : 2.0\n",
      "GDP growth rate of  Montenegro : 3.0\n",
      "GDP growth rate of  Montserrat : 3.5\n",
      "GDP growth rate of  Morocco : 4.8\n",
      "GDP growth rate of  Mozambique : 4.7\n",
      "GDP growth rate of  Namibia : 0.8\n",
      "GDP growth rate of  Nauru : 4.0\n",
      "**Could not find GDP growth rate data!**\n",
      "GDP growth rate of  Nepal : 7.5\n",
      "GDP growth rate of  Netherlands : 3.1\n",
      "GDP growth rate of  New Caledonia : 1.4\n",
      "GDP growth rate of  New Zealand : 3.5\n",
      "GDP growth rate of  Nicaragua : 4.5\n",
      "GDP growth rate of  Niger : 4.2\n",
      "GDP growth rate of  Nigeria : 0.8\n",
      "GDP growth rate of  Niue : 6.2\n",
      "**Could not find GDP growth rate data!**\n",
      "GDP growth rate of  Northern Mariana Islands : 4.5\n",
      "GDP growth rate of  Norway : 1.4\n",
      "GDP growth rate of  Oman : 0.0\n",
      "**Could not find GDP growth rate data!**\n",
      "GDP growth rate of  Pakistan : 5.3\n",
      "GDP growth rate of  Palau : 1.0\n",
      "**Could not find GDP growth rate data!**\n",
      "GDP growth rate of  Panama : 5.3\n",
      "GDP growth rate of  Papua New Guinea : 3.1\n",
      "**Could not find GDP growth rate data!**\n",
      "GDP growth rate of  Paraguay : 3.9\n",
      "GDP growth rate of  Peru : 2.7\n",
      "GDP growth rate of  Philippines : 6.6\n",
      "**Could not find GDP growth rate data!**\n",
      "GDP growth rate of  Poland : 3.8\n",
      "GDP growth rate of  Portugal : 2.5\n",
      "GDP growth rate of  Puerto Rico : -2.8\n",
      "GDP growth rate of  Qatar : 2.5\n",
      "GDP growth rate of  Romania : 5.5\n",
      "GDP growth rate of  Russia : 1.8\n",
      "GDP growth rate of  Rwanda : 6.2\n",
      "**Could not find GDP growth rate data!**\n",
      "**Could not find GDP growth rate data!**\n",
      "GDP growth rate of  Saint Kitts and Nevis : 2.7\n",
      "GDP growth rate of  Saint Lucia : 1.6\n",
      "**Could not find GDP growth rate data!**\n",
      "**Could not find GDP growth rate data!**\n",
      "GDP growth rate of  Saint Vincent and the Grenadines : 2.2\n",
      "GDP growth rate of  Samoa : 2.1\n",
      "GDP growth rate of  San Marino : 1.2\n",
      "GDP growth rate of  Sao Tome and Principe : 5.0\n",
      "GDP growth rate of  Saudi Arabia : 0.1\n",
      "GDP growth rate of  Senegal : 6.8\n",
      "GDP growth rate of  Serbia : 3.0\n",
      "GDP growth rate of  Seychelles : 4.1\n",
      "GDP growth rate of  Sierra Leone : 6.0\n",
      "GDP growth rate of  Singapore : 2.5\n",
      "GDP growth rate of  Sint Maarten : 3.6\n",
      "GDP growth rate of  Slovakia : 3.3\n",
      "GDP growth rate of  Slovenia : 4.0\n",
      "GDP growth rate of  Solomon Islands : 3.0\n",
      "GDP growth rate of  Somalia : 2.4\n",
      "GDP growth rate of  South Africa : 0.7\n",
      "**Could not find GDP growth rate data!**\n",
      "**Could not find GDP growth rate data!**\n",
      "GDP growth rate of  South Sudan : -6.3\n",
      "GDP growth rate of  Spain : 3.1\n",
      "**Could not find GDP growth rate data!**\n",
      "GDP growth rate of  Sri Lanka : 4.7\n",
      "GDP growth rate of  Sudan : 3.7\n",
      "GDP growth rate of  Suriname : -1.2\n",
      "**Could not find GDP growth rate data!**\n",
      "GDP growth rate of  Swaziland : 0.3\n",
      "GDP growth rate of  Sweden : 3.1\n",
      "GDP growth rate of  Switzerland : 1.0\n",
      "**Could not find GDP growth rate data!**\n",
      "GDP growth rate of  Taiwan : 2.0\n",
      "GDP growth rate of  Tajikistan : 4.5\n",
      "GDP growth rate of  Tanzania : 6.5\n",
      "GDP growth rate of  Thailand : 3.7\n",
      "GDP growth rate of  Timor-Leste : 4.0\n",
      "GDP growth rate of  Togo : 5.0\n",
      "**Could not find GDP growth rate data!**\n",
      "GDP growth rate of  Tonga : 3.1\n",
      "GDP growth rate of  Trinidad and Tobago : -3.2\n",
      "GDP growth rate of  Tunisia : 2.3\n",
      "GDP growth rate of  Turkey : 5.1\n",
      "GDP growth rate of  Turkmenistan : 6.5\n",
      "GDP growth rate of  Turks and Caicos Islands : 11.2\n",
      "GDP growth rate of  Tuvalu : 3.2\n",
      "GDP growth rate of  Uganda : 4.4\n",
      "GDP growth rate of  Ukraine : 2.0\n",
      "GDP growth rate of  United Arab Emirates : 1.3\n",
      "GDP growth rate of  United Kingdom : 1.7\n",
      "GDP growth rate of  United States : 2.2\n",
      "**Could not find GDP growth rate data!**\n",
      "GDP growth rate of  Uruguay : 3.5\n",
      "GDP growth rate of  Uzbekistan : 6.0\n",
      "GDP growth rate of  Vanuatu : 4.5\n",
      "GDP growth rate of  Venezuela : -12.0\n",
      "GDP growth rate of  Vietnam : 6.3\n",
      "GDP growth rate of  Virgin Islands : -5.4\n",
      "**Could not find GDP growth rate data!**\n",
      "**Could not find GDP growth rate data!**\n",
      "GDP growth rate of  West Bank : 5.3\n",
      "**Could not find GDP growth rate data!**\n",
      "GDP growth rate of  Yemen : -2.0\n",
      "GDP growth rate of  Zambia : 4.0\n",
      "GDP growth rate of  Zimbabwe : 2.8\n",
      "\n",
      "Finished finding all GDP growth rate data\n"
     ]
    }
   ],
   "source": [
    "# Initialize dictionary for holding the data\n",
    "GDP_growth = {}\n",
    "# Iterate over every country\n",
    "for i in range(1,len(country_names)-1):\n",
    "    country= country_names[i]\n",
    "    txt=text_data[country]       \n",
    "    pos = txt.find('GDP - real growth rate:')\n",
    "    if pos!=-1: \n",
    "        pos= pos+len('GDP - real growth rate: ')\n",
    "        string = txt[pos:pos+12]\n",
    "        end = re.search('%',string)\n",
    "        if (end!=None):\n",
    "            end=end.start()\n",
    "            a=string[:end]\n",
    "            if a[-1].isdigit():\n",
    "                a = float(a)\n",
    "                print(f\"GDP growth rate of {country}: {a}\")\n",
    "                # Insert the data in the dictionary\n",
    "                GDP_growth[country]=a\n",
    "            else:\n",
    "                print(\"**Could not find GDP growth rate data!**\")\n",
    "        else:\n",
    "            print(\"**Could not find GDP growth rate data!**\")\n",
    "    else:\n",
    "        print(\"**Could not find GDP growth rate data!**\")\n",
    "\n",
    "print (\"\\nFinished finding all GDP growth rate data\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GDP growth rate (%)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COUNTRY</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Afghanistan</th>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>3.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>American Samoa</th>\n",
       "      <td>-2.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andorra</th>\n",
       "      <td>-1.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Angola</th>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Anguilla</th>\n",
       "      <td>-8.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Antigua and Barbuda</th>\n",
       "      <td>2.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Argentina</th>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Armenia</th>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aruba</th>\n",
       "      <td>2.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <td>2.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Austria</th>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Azerbaijan</th>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bahamas, The</th>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bahrain</th>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bangladesh</th>\n",
       "      <td>7.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Barbados</th>\n",
       "      <td>0.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belarus</th>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belgium</th>\n",
       "      <td>1.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belize</th>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Benin</th>\n",
       "      <td>5.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bermuda</th>\n",
       "      <td>-2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bhutan</th>\n",
       "      <td>5.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bolivia</th>\n",
       "      <td>4.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bosnia and Herzegovina</th>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Botswana</th>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brazil</th>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>British Virgin Islands</th>\n",
       "      <td>1.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brunei</th>\n",
       "      <td>-1.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sweden</th>\n",
       "      <td>3.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Switzerland</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Taiwan</th>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tajikistan</th>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tanzania</th>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thailand</th>\n",
       "      <td>3.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Timor-Leste</th>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Togo</th>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tonga</th>\n",
       "      <td>3.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Trinidad and Tobago</th>\n",
       "      <td>-3.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tunisia</th>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Turkey</th>\n",
       "      <td>5.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Turkmenistan</th>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Turks and Caicos Islands</th>\n",
       "      <td>11.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tuvalu</th>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uganda</th>\n",
       "      <td>4.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ukraine</th>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Arab Emirates</th>\n",
       "      <td>1.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>1.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>2.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uruguay</th>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uzbekistan</th>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vanuatu</th>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Venezuela</th>\n",
       "      <td>-12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vietnam</th>\n",
       "      <td>6.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Virgin Islands</th>\n",
       "      <td>-5.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>West Bank</th>\n",
       "      <td>5.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yemen</th>\n",
       "      <td>-2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zambia</th>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zimbabwe</th>\n",
       "      <td>2.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>222 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                            GDP growth rate (%)\n",
       "COUNTRY                                        \n",
       " Afghanistan                                2.5\n",
       " Albania                                    3.7\n",
       " Algeria                                    1.5\n",
       " American Samoa                            -2.4\n",
       " Andorra                                   -1.1\n",
       " Angola                                     1.5\n",
       " Anguilla                                  -8.5\n",
       " Antigua and Barbuda                        2.7\n",
       " Argentina                                  2.5\n",
       " Armenia                                    3.5\n",
       " Aruba                                      2.4\n",
       " Australia                                  2.2\n",
       " Austria                                    2.3\n",
       " Azerbaijan                                -1.0\n",
       " Bahamas, The                               1.8\n",
       " Bahrain                                    2.5\n",
       " Bangladesh                                 7.1\n",
       " Barbados                                   0.9\n",
       " Belarus                                    0.7\n",
       " Belgium                                    1.6\n",
       " Belize                                     2.5\n",
       " Benin                                      5.4\n",
       " Bermuda                                   -2.5\n",
       " Bhutan                                     5.9\n",
       " Bolivia                                    4.2\n",
       " Bosnia and Herzegovina                     2.5\n",
       " Botswana                                   4.5\n",
       " Brazil                                     0.7\n",
       " British Virgin Islands                     1.3\n",
       " Brunei                                    -1.3\n",
       "...                                         ...\n",
       " Sweden                                     3.1\n",
       " Switzerland                                1.0\n",
       " Taiwan                                     2.0\n",
       " Tajikistan                                 4.5\n",
       " Tanzania                                   6.5\n",
       " Thailand                                   3.7\n",
       " Timor-Leste                                4.0\n",
       " Togo                                       5.0\n",
       " Tonga                                      3.1\n",
       " Trinidad and Tobago                       -3.2\n",
       " Tunisia                                    2.3\n",
       " Turkey                                     5.1\n",
       " Turkmenistan                               6.5\n",
       " Turks and Caicos Islands                  11.2\n",
       " Tuvalu                                     3.2\n",
       " Uganda                                     4.4\n",
       " Ukraine                                    2.0\n",
       " United Arab Emirates                       1.3\n",
       " United Kingdom                             1.7\n",
       " United States                              2.2\n",
       " Uruguay                                    3.5\n",
       " Uzbekistan                                 6.0\n",
       " Vanuatu                                    4.5\n",
       " Venezuela                                -12.0\n",
       " Vietnam                                    6.3\n",
       " Virgin Islands                            -5.4\n",
       " West Bank                                  5.3\n",
       " Yemen                                     -2.0\n",
       " Zambia                                     4.0\n",
       " Zimbabwe                                   2.8\n",
       "\n",
       "[222 rows x 1 columns]"
      ]
     },
     "execution_count": 237,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_GDP_growth=pd.Series(GDP_growth).to_frame()\n",
    "df_GDP_growth.columns=['GDP growth rate (%)']\n",
    "df_GDP_growth.index.name='COUNTRY'\n",
    "df_GDP_growth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0-14 years old %</th>\n",
       "      <th>15-24 years %</th>\n",
       "      <th>25-54 years %</th>\n",
       "      <th>55-64 years %</th>\n",
       "      <th>65 years and above %</th>\n",
       "      <th>GDP (PPP)</th>\n",
       "      <th>Total GDP (PPP)</th>\n",
       "      <th>GDP growth rate (%)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COUNTRY</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Afghanistan</th>\n",
       "      <td>40.92</td>\n",
       "      <td>22.22</td>\n",
       "      <td>30.35</td>\n",
       "      <td>3.92</td>\n",
       "      <td>2.59</td>\n",
       "      <td>1900.0</td>\n",
       "      <td>69.510</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>18.05</td>\n",
       "      <td>17.47</td>\n",
       "      <td>41.06</td>\n",
       "      <td>11.54</td>\n",
       "      <td>11.89</td>\n",
       "      <td>12500.0</td>\n",
       "      <td>35.870</td>\n",
       "      <td>3.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>29.31</td>\n",
       "      <td>15.30</td>\n",
       "      <td>42.93</td>\n",
       "      <td>6.81</td>\n",
       "      <td>5.65</td>\n",
       "      <td>15100.0</td>\n",
       "      <td>629.300</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andorra</th>\n",
       "      <td>14.40</td>\n",
       "      <td>9.64</td>\n",
       "      <td>46.18</td>\n",
       "      <td>14.16</td>\n",
       "      <td>15.61</td>\n",
       "      <td>49900.0</td>\n",
       "      <td>3.327</td>\n",
       "      <td>-1.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Angola</th>\n",
       "      <td>48.12</td>\n",
       "      <td>18.25</td>\n",
       "      <td>28.03</td>\n",
       "      <td>3.26</td>\n",
       "      <td>2.34</td>\n",
       "      <td>6800.0</td>\n",
       "      <td>192.000</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Antigua and Barbuda</th>\n",
       "      <td>23.09</td>\n",
       "      <td>16.83</td>\n",
       "      <td>42.19</td>\n",
       "      <td>9.83</td>\n",
       "      <td>8.06</td>\n",
       "      <td>26300.0</td>\n",
       "      <td>2.390</td>\n",
       "      <td>2.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Argentina</th>\n",
       "      <td>24.59</td>\n",
       "      <td>15.28</td>\n",
       "      <td>39.38</td>\n",
       "      <td>9.13</td>\n",
       "      <td>11.62</td>\n",
       "      <td>20700.0</td>\n",
       "      <td>911.500</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Armenia</th>\n",
       "      <td>18.94</td>\n",
       "      <td>12.89</td>\n",
       "      <td>43.43</td>\n",
       "      <td>13.41</td>\n",
       "      <td>11.33</td>\n",
       "      <td>9100.0</td>\n",
       "      <td>27.210</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aruba</th>\n",
       "      <td>17.64</td>\n",
       "      <td>12.78</td>\n",
       "      <td>41.72</td>\n",
       "      <td>14.28</td>\n",
       "      <td>13.59</td>\n",
       "      <td>25300.0</td>\n",
       "      <td>2.516</td>\n",
       "      <td>2.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <td>17.80</td>\n",
       "      <td>12.79</td>\n",
       "      <td>41.45</td>\n",
       "      <td>11.83</td>\n",
       "      <td>16.14</td>\n",
       "      <td>49900.0</td>\n",
       "      <td>1235.000</td>\n",
       "      <td>2.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Austria</th>\n",
       "      <td>14.01</td>\n",
       "      <td>11.07</td>\n",
       "      <td>42.42</td>\n",
       "      <td>13.23</td>\n",
       "      <td>19.26</td>\n",
       "      <td>49200.0</td>\n",
       "      <td>434.100</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Azerbaijan</th>\n",
       "      <td>22.95</td>\n",
       "      <td>14.84</td>\n",
       "      <td>45.39</td>\n",
       "      <td>10.17</td>\n",
       "      <td>6.64</td>\n",
       "      <td>17400.0</td>\n",
       "      <td>166.800</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bahamas, The</th>\n",
       "      <td>22.55</td>\n",
       "      <td>16.40</td>\n",
       "      <td>44.14</td>\n",
       "      <td>9.16</td>\n",
       "      <td>7.75</td>\n",
       "      <td>25100.0</td>\n",
       "      <td>9.339</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bahrain</th>\n",
       "      <td>19.08</td>\n",
       "      <td>15.65</td>\n",
       "      <td>56.04</td>\n",
       "      <td>6.28</td>\n",
       "      <td>2.95</td>\n",
       "      <td>51800.0</td>\n",
       "      <td>69.770</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bangladesh</th>\n",
       "      <td>27.76</td>\n",
       "      <td>19.36</td>\n",
       "      <td>39.73</td>\n",
       "      <td>6.93</td>\n",
       "      <td>6.23</td>\n",
       "      <td>4200.0</td>\n",
       "      <td>686.500</td>\n",
       "      <td>7.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Barbados</th>\n",
       "      <td>17.97</td>\n",
       "      <td>12.74</td>\n",
       "      <td>44.06</td>\n",
       "      <td>13.43</td>\n",
       "      <td>11.81</td>\n",
       "      <td>17500.0</td>\n",
       "      <td>4.919</td>\n",
       "      <td>0.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belarus</th>\n",
       "      <td>15.78</td>\n",
       "      <td>10.29</td>\n",
       "      <td>44.76</td>\n",
       "      <td>14.21</td>\n",
       "      <td>14.95</td>\n",
       "      <td>18600.0</td>\n",
       "      <td>175.900</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belgium</th>\n",
       "      <td>17.16</td>\n",
       "      <td>11.34</td>\n",
       "      <td>40.05</td>\n",
       "      <td>12.86</td>\n",
       "      <td>18.58</td>\n",
       "      <td>46300.0</td>\n",
       "      <td>526.400</td>\n",
       "      <td>1.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belize</th>\n",
       "      <td>33.95</td>\n",
       "      <td>20.55</td>\n",
       "      <td>36.62</td>\n",
       "      <td>4.99</td>\n",
       "      <td>3.89</td>\n",
       "      <td>8300.0</td>\n",
       "      <td>3.230</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Benin</th>\n",
       "      <td>42.65</td>\n",
       "      <td>20.44</td>\n",
       "      <td>30.44</td>\n",
       "      <td>3.61</td>\n",
       "      <td>2.87</td>\n",
       "      <td>2200.0</td>\n",
       "      <td>25.290</td>\n",
       "      <td>5.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bermuda</th>\n",
       "      <td>17.04</td>\n",
       "      <td>12.00</td>\n",
       "      <td>37.24</td>\n",
       "      <td>15.73</td>\n",
       "      <td>17.98</td>\n",
       "      <td>85700.0</td>\n",
       "      <td>5.198</td>\n",
       "      <td>-2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bhutan</th>\n",
       "      <td>25.80</td>\n",
       "      <td>18.81</td>\n",
       "      <td>43.07</td>\n",
       "      <td>6.03</td>\n",
       "      <td>6.29</td>\n",
       "      <td>8700.0</td>\n",
       "      <td>7.011</td>\n",
       "      <td>5.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bolivia</th>\n",
       "      <td>31.85</td>\n",
       "      <td>19.46</td>\n",
       "      <td>37.48</td>\n",
       "      <td>5.90</td>\n",
       "      <td>5.30</td>\n",
       "      <td>7500.0</td>\n",
       "      <td>83.500</td>\n",
       "      <td>4.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bosnia and Herzegovina</th>\n",
       "      <td>13.29</td>\n",
       "      <td>11.58</td>\n",
       "      <td>45.88</td>\n",
       "      <td>14.83</td>\n",
       "      <td>14.43</td>\n",
       "      <td>11400.0</td>\n",
       "      <td>43.850</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Botswana</th>\n",
       "      <td>31.95</td>\n",
       "      <td>18.91</td>\n",
       "      <td>38.45</td>\n",
       "      <td>5.46</td>\n",
       "      <td>5.23</td>\n",
       "      <td>18100.0</td>\n",
       "      <td>39.550</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brazil</th>\n",
       "      <td>22.33</td>\n",
       "      <td>16.36</td>\n",
       "      <td>43.86</td>\n",
       "      <td>9.12</td>\n",
       "      <td>8.33</td>\n",
       "      <td>15500.0</td>\n",
       "      <td>3219.000</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brunei</th>\n",
       "      <td>23.12</td>\n",
       "      <td>17.05</td>\n",
       "      <td>46.75</td>\n",
       "      <td>8.23</td>\n",
       "      <td>4.84</td>\n",
       "      <td>76700.0</td>\n",
       "      <td>32.910</td>\n",
       "      <td>-1.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bulgaria</th>\n",
       "      <td>14.58</td>\n",
       "      <td>9.58</td>\n",
       "      <td>43.21</td>\n",
       "      <td>13.35</td>\n",
       "      <td>19.28</td>\n",
       "      <td>21600.0</td>\n",
       "      <td>152.400</td>\n",
       "      <td>3.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Burkina Faso</th>\n",
       "      <td>44.88</td>\n",
       "      <td>20.07</td>\n",
       "      <td>29.42</td>\n",
       "      <td>3.20</td>\n",
       "      <td>2.43</td>\n",
       "      <td>1900.0</td>\n",
       "      <td>35.680</td>\n",
       "      <td>6.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Burma</th>\n",
       "      <td>26.85</td>\n",
       "      <td>17.75</td>\n",
       "      <td>42.36</td>\n",
       "      <td>7.52</td>\n",
       "      <td>5.53</td>\n",
       "      <td>6300.0</td>\n",
       "      <td>330.900</td>\n",
       "      <td>7.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sri Lanka</th>\n",
       "      <td>24.06</td>\n",
       "      <td>14.63</td>\n",
       "      <td>41.58</td>\n",
       "      <td>10.06</td>\n",
       "      <td>9.67</td>\n",
       "      <td>13000.0</td>\n",
       "      <td>278.200</td>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sudan</th>\n",
       "      <td>38.68</td>\n",
       "      <td>21.04</td>\n",
       "      <td>32.77</td>\n",
       "      <td>4.24</td>\n",
       "      <td>3.27</td>\n",
       "      <td>4600.0</td>\n",
       "      <td>186.800</td>\n",
       "      <td>3.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Suriname</th>\n",
       "      <td>24.62</td>\n",
       "      <td>17.44</td>\n",
       "      <td>44.40</td>\n",
       "      <td>7.54</td>\n",
       "      <td>6.01</td>\n",
       "      <td>13900.0</td>\n",
       "      <td>7.928</td>\n",
       "      <td>-1.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Swaziland</th>\n",
       "      <td>35.01</td>\n",
       "      <td>22.12</td>\n",
       "      <td>34.60</td>\n",
       "      <td>4.30</td>\n",
       "      <td>3.97</td>\n",
       "      <td>9900.0</td>\n",
       "      <td>11.340</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sweden</th>\n",
       "      <td>17.43</td>\n",
       "      <td>11.31</td>\n",
       "      <td>39.42</td>\n",
       "      <td>11.58</td>\n",
       "      <td>20.26</td>\n",
       "      <td>51300.0</td>\n",
       "      <td>521.700</td>\n",
       "      <td>3.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Switzerland</th>\n",
       "      <td>15.16</td>\n",
       "      <td>10.88</td>\n",
       "      <td>43.21</td>\n",
       "      <td>12.60</td>\n",
       "      <td>18.15</td>\n",
       "      <td>61400.0</td>\n",
       "      <td>516.700</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Taiwan</th>\n",
       "      <td>12.88</td>\n",
       "      <td>12.88</td>\n",
       "      <td>46.41</td>\n",
       "      <td>14.12</td>\n",
       "      <td>13.72</td>\n",
       "      <td>49800.0</td>\n",
       "      <td>1175.000</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tajikistan</th>\n",
       "      <td>32.33</td>\n",
       "      <td>18.61</td>\n",
       "      <td>40.12</td>\n",
       "      <td>5.62</td>\n",
       "      <td>3.32</td>\n",
       "      <td>3100.0</td>\n",
       "      <td>27.670</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tanzania</th>\n",
       "      <td>43.74</td>\n",
       "      <td>19.86</td>\n",
       "      <td>29.88</td>\n",
       "      <td>3.51</td>\n",
       "      <td>3.02</td>\n",
       "      <td>3300.0</td>\n",
       "      <td>162.800</td>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thailand</th>\n",
       "      <td>16.93</td>\n",
       "      <td>14.17</td>\n",
       "      <td>46.32</td>\n",
       "      <td>12.00</td>\n",
       "      <td>10.58</td>\n",
       "      <td>17800.0</td>\n",
       "      <td>1229.000</td>\n",
       "      <td>3.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Timor-Leste</th>\n",
       "      <td>40.91</td>\n",
       "      <td>20.32</td>\n",
       "      <td>29.95</td>\n",
       "      <td>4.94</td>\n",
       "      <td>3.87</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>6.211</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Togo</th>\n",
       "      <td>40.29</td>\n",
       "      <td>19.20</td>\n",
       "      <td>32.79</td>\n",
       "      <td>4.31</td>\n",
       "      <td>3.41</td>\n",
       "      <td>1600.0</td>\n",
       "      <td>12.430</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Trinidad and Tobago</th>\n",
       "      <td>19.29</td>\n",
       "      <td>11.88</td>\n",
       "      <td>45.56</td>\n",
       "      <td>12.61</td>\n",
       "      <td>10.65</td>\n",
       "      <td>31200.0</td>\n",
       "      <td>42.780</td>\n",
       "      <td>-3.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tunisia</th>\n",
       "      <td>25.15</td>\n",
       "      <td>13.99</td>\n",
       "      <td>43.38</td>\n",
       "      <td>9.54</td>\n",
       "      <td>7.95</td>\n",
       "      <td>12000.0</td>\n",
       "      <td>135.900</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Turkey</th>\n",
       "      <td>24.68</td>\n",
       "      <td>15.99</td>\n",
       "      <td>43.21</td>\n",
       "      <td>8.58</td>\n",
       "      <td>7.53</td>\n",
       "      <td>26500.0</td>\n",
       "      <td>2133.000</td>\n",
       "      <td>5.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Turkmenistan</th>\n",
       "      <td>25.79</td>\n",
       "      <td>18.39</td>\n",
       "      <td>43.18</td>\n",
       "      <td>7.90</td>\n",
       "      <td>4.74</td>\n",
       "      <td>18700.0</td>\n",
       "      <td>103.500</td>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uganda</th>\n",
       "      <td>48.05</td>\n",
       "      <td>21.10</td>\n",
       "      <td>26.30</td>\n",
       "      <td>2.57</td>\n",
       "      <td>1.98</td>\n",
       "      <td>2400.0</td>\n",
       "      <td>88.610</td>\n",
       "      <td>4.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ukraine</th>\n",
       "      <td>15.76</td>\n",
       "      <td>9.86</td>\n",
       "      <td>44.29</td>\n",
       "      <td>13.80</td>\n",
       "      <td>16.30</td>\n",
       "      <td>8700.0</td>\n",
       "      <td>366.400</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Arab Emirates</th>\n",
       "      <td>21.01</td>\n",
       "      <td>13.51</td>\n",
       "      <td>61.14</td>\n",
       "      <td>3.27</td>\n",
       "      <td>1.07</td>\n",
       "      <td>68200.0</td>\n",
       "      <td>691.900</td>\n",
       "      <td>1.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>17.53</td>\n",
       "      <td>11.90</td>\n",
       "      <td>40.55</td>\n",
       "      <td>11.98</td>\n",
       "      <td>18.04</td>\n",
       "      <td>43600.0</td>\n",
       "      <td>2880.000</td>\n",
       "      <td>1.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>18.73</td>\n",
       "      <td>13.27</td>\n",
       "      <td>39.45</td>\n",
       "      <td>12.91</td>\n",
       "      <td>15.63</td>\n",
       "      <td>59500.0</td>\n",
       "      <td>19360.000</td>\n",
       "      <td>2.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uruguay</th>\n",
       "      <td>20.17</td>\n",
       "      <td>15.69</td>\n",
       "      <td>39.34</td>\n",
       "      <td>10.56</td>\n",
       "      <td>14.25</td>\n",
       "      <td>22400.0</td>\n",
       "      <td>78.410</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uzbekistan</th>\n",
       "      <td>23.88</td>\n",
       "      <td>18.52</td>\n",
       "      <td>44.49</td>\n",
       "      <td>7.85</td>\n",
       "      <td>5.25</td>\n",
       "      <td>7000.0</td>\n",
       "      <td>221.700</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Venezuela</th>\n",
       "      <td>27.36</td>\n",
       "      <td>17.03</td>\n",
       "      <td>40.53</td>\n",
       "      <td>7.98</td>\n",
       "      <td>7.09</td>\n",
       "      <td>12400.0</td>\n",
       "      <td>389.400</td>\n",
       "      <td>-12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vietnam</th>\n",
       "      <td>23.55</td>\n",
       "      <td>16.23</td>\n",
       "      <td>45.56</td>\n",
       "      <td>8.55</td>\n",
       "      <td>6.12</td>\n",
       "      <td>6900.0</td>\n",
       "      <td>643.900</td>\n",
       "      <td>6.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Virgin Islands</th>\n",
       "      <td>20.41</td>\n",
       "      <td>11.19</td>\n",
       "      <td>37.07</td>\n",
       "      <td>13.78</td>\n",
       "      <td>17.55</td>\n",
       "      <td>36100.0</td>\n",
       "      <td>3.792</td>\n",
       "      <td>-5.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>West Bank</th>\n",
       "      <td>36.50</td>\n",
       "      <td>21.37</td>\n",
       "      <td>34.10</td>\n",
       "      <td>4.54</td>\n",
       "      <td>3.49</td>\n",
       "      <td>4300.0</td>\n",
       "      <td>21.220</td>\n",
       "      <td>5.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yemen</th>\n",
       "      <td>39.83</td>\n",
       "      <td>21.21</td>\n",
       "      <td>32.27</td>\n",
       "      <td>3.94</td>\n",
       "      <td>2.75</td>\n",
       "      <td>2300.0</td>\n",
       "      <td>68.950</td>\n",
       "      <td>-2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zambia</th>\n",
       "      <td>46.03</td>\n",
       "      <td>20.00</td>\n",
       "      <td>28.72</td>\n",
       "      <td>2.93</td>\n",
       "      <td>2.33</td>\n",
       "      <td>4000.0</td>\n",
       "      <td>68.900</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zimbabwe</th>\n",
       "      <td>38.90</td>\n",
       "      <td>20.47</td>\n",
       "      <td>31.90</td>\n",
       "      <td>4.27</td>\n",
       "      <td>4.46</td>\n",
       "      <td>2300.0</td>\n",
       "      <td>33.870</td>\n",
       "      <td>2.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>200 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                          0-14 years old %  15-24 years %  25-54 years %  \\\n",
       "COUNTRY                                                                    \n",
       " Afghanistan                         40.92          22.22          30.35   \n",
       " Albania                             18.05          17.47          41.06   \n",
       " Algeria                             29.31          15.30          42.93   \n",
       " Andorra                             14.40           9.64          46.18   \n",
       " Angola                              48.12          18.25          28.03   \n",
       " Antigua and Barbuda                 23.09          16.83          42.19   \n",
       " Argentina                           24.59          15.28          39.38   \n",
       " Armenia                             18.94          12.89          43.43   \n",
       " Aruba                               17.64          12.78          41.72   \n",
       " Australia                           17.80          12.79          41.45   \n",
       " Austria                             14.01          11.07          42.42   \n",
       " Azerbaijan                          22.95          14.84          45.39   \n",
       " Bahamas, The                        22.55          16.40          44.14   \n",
       " Bahrain                             19.08          15.65          56.04   \n",
       " Bangladesh                          27.76          19.36          39.73   \n",
       " Barbados                            17.97          12.74          44.06   \n",
       " Belarus                             15.78          10.29          44.76   \n",
       " Belgium                             17.16          11.34          40.05   \n",
       " Belize                              33.95          20.55          36.62   \n",
       " Benin                               42.65          20.44          30.44   \n",
       " Bermuda                             17.04          12.00          37.24   \n",
       " Bhutan                              25.80          18.81          43.07   \n",
       " Bolivia                             31.85          19.46          37.48   \n",
       " Bosnia and Herzegovina              13.29          11.58          45.88   \n",
       " Botswana                            31.95          18.91          38.45   \n",
       " Brazil                              22.33          16.36          43.86   \n",
       " Brunei                              23.12          17.05          46.75   \n",
       " Bulgaria                            14.58           9.58          43.21   \n",
       " Burkina Faso                        44.88          20.07          29.42   \n",
       " Burma                               26.85          17.75          42.36   \n",
       "...                                    ...            ...            ...   \n",
       " Sri Lanka                           24.06          14.63          41.58   \n",
       " Sudan                               38.68          21.04          32.77   \n",
       " Suriname                            24.62          17.44          44.40   \n",
       " Swaziland                           35.01          22.12          34.60   \n",
       " Sweden                              17.43          11.31          39.42   \n",
       " Switzerland                         15.16          10.88          43.21   \n",
       " Taiwan                              12.88          12.88          46.41   \n",
       " Tajikistan                          32.33          18.61          40.12   \n",
       " Tanzania                            43.74          19.86          29.88   \n",
       " Thailand                            16.93          14.17          46.32   \n",
       " Timor-Leste                         40.91          20.32          29.95   \n",
       " Togo                                40.29          19.20          32.79   \n",
       " Trinidad and Tobago                 19.29          11.88          45.56   \n",
       " Tunisia                             25.15          13.99          43.38   \n",
       " Turkey                              24.68          15.99          43.21   \n",
       " Turkmenistan                        25.79          18.39          43.18   \n",
       " Uganda                              48.05          21.10          26.30   \n",
       " Ukraine                             15.76           9.86          44.29   \n",
       " United Arab Emirates                21.01          13.51          61.14   \n",
       " United Kingdom                      17.53          11.90          40.55   \n",
       " United States                       18.73          13.27          39.45   \n",
       " Uruguay                             20.17          15.69          39.34   \n",
       " Uzbekistan                          23.88          18.52          44.49   \n",
       " Venezuela                           27.36          17.03          40.53   \n",
       " Vietnam                             23.55          16.23          45.56   \n",
       " Virgin Islands                      20.41          11.19          37.07   \n",
       " West Bank                           36.50          21.37          34.10   \n",
       " Yemen                               39.83          21.21          32.27   \n",
       " Zambia                              46.03          20.00          28.72   \n",
       " Zimbabwe                            38.90          20.47          31.90   \n",
       "\n",
       "                          55-64 years %  65 years and above %  GDP (PPP)  \\\n",
       "COUNTRY                                                                    \n",
       " Afghanistan                       3.92                  2.59     1900.0   \n",
       " Albania                          11.54                 11.89    12500.0   \n",
       " Algeria                           6.81                  5.65    15100.0   \n",
       " Andorra                          14.16                 15.61    49900.0   \n",
       " Angola                            3.26                  2.34     6800.0   \n",
       " Antigua and Barbuda               9.83                  8.06    26300.0   \n",
       " Argentina                         9.13                 11.62    20700.0   \n",
       " Armenia                          13.41                 11.33     9100.0   \n",
       " Aruba                            14.28                 13.59    25300.0   \n",
       " Australia                        11.83                 16.14    49900.0   \n",
       " Austria                          13.23                 19.26    49200.0   \n",
       " Azerbaijan                       10.17                  6.64    17400.0   \n",
       " Bahamas, The                      9.16                  7.75    25100.0   \n",
       " Bahrain                           6.28                  2.95    51800.0   \n",
       " Bangladesh                        6.93                  6.23     4200.0   \n",
       " Barbados                         13.43                 11.81    17500.0   \n",
       " Belarus                          14.21                 14.95    18600.0   \n",
       " Belgium                          12.86                 18.58    46300.0   \n",
       " Belize                            4.99                  3.89     8300.0   \n",
       " Benin                             3.61                  2.87     2200.0   \n",
       " Bermuda                          15.73                 17.98    85700.0   \n",
       " Bhutan                            6.03                  6.29     8700.0   \n",
       " Bolivia                           5.90                  5.30     7500.0   \n",
       " Bosnia and Herzegovina           14.83                 14.43    11400.0   \n",
       " Botswana                          5.46                  5.23    18100.0   \n",
       " Brazil                            9.12                  8.33    15500.0   \n",
       " Brunei                            8.23                  4.84    76700.0   \n",
       " Bulgaria                         13.35                 19.28    21600.0   \n",
       " Burkina Faso                      3.20                  2.43     1900.0   \n",
       " Burma                             7.52                  5.53     6300.0   \n",
       "...                                 ...                   ...        ...   \n",
       " Sri Lanka                        10.06                  9.67    13000.0   \n",
       " Sudan                             4.24                  3.27     4600.0   \n",
       " Suriname                          7.54                  6.01    13900.0   \n",
       " Swaziland                         4.30                  3.97     9900.0   \n",
       " Sweden                           11.58                 20.26    51300.0   \n",
       " Switzerland                      12.60                 18.15    61400.0   \n",
       " Taiwan                           14.12                 13.72    49800.0   \n",
       " Tajikistan                        5.62                  3.32     3100.0   \n",
       " Tanzania                          3.51                  3.02     3300.0   \n",
       " Thailand                         12.00                 10.58    17800.0   \n",
       " Timor-Leste                       4.94                  3.87     5000.0   \n",
       " Togo                              4.31                  3.41     1600.0   \n",
       " Trinidad and Tobago              12.61                 10.65    31200.0   \n",
       " Tunisia                           9.54                  7.95    12000.0   \n",
       " Turkey                            8.58                  7.53    26500.0   \n",
       " Turkmenistan                      7.90                  4.74    18700.0   \n",
       " Uganda                            2.57                  1.98     2400.0   \n",
       " Ukraine                          13.80                 16.30     8700.0   \n",
       " United Arab Emirates              3.27                  1.07    68200.0   \n",
       " United Kingdom                   11.98                 18.04    43600.0   \n",
       " United States                    12.91                 15.63    59500.0   \n",
       " Uruguay                          10.56                 14.25    22400.0   \n",
       " Uzbekistan                        7.85                  5.25     7000.0   \n",
       " Venezuela                         7.98                  7.09    12400.0   \n",
       " Vietnam                           8.55                  6.12     6900.0   \n",
       " Virgin Islands                   13.78                 17.55    36100.0   \n",
       " West Bank                         4.54                  3.49     4300.0   \n",
       " Yemen                             3.94                  2.75     2300.0   \n",
       " Zambia                            2.93                  2.33     4000.0   \n",
       " Zimbabwe                          4.27                  4.46     2300.0   \n",
       "\n",
       "                          Total GDP (PPP)  GDP growth rate (%)  \n",
       "COUNTRY                                                         \n",
       " Afghanistan                       69.510                  2.5  \n",
       " Albania                           35.870                  3.7  \n",
       " Algeria                          629.300                  1.5  \n",
       " Andorra                            3.327                 -1.1  \n",
       " Angola                           192.000                  1.5  \n",
       " Antigua and Barbuda                2.390                  2.7  \n",
       " Argentina                        911.500                  2.5  \n",
       " Armenia                           27.210                  3.5  \n",
       " Aruba                              2.516                  2.4  \n",
       " Australia                       1235.000                  2.2  \n",
       " Austria                          434.100                  2.3  \n",
       " Azerbaijan                       166.800                 -1.0  \n",
       " Bahamas, The                       9.339                  1.8  \n",
       " Bahrain                           69.770                  2.5  \n",
       " Bangladesh                       686.500                  7.1  \n",
       " Barbados                           4.919                  0.9  \n",
       " Belarus                          175.900                  0.7  \n",
       " Belgium                          526.400                  1.6  \n",
       " Belize                             3.230                  2.5  \n",
       " Benin                             25.290                  5.4  \n",
       " Bermuda                            5.198                 -2.5  \n",
       " Bhutan                             7.011                  5.9  \n",
       " Bolivia                           83.500                  4.2  \n",
       " Bosnia and Herzegovina            43.850                  2.5  \n",
       " Botswana                          39.550                  4.5  \n",
       " Brazil                          3219.000                  0.7  \n",
       " Brunei                            32.910                 -1.3  \n",
       " Bulgaria                         152.400                  3.6  \n",
       " Burkina Faso                      35.680                  6.4  \n",
       " Burma                            330.900                  7.2  \n",
       "...                                   ...                  ...  \n",
       " Sri Lanka                        278.200                  4.7  \n",
       " Sudan                            186.800                  3.7  \n",
       " Suriname                           7.928                 -1.2  \n",
       " Swaziland                         11.340                  0.3  \n",
       " Sweden                           521.700                  3.1  \n",
       " Switzerland                      516.700                  1.0  \n",
       " Taiwan                          1175.000                  2.0  \n",
       " Tajikistan                        27.670                  4.5  \n",
       " Tanzania                         162.800                  6.5  \n",
       " Thailand                        1229.000                  3.7  \n",
       " Timor-Leste                        6.211                  4.0  \n",
       " Togo                              12.430                  5.0  \n",
       " Trinidad and Tobago               42.780                 -3.2  \n",
       " Tunisia                          135.900                  2.3  \n",
       " Turkey                          2133.000                  5.1  \n",
       " Turkmenistan                     103.500                  6.5  \n",
       " Uganda                            88.610                  4.4  \n",
       " Ukraine                          366.400                  2.0  \n",
       " United Arab Emirates             691.900                  1.3  \n",
       " United Kingdom                  2880.000                  1.7  \n",
       " United States                  19360.000                  2.2  \n",
       " Uruguay                           78.410                  3.5  \n",
       " Uzbekistan                       221.700                  6.0  \n",
       " Venezuela                        389.400                -12.0  \n",
       " Vietnam                          643.900                  6.3  \n",
       " Virgin Islands                     3.792                 -5.4  \n",
       " West Bank                         21.220                  5.3  \n",
       " Yemen                             68.950                 -2.0  \n",
       " Zambia                            68.900                  4.0  \n",
       " Zimbabwe                          33.870                  2.8  \n",
       "\n",
       "[200 rows x 8 columns]"
      ]
     },
     "execution_count": 238,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_combined = df_combined.join(df_GDP_growth)\n",
    "df_combined.dropna(inplace=True)\n",
    "df_combined"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Internet users %"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Internet users % of  Afghanistan : 10.6\n",
      "**Could not find Internet users data!**\n",
      "Internet users % of  Albania : 66.4\n",
      "Internet users % of  Algeria : 42.9\n",
      "Internet users % of  American Samoa : 31.3\n",
      "Internet users % of  Andorra : 97.9\n",
      "Internet users % of  Angola : 13.0\n",
      "Internet users % of  Anguilla : 81.6\n",
      "Internet users % of  Antarctica : 100.0\n",
      "Internet users % of  Antigua and Barbuda : 65.2\n",
      "**Could not find Internet users data!**\n",
      "Internet users % of  Argentina : 70.2\n",
      "Internet users % of  Armenia : 62.0\n",
      "Internet users % of  Aruba : 93.5\n",
      "**Could not find Internet users data!**\n",
      "**Could not find Internet users data!**\n",
      "Internet users % of  Australia : 88.2\n",
      "Internet users % of  Austria : 84.3\n",
      "Internet users % of  Azerbaijan : 78.2\n",
      "Internet users % of  Bahamas, The : 80.0\n",
      "Internet users % of  Bahrain : 98.0\n",
      "**Could not find Internet users data!**\n",
      "Internet users % of  Bangladesh : 18.2\n",
      "Internet users % of  Barbados : 79.5\n",
      "Internet users % of  Belarus : 71.1\n",
      "Internet users % of  Belgium : 86.5\n",
      "Internet users % of  Belize : 44.6\n",
      "Internet users % of  Benin : 12.0\n",
      "Internet users % of  Bermuda : 98.0\n",
      "Internet users % of  Bhutan : 41.8\n",
      "Internet users % of  Bolivia : 39.7\n",
      "Internet users % of  Bosnia and Herzegovina : 69.3\n",
      "Internet users % of  Botswana : 39.4\n",
      "**Could not find Internet users data!**\n",
      "Internet users % of  Brazil : 59.7\n",
      "**Could not find Internet users data!**\n",
      "Internet users % of  British Virgin Islands : 43.6\n",
      "Internet users % of  Brunei : 71.2\n",
      "Internet users % of  Bulgaria : 59.8\n",
      "Internet users % of  Burkina Faso : 14.0\n",
      "Internet users % of  Burma : 25.1\n",
      "Internet users % of  Burundi : 5.2\n",
      "Internet users % of  Cabo Verde : 48.2\n",
      "Internet users % of  Cambodia : 25.6\n",
      "Internet users % of  Cameroon : 25.0\n",
      "Internet users % of  Canada : 89.8\n",
      "Internet users % of  Cayman Islands : 79.0\n",
      "Internet users % of  Central African Republic : 4.6\n",
      "Internet users % of  Chad : 5.0\n",
      "Internet users % of  Chile : 66.0\n",
      "Internet users % of  China : 53.2\n",
      "Internet users % of  Christmas Island : 35.8\n",
      "**Could not find Internet users data!**\n",
      "**Could not find Internet users data!**\n",
      "Internet users % of  Colombia : 58.1\n",
      "Internet users % of  Comoros : 7.9\n",
      "Internet users % of  Congo, Democratic Republic of the : 3.8\n",
      "Internet users % of  Congo, Republic of the : 7.6\n",
      "Internet users % of  Cook Islands : 54.0\n",
      "**Could not find Internet users data!**\n",
      "Internet users % of  Costa Rica : 66.0\n",
      "Internet users % of  Cote d'Ivoire : 26.5\n",
      "Internet users % of  Croatia : 72.7\n",
      "Internet users % of  Cuba : 38.8\n",
      "Internet users % of  Curacao : 93.9\n",
      "Internet users % of  Cyprus : 75.9\n",
      "Internet users % of  Czechia : 76.5\n",
      "Internet users % of  Denmark : 97.0\n",
      "**Could not find Internet users data!**\n",
      "Internet users % of  Djibouti : 13.1\n",
      "Internet users % of  Dominica : 67.0\n",
      "Internet users % of  Dominican Republic : 61.3\n",
      "Internet users % of  Ecuador : 54.1\n",
      "Internet users % of  Egypt : 39.2\n",
      "Internet users % of  El Salvador : 29.0\n",
      "Internet users % of  Equatorial Guinea : 23.8\n",
      "Internet users % of  Eritrea : 1.2\n",
      "Internet users % of  Estonia : 87.2\n",
      "Internet users % of  Ethiopia : 15.4\n",
      "Internet users % of  Falkland Islands (Islas Malvinas) : 98.3\n",
      "Internet users % of  Faroe Islands : 95.1\n",
      "Internet users % of  Fiji : 46.5\n",
      "Internet users % of  Finland : 87.7\n",
      "Internet users % of  France : 85.6\n",
      "Internet users % of  French Polynesia : 68.4\n",
      "**Could not find Internet users data!**\n",
      "Internet users % of  Gabon : 48.1\n",
      "Internet users % of  Gambia, The : 18.5\n",
      "**Could not find Internet users data!**\n",
      "Internet users % of  Georgia : 50.0\n",
      "Internet users % of  Germany : 89.6\n",
      "Internet users % of  Ghana : 34.7\n",
      "Internet users % of  Gibraltar : 94.4\n",
      "Internet users % of  Greece : 69.1\n",
      "Internet users % of  Greenland : 68.5\n",
      "Internet users % of  Grenada : 55.9\n",
      "Internet users % of  Guam : 77.0\n",
      "Internet users % of  Guatemala : 34.5\n",
      "Internet users % of  Guernsey : 83.3\n",
      "Internet users % of  Guinea : 9.8\n",
      "Internet users % of  Guinea-Bissau : 3.8\n",
      "Internet users % of  Guyana : 35.7\n",
      "Internet users % of  Haiti : 12.2\n",
      "**Could not find Internet users data!**\n",
      "**Could not find Internet users data!**\n",
      "Internet users % of  Honduras : 30.0\n",
      "Internet users % of  Hong Kong : 85.0\n",
      "**Could not find Internet users data!**\n",
      "Internet users % of  Hungary : 79.3\n",
      "Internet users % of  Iceland : 98.2\n",
      "Internet users % of  India : 29.5\n",
      "**Could not find Internet users data!**\n",
      "Internet users % of  Indonesia : 25.4\n",
      "Internet users % of  Iran : 44.1\n",
      "Internet users % of  Iraq : 21.2\n",
      "Internet users % of  Ireland : 82.2\n",
      "**Could not find Internet users data!**\n",
      "Internet users % of  Israel : 79.8\n",
      "Internet users % of  Italy : 61.3\n",
      "Internet users % of  Jamaica : 45.0\n",
      "**Could not find Internet users data!**\n",
      "Internet users % of  Japan : 92.0\n",
      "**Could not find Internet users data!**\n",
      "Internet users % of  Jersey : 59.6\n",
      "**Could not find Internet users data!**\n",
      "Internet users % of  Jordan : 62.3\n",
      "Internet users % of  Kazakhstan : 76.8\n",
      "Internet users % of  Kenya : 26.0\n",
      "**Could not find Internet users data!**\n",
      "Internet users % of  Kiribati : 13.7\n",
      "**Could not find Internet users data!**\n",
      "Internet users % of  Korea, South : 89.9\n",
      "**Could not find Internet users data!**\n",
      "Internet users % of  Kuwait : 78.4\n",
      "Internet users % of  Kyrgyzstan : 34.5\n",
      "Internet users % of  Laos : 18.2\n",
      "Internet users % of  Latvia : 79.9\n",
      "Internet users % of  Lebanon : 76.1\n",
      "Internet users % of  Lesotho : 27.4\n",
      "Internet users % of  Liberia : 7.3\n",
      "Internet users % of  Libya : 20.3\n",
      "Internet users % of  Liechtenstein : 98.1\n",
      "Internet users % of  Lithuania : 74.4\n",
      "Internet users % of  Luxembourg : 97.5\n",
      "Internet users % of  Macau : 77.6\n",
      "**Could not find Internet users data!**\n",
      "Internet users % of  Madagascar : 4.7\n",
      "Internet users % of  Malawi : 9.6\n",
      "Internet users % of  Malaysia : 78.8\n",
      "Internet users % of  Maldives : 59.1\n",
      "Internet users % of  Mali : 11.1\n",
      "Internet users % of  Malta : 77.3\n",
      "Internet users % of  Marshall Islands : 29.8\n",
      "Internet users % of  Mauritania : 18.0\n",
      "Internet users % of  Mauritius : 53.2\n",
      "Internet users % of  Mexico : 59.5\n",
      "Internet users % of  Micronesia, Federated States of : 31.5\n",
      "**Could not find Internet users data!**\n",
      "Internet users % of  Moldova : 71.0\n",
      "Internet users % of  Monaco : 95.2\n",
      "Internet users % of  Mongolia : 22.3\n",
      "Internet users % of  Montenegro : 69.9\n",
      "Internet users % of  Montserrat : 54.6\n",
      "Internet users % of  Morocco : 58.3\n",
      "Internet users % of  Mozambique : 17.5\n",
      "Internet users % of  Namibia : 31.0\n",
      "Internet users % of  Nauru : 53.5\n",
      "**Could not find Internet users data!**\n",
      "Internet users % of  Nepal : 19.7\n",
      "Internet users % of  Netherlands : 90.4\n",
      "Internet users % of  New Caledonia : 74.0\n",
      "Internet users % of  New Zealand : 88.5\n",
      "Internet users % of  Nicaragua : 24.6\n",
      "Internet users % of  Niger : 4.3\n",
      "Internet users % of  Nigeria : 25.7\n",
      "Internet users % of  Niue : 91.6\n",
      "Internet users % of  Norfolk Island : 34.6\n",
      "Internet users % of  Northern Mariana Islands : 30.6\n",
      "Internet users % of  Norway : 97.3\n",
      "Internet users % of  Oman : 69.8\n",
      "**Could not find Internet users data!**\n",
      "Internet users % of  Pakistan : 15.5\n",
      "Internet users % of  Palau : 36.0\n",
      "**Could not find Internet users data!**\n",
      "Internet users % of  Panama : 54.0\n",
      "Internet users % of  Papua New Guinea : 9.6\n",
      "**Could not find Internet users data!**\n",
      "Internet users % of  Paraguay : 51.3\n",
      "Internet users % of  Peru : 45.5\n",
      "Internet users % of  Philippines : 55.5\n",
      "Internet users % of  Pitcairn Islands : 100.0\n",
      "Internet users % of  Poland : 73.3\n",
      "Internet users % of  Portugal : 70.4\n",
      "Internet users % of  Puerto Rico : 80.3\n",
      "Internet users % of  Qatar : 94.3\n",
      "Internet users % of  Romania : 59.5\n",
      "Internet users % of  Russia : 76.4\n",
      "Internet users % of  Rwanda : 20.0\n",
      "**Could not find Internet users data!**\n",
      "Internet users % of  Saint Helena, Ascension, and Tristan da Cunha : 23.1\n",
      "Internet users % of  Saint Kitts and Nevis : 75.7\n",
      "Internet users % of  Saint Lucia : 52.4\n",
      "Internet users % of  Saint Martin : 3.5\n",
      "Internet users % of  Saint Pierre and Miquelon : 79.5\n",
      "Internet users % of  Saint Vincent and the Grenadines : 51.8\n",
      "Internet users % of  Samoa : 29.4\n",
      "Internet users % of  San Marino : 52.6\n",
      "Internet users % of  Sao Tome and Principe : 25.8\n",
      "Internet users % of  Saudi Arabia : 73.8\n",
      "Internet users % of  Senegal : 25.7\n",
      "Internet users % of  Serbia : 67.1\n",
      "Internet users % of  Seychelles : 56.5\n",
      "Internet users % of  Sierra Leone : 11.8\n",
      "Internet users % of  Singapore : 81.0\n",
      "**Could not find Internet users data!**\n",
      "Internet users % of  Slovakia : 80.5\n",
      "Internet users % of  Slovenia : 75.5\n",
      "Internet users % of  Solomon Islands : 11.0\n",
      "Internet users % of  Somalia : 1.9\n",
      "Internet users % of  South Africa : 54.0\n",
      "**Could not find Internet users data!**\n",
      "**Could not find Internet users data!**\n",
      "**Could not find Internet users data!**\n",
      "Internet users % of  Spain : 80.6\n",
      "**Could not find Internet users data!**\n",
      "Internet users % of  Sri Lanka : 32.1\n",
      "Internet users % of  Sudan : 28.0\n",
      "Internet users % of  Suriname : 45.4\n",
      "**Could not find Internet users data!**\n",
      "Internet users % of  Swaziland : 28.6\n",
      "Internet users % of  Sweden : 91.5\n",
      "Internet users % of  Switzerland : 89.4\n",
      "Internet users % of  Syria : 31.9\n",
      "Internet users % of  Taiwan : 88.0\n",
      "Internet users % of  Tajikistan : 20.5\n",
      "Internet users % of  Tanzania : 13.0\n",
      "Internet users % of  Thailand : 47.5\n",
      "Internet users % of  Timor-Leste : 25.2\n",
      "Internet users % of  Togo : 11.3\n",
      "Internet users % of  Tokelau : 60.2\n",
      "Internet users % of  Tonga : 40.0\n",
      "Internet users % of  Trinidad and Tobago : 69.2\n",
      "Internet users % of  Tunisia : 50.9\n",
      "Internet users % of  Turkey : 58.3\n",
      "Internet users % of  Turkmenistan : 18.0\n",
      "**Could not find Internet users data!**\n",
      "Internet users % of  Tuvalu : 46.0\n",
      "Internet users % of  Uganda : 21.9\n",
      "Internet users % of  Ukraine : 52.5\n",
      "Internet users % of  United Arab Emirates : 90.6\n",
      "Internet users % of  United Kingdom : 94.8\n",
      "Internet users % of  United States : 76.2\n",
      "**Could not find Internet users data!**\n",
      "Internet users % of  Uruguay : 66.4\n",
      "Internet users % of  Uzbekistan : 46.8\n",
      "Internet users % of  Vanuatu : 24.0\n",
      "Internet users % of  Venezuela : 60.0\n",
      "Internet users % of  Vietnam : 52.7\n",
      "Internet users % of  Virgin Islands : 54.8\n",
      "**Could not find Internet users data!**\n",
      "Internet users % of  Wallis and Futuna : 22.1\n",
      "**Could not find Internet users data!**\n",
      "**Could not find Internet users data!**\n",
      "Internet users % of  Yemen : 24.6\n",
      "Internet users % of  Zambia : 25.5\n",
      "Internet users % of  Zimbabwe : 23.1\n",
      "\n",
      "Finished finding all Internet users data\n"
     ]
    }
   ],
   "source": [
    "# Initialize dictionary for holding the data\n",
    "Internet_user = {}\n",
    "# Iterate over every country\n",
    "for i in range(1,len(country_names)-1):\n",
    "    country= country_names[i]\n",
    "    txt=text_data[country]       \n",
    "    pos = txt.find('Internet users:')\n",
    "    if pos!=-1: \n",
    "        pos= pos+len('Internet users: ')\n",
    "        string = txt[pos:pos+50]\n",
    "        #print(string)\n",
    "        start=re.search('percent of population: ',string)\n",
    "        end = re.search('%',string)\n",
    "        if (start!=None and end!=None):\n",
    "            start=start.end()\n",
    "            end=end.start()\n",
    "            a=string[start:end]\n",
    "            if a[-1].isdigit():\n",
    "                a = float(a)\n",
    "                print(f\"Internet users % of {country}: {a}\")\n",
    "                # Insert the data in the dictionary\n",
    "                Internet_user[country]=a\n",
    "            else:\n",
    "                print(\"**Could not find Internet users data!**\")\n",
    "        else:\n",
    "            print(\"**Could not find Internet users data!**\")\n",
    "    else:\n",
    "        print(\"**Could not find Internet users data!**\")\n",
    "\n",
    "print (\"\\nFinished finding all Internet users data\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Internet users (%)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COUNTRY</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Afghanistan</th>\n",
       "      <td>10.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>66.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>42.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>American Samoa</th>\n",
       "      <td>31.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andorra</th>\n",
       "      <td>97.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Angola</th>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Anguilla</th>\n",
       "      <td>81.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Antarctica</th>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Antigua and Barbuda</th>\n",
       "      <td>65.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Argentina</th>\n",
       "      <td>70.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Armenia</th>\n",
       "      <td>62.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aruba</th>\n",
       "      <td>93.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <td>88.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Austria</th>\n",
       "      <td>84.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Azerbaijan</th>\n",
       "      <td>78.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bahamas, The</th>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bahrain</th>\n",
       "      <td>98.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bangladesh</th>\n",
       "      <td>18.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Barbados</th>\n",
       "      <td>79.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belarus</th>\n",
       "      <td>71.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belgium</th>\n",
       "      <td>86.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belize</th>\n",
       "      <td>44.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Benin</th>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bermuda</th>\n",
       "      <td>98.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bhutan</th>\n",
       "      <td>41.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bolivia</th>\n",
       "      <td>39.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bosnia and Herzegovina</th>\n",
       "      <td>69.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Botswana</th>\n",
       "      <td>39.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brazil</th>\n",
       "      <td>59.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>British Virgin Islands</th>\n",
       "      <td>43.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Switzerland</th>\n",
       "      <td>89.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Syria</th>\n",
       "      <td>31.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Taiwan</th>\n",
       "      <td>88.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tajikistan</th>\n",
       "      <td>20.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tanzania</th>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thailand</th>\n",
       "      <td>47.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Timor-Leste</th>\n",
       "      <td>25.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Togo</th>\n",
       "      <td>11.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tokelau</th>\n",
       "      <td>60.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tonga</th>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Trinidad and Tobago</th>\n",
       "      <td>69.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tunisia</th>\n",
       "      <td>50.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Turkey</th>\n",
       "      <td>58.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Turkmenistan</th>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tuvalu</th>\n",
       "      <td>46.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uganda</th>\n",
       "      <td>21.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ukraine</th>\n",
       "      <td>52.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Arab Emirates</th>\n",
       "      <td>90.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>94.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>76.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uruguay</th>\n",
       "      <td>66.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uzbekistan</th>\n",
       "      <td>46.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vanuatu</th>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Venezuela</th>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vietnam</th>\n",
       "      <td>52.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Virgin Islands</th>\n",
       "      <td>54.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wallis and Futuna</th>\n",
       "      <td>22.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yemen</th>\n",
       "      <td>24.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zambia</th>\n",
       "      <td>25.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zimbabwe</th>\n",
       "      <td>23.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>224 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Internet users (%)\n",
       "COUNTRY                                     \n",
       " Afghanistan                            10.6\n",
       " Albania                                66.4\n",
       " Algeria                                42.9\n",
       " American Samoa                         31.3\n",
       " Andorra                                97.9\n",
       " Angola                                 13.0\n",
       " Anguilla                               81.6\n",
       " Antarctica                            100.0\n",
       " Antigua and Barbuda                    65.2\n",
       " Argentina                              70.2\n",
       " Armenia                                62.0\n",
       " Aruba                                  93.5\n",
       " Australia                              88.2\n",
       " Austria                                84.3\n",
       " Azerbaijan                             78.2\n",
       " Bahamas, The                           80.0\n",
       " Bahrain                                98.0\n",
       " Bangladesh                             18.2\n",
       " Barbados                               79.5\n",
       " Belarus                                71.1\n",
       " Belgium                                86.5\n",
       " Belize                                 44.6\n",
       " Benin                                  12.0\n",
       " Bermuda                                98.0\n",
       " Bhutan                                 41.8\n",
       " Bolivia                                39.7\n",
       " Bosnia and Herzegovina                 69.3\n",
       " Botswana                               39.4\n",
       " Brazil                                 59.7\n",
       " British Virgin Islands                 43.6\n",
       "...                                      ...\n",
       " Switzerland                            89.4\n",
       " Syria                                  31.9\n",
       " Taiwan                                 88.0\n",
       " Tajikistan                             20.5\n",
       " Tanzania                               13.0\n",
       " Thailand                               47.5\n",
       " Timor-Leste                            25.2\n",
       " Togo                                   11.3\n",
       " Tokelau                                60.2\n",
       " Tonga                                  40.0\n",
       " Trinidad and Tobago                    69.2\n",
       " Tunisia                                50.9\n",
       " Turkey                                 58.3\n",
       " Turkmenistan                           18.0\n",
       " Tuvalu                                 46.0\n",
       " Uganda                                 21.9\n",
       " Ukraine                                52.5\n",
       " United Arab Emirates                   90.6\n",
       " United Kingdom                         94.8\n",
       " United States                          76.2\n",
       " Uruguay                                66.4\n",
       " Uzbekistan                             46.8\n",
       " Vanuatu                                24.0\n",
       " Venezuela                              60.0\n",
       " Vietnam                                52.7\n",
       " Virgin Islands                         54.8\n",
       " Wallis and Futuna                      22.1\n",
       " Yemen                                  24.6\n",
       " Zambia                                 25.5\n",
       " Zimbabwe                               23.1\n",
       "\n",
       "[224 rows x 1 columns]"
      ]
     },
     "execution_count": 240,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_Internet_user=pd.Series(Internet_user).to_frame()\n",
    "df_Internet_user.columns=['Internet users (%)']\n",
    "df_Internet_user.index.name='COUNTRY'\n",
    "df_Internet_user"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0-14 years old %</th>\n",
       "      <th>15-24 years %</th>\n",
       "      <th>25-54 years %</th>\n",
       "      <th>55-64 years %</th>\n",
       "      <th>65 years and above %</th>\n",
       "      <th>GDP (PPP)</th>\n",
       "      <th>Total GDP (PPP)</th>\n",
       "      <th>GDP growth rate (%)</th>\n",
       "      <th>Internet users (%)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COUNTRY</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Afghanistan</th>\n",
       "      <td>40.92</td>\n",
       "      <td>22.22</td>\n",
       "      <td>30.35</td>\n",
       "      <td>3.92</td>\n",
       "      <td>2.59</td>\n",
       "      <td>1900.0</td>\n",
       "      <td>69.510</td>\n",
       "      <td>2.5</td>\n",
       "      <td>10.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>18.05</td>\n",
       "      <td>17.47</td>\n",
       "      <td>41.06</td>\n",
       "      <td>11.54</td>\n",
       "      <td>11.89</td>\n",
       "      <td>12500.0</td>\n",
       "      <td>35.870</td>\n",
       "      <td>3.7</td>\n",
       "      <td>66.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>29.31</td>\n",
       "      <td>15.30</td>\n",
       "      <td>42.93</td>\n",
       "      <td>6.81</td>\n",
       "      <td>5.65</td>\n",
       "      <td>15100.0</td>\n",
       "      <td>629.300</td>\n",
       "      <td>1.5</td>\n",
       "      <td>42.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andorra</th>\n",
       "      <td>14.40</td>\n",
       "      <td>9.64</td>\n",
       "      <td>46.18</td>\n",
       "      <td>14.16</td>\n",
       "      <td>15.61</td>\n",
       "      <td>49900.0</td>\n",
       "      <td>3.327</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>97.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Angola</th>\n",
       "      <td>48.12</td>\n",
       "      <td>18.25</td>\n",
       "      <td>28.03</td>\n",
       "      <td>3.26</td>\n",
       "      <td>2.34</td>\n",
       "      <td>6800.0</td>\n",
       "      <td>192.000</td>\n",
       "      <td>1.5</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Antigua and Barbuda</th>\n",
       "      <td>23.09</td>\n",
       "      <td>16.83</td>\n",
       "      <td>42.19</td>\n",
       "      <td>9.83</td>\n",
       "      <td>8.06</td>\n",
       "      <td>26300.0</td>\n",
       "      <td>2.390</td>\n",
       "      <td>2.7</td>\n",
       "      <td>65.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Argentina</th>\n",
       "      <td>24.59</td>\n",
       "      <td>15.28</td>\n",
       "      <td>39.38</td>\n",
       "      <td>9.13</td>\n",
       "      <td>11.62</td>\n",
       "      <td>20700.0</td>\n",
       "      <td>911.500</td>\n",
       "      <td>2.5</td>\n",
       "      <td>70.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Armenia</th>\n",
       "      <td>18.94</td>\n",
       "      <td>12.89</td>\n",
       "      <td>43.43</td>\n",
       "      <td>13.41</td>\n",
       "      <td>11.33</td>\n",
       "      <td>9100.0</td>\n",
       "      <td>27.210</td>\n",
       "      <td>3.5</td>\n",
       "      <td>62.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aruba</th>\n",
       "      <td>17.64</td>\n",
       "      <td>12.78</td>\n",
       "      <td>41.72</td>\n",
       "      <td>14.28</td>\n",
       "      <td>13.59</td>\n",
       "      <td>25300.0</td>\n",
       "      <td>2.516</td>\n",
       "      <td>2.4</td>\n",
       "      <td>93.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <td>17.80</td>\n",
       "      <td>12.79</td>\n",
       "      <td>41.45</td>\n",
       "      <td>11.83</td>\n",
       "      <td>16.14</td>\n",
       "      <td>49900.0</td>\n",
       "      <td>1235.000</td>\n",
       "      <td>2.2</td>\n",
       "      <td>88.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Austria</th>\n",
       "      <td>14.01</td>\n",
       "      <td>11.07</td>\n",
       "      <td>42.42</td>\n",
       "      <td>13.23</td>\n",
       "      <td>19.26</td>\n",
       "      <td>49200.0</td>\n",
       "      <td>434.100</td>\n",
       "      <td>2.3</td>\n",
       "      <td>84.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Azerbaijan</th>\n",
       "      <td>22.95</td>\n",
       "      <td>14.84</td>\n",
       "      <td>45.39</td>\n",
       "      <td>10.17</td>\n",
       "      <td>6.64</td>\n",
       "      <td>17400.0</td>\n",
       "      <td>166.800</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>78.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bahamas, The</th>\n",
       "      <td>22.55</td>\n",
       "      <td>16.40</td>\n",
       "      <td>44.14</td>\n",
       "      <td>9.16</td>\n",
       "      <td>7.75</td>\n",
       "      <td>25100.0</td>\n",
       "      <td>9.339</td>\n",
       "      <td>1.8</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bahrain</th>\n",
       "      <td>19.08</td>\n",
       "      <td>15.65</td>\n",
       "      <td>56.04</td>\n",
       "      <td>6.28</td>\n",
       "      <td>2.95</td>\n",
       "      <td>51800.0</td>\n",
       "      <td>69.770</td>\n",
       "      <td>2.5</td>\n",
       "      <td>98.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bangladesh</th>\n",
       "      <td>27.76</td>\n",
       "      <td>19.36</td>\n",
       "      <td>39.73</td>\n",
       "      <td>6.93</td>\n",
       "      <td>6.23</td>\n",
       "      <td>4200.0</td>\n",
       "      <td>686.500</td>\n",
       "      <td>7.1</td>\n",
       "      <td>18.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Barbados</th>\n",
       "      <td>17.97</td>\n",
       "      <td>12.74</td>\n",
       "      <td>44.06</td>\n",
       "      <td>13.43</td>\n",
       "      <td>11.81</td>\n",
       "      <td>17500.0</td>\n",
       "      <td>4.919</td>\n",
       "      <td>0.9</td>\n",
       "      <td>79.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belarus</th>\n",
       "      <td>15.78</td>\n",
       "      <td>10.29</td>\n",
       "      <td>44.76</td>\n",
       "      <td>14.21</td>\n",
       "      <td>14.95</td>\n",
       "      <td>18600.0</td>\n",
       "      <td>175.900</td>\n",
       "      <td>0.7</td>\n",
       "      <td>71.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belgium</th>\n",
       "      <td>17.16</td>\n",
       "      <td>11.34</td>\n",
       "      <td>40.05</td>\n",
       "      <td>12.86</td>\n",
       "      <td>18.58</td>\n",
       "      <td>46300.0</td>\n",
       "      <td>526.400</td>\n",
       "      <td>1.6</td>\n",
       "      <td>86.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belize</th>\n",
       "      <td>33.95</td>\n",
       "      <td>20.55</td>\n",
       "      <td>36.62</td>\n",
       "      <td>4.99</td>\n",
       "      <td>3.89</td>\n",
       "      <td>8300.0</td>\n",
       "      <td>3.230</td>\n",
       "      <td>2.5</td>\n",
       "      <td>44.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Benin</th>\n",
       "      <td>42.65</td>\n",
       "      <td>20.44</td>\n",
       "      <td>30.44</td>\n",
       "      <td>3.61</td>\n",
       "      <td>2.87</td>\n",
       "      <td>2200.0</td>\n",
       "      <td>25.290</td>\n",
       "      <td>5.4</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bermuda</th>\n",
       "      <td>17.04</td>\n",
       "      <td>12.00</td>\n",
       "      <td>37.24</td>\n",
       "      <td>15.73</td>\n",
       "      <td>17.98</td>\n",
       "      <td>85700.0</td>\n",
       "      <td>5.198</td>\n",
       "      <td>-2.5</td>\n",
       "      <td>98.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bhutan</th>\n",
       "      <td>25.80</td>\n",
       "      <td>18.81</td>\n",
       "      <td>43.07</td>\n",
       "      <td>6.03</td>\n",
       "      <td>6.29</td>\n",
       "      <td>8700.0</td>\n",
       "      <td>7.011</td>\n",
       "      <td>5.9</td>\n",
       "      <td>41.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bolivia</th>\n",
       "      <td>31.85</td>\n",
       "      <td>19.46</td>\n",
       "      <td>37.48</td>\n",
       "      <td>5.90</td>\n",
       "      <td>5.30</td>\n",
       "      <td>7500.0</td>\n",
       "      <td>83.500</td>\n",
       "      <td>4.2</td>\n",
       "      <td>39.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bosnia and Herzegovina</th>\n",
       "      <td>13.29</td>\n",
       "      <td>11.58</td>\n",
       "      <td>45.88</td>\n",
       "      <td>14.83</td>\n",
       "      <td>14.43</td>\n",
       "      <td>11400.0</td>\n",
       "      <td>43.850</td>\n",
       "      <td>2.5</td>\n",
       "      <td>69.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Botswana</th>\n",
       "      <td>31.95</td>\n",
       "      <td>18.91</td>\n",
       "      <td>38.45</td>\n",
       "      <td>5.46</td>\n",
       "      <td>5.23</td>\n",
       "      <td>18100.0</td>\n",
       "      <td>39.550</td>\n",
       "      <td>4.5</td>\n",
       "      <td>39.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brazil</th>\n",
       "      <td>22.33</td>\n",
       "      <td>16.36</td>\n",
       "      <td>43.86</td>\n",
       "      <td>9.12</td>\n",
       "      <td>8.33</td>\n",
       "      <td>15500.0</td>\n",
       "      <td>3219.000</td>\n",
       "      <td>0.7</td>\n",
       "      <td>59.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brunei</th>\n",
       "      <td>23.12</td>\n",
       "      <td>17.05</td>\n",
       "      <td>46.75</td>\n",
       "      <td>8.23</td>\n",
       "      <td>4.84</td>\n",
       "      <td>76700.0</td>\n",
       "      <td>32.910</td>\n",
       "      <td>-1.3</td>\n",
       "      <td>71.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bulgaria</th>\n",
       "      <td>14.58</td>\n",
       "      <td>9.58</td>\n",
       "      <td>43.21</td>\n",
       "      <td>13.35</td>\n",
       "      <td>19.28</td>\n",
       "      <td>21600.0</td>\n",
       "      <td>152.400</td>\n",
       "      <td>3.6</td>\n",
       "      <td>59.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Burkina Faso</th>\n",
       "      <td>44.88</td>\n",
       "      <td>20.07</td>\n",
       "      <td>29.42</td>\n",
       "      <td>3.20</td>\n",
       "      <td>2.43</td>\n",
       "      <td>1900.0</td>\n",
       "      <td>35.680</td>\n",
       "      <td>6.4</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Burma</th>\n",
       "      <td>26.85</td>\n",
       "      <td>17.75</td>\n",
       "      <td>42.36</td>\n",
       "      <td>7.52</td>\n",
       "      <td>5.53</td>\n",
       "      <td>6300.0</td>\n",
       "      <td>330.900</td>\n",
       "      <td>7.2</td>\n",
       "      <td>25.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spain</th>\n",
       "      <td>15.38</td>\n",
       "      <td>9.58</td>\n",
       "      <td>44.91</td>\n",
       "      <td>12.14</td>\n",
       "      <td>17.98</td>\n",
       "      <td>38200.0</td>\n",
       "      <td>1769.000</td>\n",
       "      <td>3.1</td>\n",
       "      <td>80.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sri Lanka</th>\n",
       "      <td>24.06</td>\n",
       "      <td>14.63</td>\n",
       "      <td>41.58</td>\n",
       "      <td>10.06</td>\n",
       "      <td>9.67</td>\n",
       "      <td>13000.0</td>\n",
       "      <td>278.200</td>\n",
       "      <td>4.7</td>\n",
       "      <td>32.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sudan</th>\n",
       "      <td>38.68</td>\n",
       "      <td>21.04</td>\n",
       "      <td>32.77</td>\n",
       "      <td>4.24</td>\n",
       "      <td>3.27</td>\n",
       "      <td>4600.0</td>\n",
       "      <td>186.800</td>\n",
       "      <td>3.7</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Suriname</th>\n",
       "      <td>24.62</td>\n",
       "      <td>17.44</td>\n",
       "      <td>44.40</td>\n",
       "      <td>7.54</td>\n",
       "      <td>6.01</td>\n",
       "      <td>13900.0</td>\n",
       "      <td>7.928</td>\n",
       "      <td>-1.2</td>\n",
       "      <td>45.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Swaziland</th>\n",
       "      <td>35.01</td>\n",
       "      <td>22.12</td>\n",
       "      <td>34.60</td>\n",
       "      <td>4.30</td>\n",
       "      <td>3.97</td>\n",
       "      <td>9900.0</td>\n",
       "      <td>11.340</td>\n",
       "      <td>0.3</td>\n",
       "      <td>28.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sweden</th>\n",
       "      <td>17.43</td>\n",
       "      <td>11.31</td>\n",
       "      <td>39.42</td>\n",
       "      <td>11.58</td>\n",
       "      <td>20.26</td>\n",
       "      <td>51300.0</td>\n",
       "      <td>521.700</td>\n",
       "      <td>3.1</td>\n",
       "      <td>91.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Switzerland</th>\n",
       "      <td>15.16</td>\n",
       "      <td>10.88</td>\n",
       "      <td>43.21</td>\n",
       "      <td>12.60</td>\n",
       "      <td>18.15</td>\n",
       "      <td>61400.0</td>\n",
       "      <td>516.700</td>\n",
       "      <td>1.0</td>\n",
       "      <td>89.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Taiwan</th>\n",
       "      <td>12.88</td>\n",
       "      <td>12.88</td>\n",
       "      <td>46.41</td>\n",
       "      <td>14.12</td>\n",
       "      <td>13.72</td>\n",
       "      <td>49800.0</td>\n",
       "      <td>1175.000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>88.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tajikistan</th>\n",
       "      <td>32.33</td>\n",
       "      <td>18.61</td>\n",
       "      <td>40.12</td>\n",
       "      <td>5.62</td>\n",
       "      <td>3.32</td>\n",
       "      <td>3100.0</td>\n",
       "      <td>27.670</td>\n",
       "      <td>4.5</td>\n",
       "      <td>20.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tanzania</th>\n",
       "      <td>43.74</td>\n",
       "      <td>19.86</td>\n",
       "      <td>29.88</td>\n",
       "      <td>3.51</td>\n",
       "      <td>3.02</td>\n",
       "      <td>3300.0</td>\n",
       "      <td>162.800</td>\n",
       "      <td>6.5</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thailand</th>\n",
       "      <td>16.93</td>\n",
       "      <td>14.17</td>\n",
       "      <td>46.32</td>\n",
       "      <td>12.00</td>\n",
       "      <td>10.58</td>\n",
       "      <td>17800.0</td>\n",
       "      <td>1229.000</td>\n",
       "      <td>3.7</td>\n",
       "      <td>47.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Timor-Leste</th>\n",
       "      <td>40.91</td>\n",
       "      <td>20.32</td>\n",
       "      <td>29.95</td>\n",
       "      <td>4.94</td>\n",
       "      <td>3.87</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>6.211</td>\n",
       "      <td>4.0</td>\n",
       "      <td>25.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Togo</th>\n",
       "      <td>40.29</td>\n",
       "      <td>19.20</td>\n",
       "      <td>32.79</td>\n",
       "      <td>4.31</td>\n",
       "      <td>3.41</td>\n",
       "      <td>1600.0</td>\n",
       "      <td>12.430</td>\n",
       "      <td>5.0</td>\n",
       "      <td>11.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Trinidad and Tobago</th>\n",
       "      <td>19.29</td>\n",
       "      <td>11.88</td>\n",
       "      <td>45.56</td>\n",
       "      <td>12.61</td>\n",
       "      <td>10.65</td>\n",
       "      <td>31200.0</td>\n",
       "      <td>42.780</td>\n",
       "      <td>-3.2</td>\n",
       "      <td>69.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tunisia</th>\n",
       "      <td>25.15</td>\n",
       "      <td>13.99</td>\n",
       "      <td>43.38</td>\n",
       "      <td>9.54</td>\n",
       "      <td>7.95</td>\n",
       "      <td>12000.0</td>\n",
       "      <td>135.900</td>\n",
       "      <td>2.3</td>\n",
       "      <td>50.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Turkey</th>\n",
       "      <td>24.68</td>\n",
       "      <td>15.99</td>\n",
       "      <td>43.21</td>\n",
       "      <td>8.58</td>\n",
       "      <td>7.53</td>\n",
       "      <td>26500.0</td>\n",
       "      <td>2133.000</td>\n",
       "      <td>5.1</td>\n",
       "      <td>58.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Turkmenistan</th>\n",
       "      <td>25.79</td>\n",
       "      <td>18.39</td>\n",
       "      <td>43.18</td>\n",
       "      <td>7.90</td>\n",
       "      <td>4.74</td>\n",
       "      <td>18700.0</td>\n",
       "      <td>103.500</td>\n",
       "      <td>6.5</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uganda</th>\n",
       "      <td>48.05</td>\n",
       "      <td>21.10</td>\n",
       "      <td>26.30</td>\n",
       "      <td>2.57</td>\n",
       "      <td>1.98</td>\n",
       "      <td>2400.0</td>\n",
       "      <td>88.610</td>\n",
       "      <td>4.4</td>\n",
       "      <td>21.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ukraine</th>\n",
       "      <td>15.76</td>\n",
       "      <td>9.86</td>\n",
       "      <td>44.29</td>\n",
       "      <td>13.80</td>\n",
       "      <td>16.30</td>\n",
       "      <td>8700.0</td>\n",
       "      <td>366.400</td>\n",
       "      <td>2.0</td>\n",
       "      <td>52.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Arab Emirates</th>\n",
       "      <td>21.01</td>\n",
       "      <td>13.51</td>\n",
       "      <td>61.14</td>\n",
       "      <td>3.27</td>\n",
       "      <td>1.07</td>\n",
       "      <td>68200.0</td>\n",
       "      <td>691.900</td>\n",
       "      <td>1.3</td>\n",
       "      <td>90.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>17.53</td>\n",
       "      <td>11.90</td>\n",
       "      <td>40.55</td>\n",
       "      <td>11.98</td>\n",
       "      <td>18.04</td>\n",
       "      <td>43600.0</td>\n",
       "      <td>2880.000</td>\n",
       "      <td>1.7</td>\n",
       "      <td>94.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>18.73</td>\n",
       "      <td>13.27</td>\n",
       "      <td>39.45</td>\n",
       "      <td>12.91</td>\n",
       "      <td>15.63</td>\n",
       "      <td>59500.0</td>\n",
       "      <td>19360.000</td>\n",
       "      <td>2.2</td>\n",
       "      <td>76.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uruguay</th>\n",
       "      <td>20.17</td>\n",
       "      <td>15.69</td>\n",
       "      <td>39.34</td>\n",
       "      <td>10.56</td>\n",
       "      <td>14.25</td>\n",
       "      <td>22400.0</td>\n",
       "      <td>78.410</td>\n",
       "      <td>3.5</td>\n",
       "      <td>66.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uzbekistan</th>\n",
       "      <td>23.88</td>\n",
       "      <td>18.52</td>\n",
       "      <td>44.49</td>\n",
       "      <td>7.85</td>\n",
       "      <td>5.25</td>\n",
       "      <td>7000.0</td>\n",
       "      <td>221.700</td>\n",
       "      <td>6.0</td>\n",
       "      <td>46.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Venezuela</th>\n",
       "      <td>27.36</td>\n",
       "      <td>17.03</td>\n",
       "      <td>40.53</td>\n",
       "      <td>7.98</td>\n",
       "      <td>7.09</td>\n",
       "      <td>12400.0</td>\n",
       "      <td>389.400</td>\n",
       "      <td>-12.0</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vietnam</th>\n",
       "      <td>23.55</td>\n",
       "      <td>16.23</td>\n",
       "      <td>45.56</td>\n",
       "      <td>8.55</td>\n",
       "      <td>6.12</td>\n",
       "      <td>6900.0</td>\n",
       "      <td>643.900</td>\n",
       "      <td>6.3</td>\n",
       "      <td>52.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Virgin Islands</th>\n",
       "      <td>20.41</td>\n",
       "      <td>11.19</td>\n",
       "      <td>37.07</td>\n",
       "      <td>13.78</td>\n",
       "      <td>17.55</td>\n",
       "      <td>36100.0</td>\n",
       "      <td>3.792</td>\n",
       "      <td>-5.4</td>\n",
       "      <td>54.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yemen</th>\n",
       "      <td>39.83</td>\n",
       "      <td>21.21</td>\n",
       "      <td>32.27</td>\n",
       "      <td>3.94</td>\n",
       "      <td>2.75</td>\n",
       "      <td>2300.0</td>\n",
       "      <td>68.950</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>24.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zambia</th>\n",
       "      <td>46.03</td>\n",
       "      <td>20.00</td>\n",
       "      <td>28.72</td>\n",
       "      <td>2.93</td>\n",
       "      <td>2.33</td>\n",
       "      <td>4000.0</td>\n",
       "      <td>68.900</td>\n",
       "      <td>4.0</td>\n",
       "      <td>25.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zimbabwe</th>\n",
       "      <td>38.90</td>\n",
       "      <td>20.47</td>\n",
       "      <td>31.90</td>\n",
       "      <td>4.27</td>\n",
       "      <td>4.46</td>\n",
       "      <td>2300.0</td>\n",
       "      <td>33.870</td>\n",
       "      <td>2.8</td>\n",
       "      <td>23.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>194 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                          0-14 years old %  15-24 years %  25-54 years %  \\\n",
       "COUNTRY                                                                    \n",
       " Afghanistan                         40.92          22.22          30.35   \n",
       " Albania                             18.05          17.47          41.06   \n",
       " Algeria                             29.31          15.30          42.93   \n",
       " Andorra                             14.40           9.64          46.18   \n",
       " Angola                              48.12          18.25          28.03   \n",
       " Antigua and Barbuda                 23.09          16.83          42.19   \n",
       " Argentina                           24.59          15.28          39.38   \n",
       " Armenia                             18.94          12.89          43.43   \n",
       " Aruba                               17.64          12.78          41.72   \n",
       " Australia                           17.80          12.79          41.45   \n",
       " Austria                             14.01          11.07          42.42   \n",
       " Azerbaijan                          22.95          14.84          45.39   \n",
       " Bahamas, The                        22.55          16.40          44.14   \n",
       " Bahrain                             19.08          15.65          56.04   \n",
       " Bangladesh                          27.76          19.36          39.73   \n",
       " Barbados                            17.97          12.74          44.06   \n",
       " Belarus                             15.78          10.29          44.76   \n",
       " Belgium                             17.16          11.34          40.05   \n",
       " Belize                              33.95          20.55          36.62   \n",
       " Benin                               42.65          20.44          30.44   \n",
       " Bermuda                             17.04          12.00          37.24   \n",
       " Bhutan                              25.80          18.81          43.07   \n",
       " Bolivia                             31.85          19.46          37.48   \n",
       " Bosnia and Herzegovina              13.29          11.58          45.88   \n",
       " Botswana                            31.95          18.91          38.45   \n",
       " Brazil                              22.33          16.36          43.86   \n",
       " Brunei                              23.12          17.05          46.75   \n",
       " Bulgaria                            14.58           9.58          43.21   \n",
       " Burkina Faso                        44.88          20.07          29.42   \n",
       " Burma                               26.85          17.75          42.36   \n",
       "...                                    ...            ...            ...   \n",
       " Spain                               15.38           9.58          44.91   \n",
       " Sri Lanka                           24.06          14.63          41.58   \n",
       " Sudan                               38.68          21.04          32.77   \n",
       " Suriname                            24.62          17.44          44.40   \n",
       " Swaziland                           35.01          22.12          34.60   \n",
       " Sweden                              17.43          11.31          39.42   \n",
       " Switzerland                         15.16          10.88          43.21   \n",
       " Taiwan                              12.88          12.88          46.41   \n",
       " Tajikistan                          32.33          18.61          40.12   \n",
       " Tanzania                            43.74          19.86          29.88   \n",
       " Thailand                            16.93          14.17          46.32   \n",
       " Timor-Leste                         40.91          20.32          29.95   \n",
       " Togo                                40.29          19.20          32.79   \n",
       " Trinidad and Tobago                 19.29          11.88          45.56   \n",
       " Tunisia                             25.15          13.99          43.38   \n",
       " Turkey                              24.68          15.99          43.21   \n",
       " Turkmenistan                        25.79          18.39          43.18   \n",
       " Uganda                              48.05          21.10          26.30   \n",
       " Ukraine                             15.76           9.86          44.29   \n",
       " United Arab Emirates                21.01          13.51          61.14   \n",
       " United Kingdom                      17.53          11.90          40.55   \n",
       " United States                       18.73          13.27          39.45   \n",
       " Uruguay                             20.17          15.69          39.34   \n",
       " Uzbekistan                          23.88          18.52          44.49   \n",
       " Venezuela                           27.36          17.03          40.53   \n",
       " Vietnam                             23.55          16.23          45.56   \n",
       " Virgin Islands                      20.41          11.19          37.07   \n",
       " Yemen                               39.83          21.21          32.27   \n",
       " Zambia                              46.03          20.00          28.72   \n",
       " Zimbabwe                            38.90          20.47          31.90   \n",
       "\n",
       "                          55-64 years %  65 years and above %  GDP (PPP)  \\\n",
       "COUNTRY                                                                    \n",
       " Afghanistan                       3.92                  2.59     1900.0   \n",
       " Albania                          11.54                 11.89    12500.0   \n",
       " Algeria                           6.81                  5.65    15100.0   \n",
       " Andorra                          14.16                 15.61    49900.0   \n",
       " Angola                            3.26                  2.34     6800.0   \n",
       " Antigua and Barbuda               9.83                  8.06    26300.0   \n",
       " Argentina                         9.13                 11.62    20700.0   \n",
       " Armenia                          13.41                 11.33     9100.0   \n",
       " Aruba                            14.28                 13.59    25300.0   \n",
       " Australia                        11.83                 16.14    49900.0   \n",
       " Austria                          13.23                 19.26    49200.0   \n",
       " Azerbaijan                       10.17                  6.64    17400.0   \n",
       " Bahamas, The                      9.16                  7.75    25100.0   \n",
       " Bahrain                           6.28                  2.95    51800.0   \n",
       " Bangladesh                        6.93                  6.23     4200.0   \n",
       " Barbados                         13.43                 11.81    17500.0   \n",
       " Belarus                          14.21                 14.95    18600.0   \n",
       " Belgium                          12.86                 18.58    46300.0   \n",
       " Belize                            4.99                  3.89     8300.0   \n",
       " Benin                             3.61                  2.87     2200.0   \n",
       " Bermuda                          15.73                 17.98    85700.0   \n",
       " Bhutan                            6.03                  6.29     8700.0   \n",
       " Bolivia                           5.90                  5.30     7500.0   \n",
       " Bosnia and Herzegovina           14.83                 14.43    11400.0   \n",
       " Botswana                          5.46                  5.23    18100.0   \n",
       " Brazil                            9.12                  8.33    15500.0   \n",
       " Brunei                            8.23                  4.84    76700.0   \n",
       " Bulgaria                         13.35                 19.28    21600.0   \n",
       " Burkina Faso                      3.20                  2.43     1900.0   \n",
       " Burma                             7.52                  5.53     6300.0   \n",
       "...                                 ...                   ...        ...   \n",
       " Spain                            12.14                 17.98    38200.0   \n",
       " Sri Lanka                        10.06                  9.67    13000.0   \n",
       " Sudan                             4.24                  3.27     4600.0   \n",
       " Suriname                          7.54                  6.01    13900.0   \n",
       " Swaziland                         4.30                  3.97     9900.0   \n",
       " Sweden                           11.58                 20.26    51300.0   \n",
       " Switzerland                      12.60                 18.15    61400.0   \n",
       " Taiwan                           14.12                 13.72    49800.0   \n",
       " Tajikistan                        5.62                  3.32     3100.0   \n",
       " Tanzania                          3.51                  3.02     3300.0   \n",
       " Thailand                         12.00                 10.58    17800.0   \n",
       " Timor-Leste                       4.94                  3.87     5000.0   \n",
       " Togo                              4.31                  3.41     1600.0   \n",
       " Trinidad and Tobago              12.61                 10.65    31200.0   \n",
       " Tunisia                           9.54                  7.95    12000.0   \n",
       " Turkey                            8.58                  7.53    26500.0   \n",
       " Turkmenistan                      7.90                  4.74    18700.0   \n",
       " Uganda                            2.57                  1.98     2400.0   \n",
       " Ukraine                          13.80                 16.30     8700.0   \n",
       " United Arab Emirates              3.27                  1.07    68200.0   \n",
       " United Kingdom                   11.98                 18.04    43600.0   \n",
       " United States                    12.91                 15.63    59500.0   \n",
       " Uruguay                          10.56                 14.25    22400.0   \n",
       " Uzbekistan                        7.85                  5.25     7000.0   \n",
       " Venezuela                         7.98                  7.09    12400.0   \n",
       " Vietnam                           8.55                  6.12     6900.0   \n",
       " Virgin Islands                   13.78                 17.55    36100.0   \n",
       " Yemen                             3.94                  2.75     2300.0   \n",
       " Zambia                            2.93                  2.33     4000.0   \n",
       " Zimbabwe                          4.27                  4.46     2300.0   \n",
       "\n",
       "                          Total GDP (PPP)  GDP growth rate (%)  \\\n",
       "COUNTRY                                                          \n",
       " Afghanistan                       69.510                  2.5   \n",
       " Albania                           35.870                  3.7   \n",
       " Algeria                          629.300                  1.5   \n",
       " Andorra                            3.327                 -1.1   \n",
       " Angola                           192.000                  1.5   \n",
       " Antigua and Barbuda                2.390                  2.7   \n",
       " Argentina                        911.500                  2.5   \n",
       " Armenia                           27.210                  3.5   \n",
       " Aruba                              2.516                  2.4   \n",
       " Australia                       1235.000                  2.2   \n",
       " Austria                          434.100                  2.3   \n",
       " Azerbaijan                       166.800                 -1.0   \n",
       " Bahamas, The                       9.339                  1.8   \n",
       " Bahrain                           69.770                  2.5   \n",
       " Bangladesh                       686.500                  7.1   \n",
       " Barbados                           4.919                  0.9   \n",
       " Belarus                          175.900                  0.7   \n",
       " Belgium                          526.400                  1.6   \n",
       " Belize                             3.230                  2.5   \n",
       " Benin                             25.290                  5.4   \n",
       " Bermuda                            5.198                 -2.5   \n",
       " Bhutan                             7.011                  5.9   \n",
       " Bolivia                           83.500                  4.2   \n",
       " Bosnia and Herzegovina            43.850                  2.5   \n",
       " Botswana                          39.550                  4.5   \n",
       " Brazil                          3219.000                  0.7   \n",
       " Brunei                            32.910                 -1.3   \n",
       " Bulgaria                         152.400                  3.6   \n",
       " Burkina Faso                      35.680                  6.4   \n",
       " Burma                            330.900                  7.2   \n",
       "...                                   ...                  ...   \n",
       " Spain                           1769.000                  3.1   \n",
       " Sri Lanka                        278.200                  4.7   \n",
       " Sudan                            186.800                  3.7   \n",
       " Suriname                           7.928                 -1.2   \n",
       " Swaziland                         11.340                  0.3   \n",
       " Sweden                           521.700                  3.1   \n",
       " Switzerland                      516.700                  1.0   \n",
       " Taiwan                          1175.000                  2.0   \n",
       " Tajikistan                        27.670                  4.5   \n",
       " Tanzania                         162.800                  6.5   \n",
       " Thailand                        1229.000                  3.7   \n",
       " Timor-Leste                        6.211                  4.0   \n",
       " Togo                              12.430                  5.0   \n",
       " Trinidad and Tobago               42.780                 -3.2   \n",
       " Tunisia                          135.900                  2.3   \n",
       " Turkey                          2133.000                  5.1   \n",
       " Turkmenistan                     103.500                  6.5   \n",
       " Uganda                            88.610                  4.4   \n",
       " Ukraine                          366.400                  2.0   \n",
       " United Arab Emirates             691.900                  1.3   \n",
       " United Kingdom                  2880.000                  1.7   \n",
       " United States                  19360.000                  2.2   \n",
       " Uruguay                           78.410                  3.5   \n",
       " Uzbekistan                       221.700                  6.0   \n",
       " Venezuela                        389.400                -12.0   \n",
       " Vietnam                          643.900                  6.3   \n",
       " Virgin Islands                     3.792                 -5.4   \n",
       " Yemen                             68.950                 -2.0   \n",
       " Zambia                            68.900                  4.0   \n",
       " Zimbabwe                          33.870                  2.8   \n",
       "\n",
       "                          Internet users (%)  \n",
       "COUNTRY                                       \n",
       " Afghanistan                            10.6  \n",
       " Albania                                66.4  \n",
       " Algeria                                42.9  \n",
       " Andorra                                97.9  \n",
       " Angola                                 13.0  \n",
       " Antigua and Barbuda                    65.2  \n",
       " Argentina                              70.2  \n",
       " Armenia                                62.0  \n",
       " Aruba                                  93.5  \n",
       " Australia                              88.2  \n",
       " Austria                                84.3  \n",
       " Azerbaijan                             78.2  \n",
       " Bahamas, The                           80.0  \n",
       " Bahrain                                98.0  \n",
       " Bangladesh                             18.2  \n",
       " Barbados                               79.5  \n",
       " Belarus                                71.1  \n",
       " Belgium                                86.5  \n",
       " Belize                                 44.6  \n",
       " Benin                                  12.0  \n",
       " Bermuda                                98.0  \n",
       " Bhutan                                 41.8  \n",
       " Bolivia                                39.7  \n",
       " Bosnia and Herzegovina                 69.3  \n",
       " Botswana                               39.4  \n",
       " Brazil                                 59.7  \n",
       " Brunei                                 71.2  \n",
       " Bulgaria                               59.8  \n",
       " Burkina Faso                           14.0  \n",
       " Burma                                  25.1  \n",
       "...                                      ...  \n",
       " Spain                                  80.6  \n",
       " Sri Lanka                              32.1  \n",
       " Sudan                                  28.0  \n",
       " Suriname                               45.4  \n",
       " Swaziland                              28.6  \n",
       " Sweden                                 91.5  \n",
       " Switzerland                            89.4  \n",
       " Taiwan                                 88.0  \n",
       " Tajikistan                             20.5  \n",
       " Tanzania                               13.0  \n",
       " Thailand                               47.5  \n",
       " Timor-Leste                            25.2  \n",
       " Togo                                   11.3  \n",
       " Trinidad and Tobago                    69.2  \n",
       " Tunisia                                50.9  \n",
       " Turkey                                 58.3  \n",
       " Turkmenistan                           18.0  \n",
       " Uganda                                 21.9  \n",
       " Ukraine                                52.5  \n",
       " United Arab Emirates                   90.6  \n",
       " United Kingdom                         94.8  \n",
       " United States                          76.2  \n",
       " Uruguay                                66.4  \n",
       " Uzbekistan                             46.8  \n",
       " Venezuela                              60.0  \n",
       " Vietnam                                52.7  \n",
       " Virgin Islands                         54.8  \n",
       " Yemen                                  24.6  \n",
       " Zambia                                 25.5  \n",
       " Zimbabwe                               23.1  \n",
       "\n",
       "[194 rows x 9 columns]"
      ]
     },
     "execution_count": 241,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_combined = df_combined.join(df_Internet_user)\n",
    "df_combined.dropna(inplace=True)\n",
    "df_combined"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2307bfa4630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2307bef66a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2307c5a6ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x23074d84358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2307c234630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2307310e160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x23074d9b320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2307c3282e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x23074dc6860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for c in df_combined.columns:\n",
    "    df_combined.hist(c,bins=20)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\tirtha\\python\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0-14 years old %</th>\n",
       "      <th>15-24 years %</th>\n",
       "      <th>25-54 years %</th>\n",
       "      <th>55-64 years %</th>\n",
       "      <th>65 years and above %</th>\n",
       "      <th>GDP (PPP)</th>\n",
       "      <th>Total GDP (PPP)</th>\n",
       "      <th>GDP growth rate (%)</th>\n",
       "      <th>Internet users (%)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COUNTRY</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Angola</th>\n",
       "      <td>48.12</td>\n",
       "      <td>18.25</td>\n",
       "      <td>28.03</td>\n",
       "      <td>3.26</td>\n",
       "      <td>2.34</td>\n",
       "      <td>6800.0</td>\n",
       "      <td>192.00</td>\n",
       "      <td>1.5</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Congo, Republic of the</th>\n",
       "      <td>41.67</td>\n",
       "      <td>17.10</td>\n",
       "      <td>33.89</td>\n",
       "      <td>4.29</td>\n",
       "      <td>3.06</td>\n",
       "      <td>6700.0</td>\n",
       "      <td>29.16</td>\n",
       "      <td>-3.6</td>\n",
       "      <td>7.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Laos</th>\n",
       "      <td>32.76</td>\n",
       "      <td>21.17</td>\n",
       "      <td>36.70</td>\n",
       "      <td>5.48</td>\n",
       "      <td>3.89</td>\n",
       "      <td>7400.0</td>\n",
       "      <td>49.21</td>\n",
       "      <td>6.9</td>\n",
       "      <td>18.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pakistan</th>\n",
       "      <td>31.36</td>\n",
       "      <td>21.14</td>\n",
       "      <td>37.45</td>\n",
       "      <td>5.57</td>\n",
       "      <td>4.48</td>\n",
       "      <td>5400.0</td>\n",
       "      <td>1056.00</td>\n",
       "      <td>5.3</td>\n",
       "      <td>15.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Turkmenistan</th>\n",
       "      <td>25.79</td>\n",
       "      <td>18.39</td>\n",
       "      <td>43.18</td>\n",
       "      <td>7.90</td>\n",
       "      <td>4.74</td>\n",
       "      <td>18700.0</td>\n",
       "      <td>103.50</td>\n",
       "      <td>6.5</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          0-14 years old %  15-24 years %  25-54 years %  \\\n",
       "COUNTRY                                                                    \n",
       " Angola                              48.12          18.25          28.03   \n",
       " Congo, Republic of the              41.67          17.10          33.89   \n",
       " Laos                                32.76          21.17          36.70   \n",
       " Pakistan                            31.36          21.14          37.45   \n",
       " Turkmenistan                        25.79          18.39          43.18   \n",
       "\n",
       "                          55-64 years %  65 years and above %  GDP (PPP)  \\\n",
       "COUNTRY                                                                    \n",
       " Angola                            3.26                  2.34     6800.0   \n",
       " Congo, Republic of the            4.29                  3.06     6700.0   \n",
       " Laos                              5.48                  3.89     7400.0   \n",
       " Pakistan                          5.57                  4.48     5400.0   \n",
       " Turkmenistan                      7.90                  4.74    18700.0   \n",
       "\n",
       "                          Total GDP (PPP)  GDP growth rate (%)  \\\n",
       "COUNTRY                                                          \n",
       " Angola                            192.00                  1.5   \n",
       " Congo, Republic of the             29.16                 -3.6   \n",
       " Laos                               49.21                  6.9   \n",
       " Pakistan                         1056.00                  5.3   \n",
       " Turkmenistan                      103.50                  6.5   \n",
       "\n",
       "                          Internet users (%)  \n",
       "COUNTRY                                       \n",
       " Angola                                 13.0  \n",
       " Congo, Republic of the                  7.6  \n",
       " Laos                                   18.2  \n",
       " Pakistan                               15.5  \n",
       " Turkmenistan                           18.0  "
      ]
     },
     "execution_count": 243,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_combined[df_combined['Internet users (%)']<20][df_combined['GDP (PPP)']>5000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "from scipy.stats import linregress"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Internet users % vs. GDP/capita for middle-income (GDP) countries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\tirtha\\python\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:7: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  import sys\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2307e5cd668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('ticks')\n",
    "# Create a filtered data frame and x and y arrays\n",
    "filter_gdp = df_combined['Total GDP (PPP)'] > 50\n",
    "filter_low_income=df_combined['GDP (PPP)']>5000\n",
    "filter_high_income=df_combined['GDP (PPP)']<25000\n",
    "\n",
    "df_filtered = df_combined[filter_gdp][filter_low_income][filter_high_income]\n",
    "x_array = df_filtered['Internet users (%)']\n",
    "y_array = df_filtered['GDP (PPP)']\n",
    "    \n",
    "fig, ax = plt.subplots()\n",
    "ax.grid(True)\n",
    "ax.set_xlabel(\"Internet users (%)\",fontsize=18)\n",
    "ax.set_ylabel(\"GDP/capita (PPP) in US dollars\",fontsize=18)\n",
    "ax.set_title(\"Internet users % vs GDP/capita (PPP)\\n Middle-GDP countries\",fontsize=20)\n",
    "fig.set_size_inches(6, 5)\n",
    "g=sns.regplot(data=df_filtered,x=x_array,y=y_array,ax=ax,\n",
    "                  line_kws={'ls':'--','linewidth':3,'color':'k'},\n",
    "                 scatter_kws={'edgecolor':'k'})\n",
    "# Linear regression calculations\n",
    "slope,intercept,rvalue,pvalue,stderr = linregress(x_array,y_array)\n",
    "# Get the bottom, top, left, and right limits\n",
    "bottom,top = ax.get_ylim()\n",
    "left,right = ax.get_xlim()\n",
    "    \n",
    "ax.text(s=f\"Slope: {round(slope,3)},\\n$R^2$: {round(rvalue,3)}\",\n",
    "            x=left+(right-left)*0.1,y=top-(top-bottom)*0.25,fontsize=15,color='red')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Internet users % vs. GDP/capita for low-income (GDP) countries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\tirtha\\python\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:5: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  \"\"\"\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2307e57ea90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a filtered data frame and x and y arrays\n",
    "filter_gdp = df_combined['Total GDP (PPP)'] > 50\n",
    "filter_low_income=df_combined['GDP (PPP)']<5000\n",
    "\n",
    "df_filtered = df_combined[filter_gdp][filter_low_income]\n",
    "x_array = df_filtered['Internet users (%)']\n",
    "y_array = df_filtered['GDP (PPP)']\n",
    "    \n",
    "fig, ax = plt.subplots()\n",
    "ax.grid(True)\n",
    "ax.set_xlabel(\"Internet users (%)\",fontsize=18)\n",
    "ax.set_ylabel(\"GDP/capita (PPP), in US $\",fontsize=18)\n",
    "ax.set_title(\"Internet users % vs GDP/capita (PPP)\\n low-GDP countries\",fontsize=20)\n",
    "fig.set_size_inches(6, 5)\n",
    "g=sns.regplot(data=df_filtered,x=x_array,y=y_array,ax=ax,\n",
    "                  line_kws={'ls':'--','linewidth':3,'color':'k'},\n",
    "                 scatter_kws={'edgecolor':'k'})\n",
    "# Linear regression calculations\n",
    "slope,intercept,rvalue,pvalue,stderr = linregress(x_array,y_array)\n",
    "# Get the bottom, top, left, and right limits\n",
    "bottom,top = ax.get_ylim()\n",
    "left,right = ax.get_xlim()\n",
    "    \n",
    "ax.text(s=f\"Slope: {round(slope,3)},\\n$R^2$: {round(rvalue,3)}\",\n",
    "            x=left+(right-left)*0.1,y=top-(top-bottom)*0.25,fontsize=15,color='red')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\tirtha\\python\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:5: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  \"\"\"\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2307e57eba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a filtered data frame and x and y arrays\n",
    "filter_gdp = df_combined['Total GDP (PPP)'] > 50\n",
    "filter_high_income=df_combined['GDP (PPP)']>25000\n",
    "\n",
    "df_filtered = df_combined[filter_gdp][filter_high_income]\n",
    "x_array = df_filtered['Internet users (%)']\n",
    "y_array = df_filtered['GDP (PPP)']\n",
    "    \n",
    "fig, ax = plt.subplots()\n",
    "ax.grid(True)\n",
    "ax.set_xlabel(\"Internet users (%)\",fontsize=18)\n",
    "ax.set_ylabel(\"GDP/capita (PPP)\",fontsize=18)\n",
    "ax.set_title(\"Internet users % vs GDP/capita (PPP)\\n high-GDP countries\",fontsize=20)\n",
    "fig.set_size_inches(6, 5)\n",
    "g=sns.regplot(data=df_filtered,x=x_array,y=y_array,ax=ax,\n",
    "                  line_kws={'ls':'--','linewidth':3,'color':'k'},\n",
    "                 scatter_kws={'edgecolor':'k'})\n",
    "# Linear regression calculations\n",
    "slope,intercept,rvalue,pvalue,stderr = linregress(x_array,y_array)\n",
    "# Get the bottom, top, left, and right limits\n",
    "bottom,top = ax.get_ylim()\n",
    "left,right = ax.get_xlim()\n",
    "    \n",
    "ax.text(s=f\"Slope: {round(slope,3)},\\n$R^2$: {round(rvalue,3)}\",\n",
    "            x=left+(right-left)*0.1,y=top-(top-bottom)*0.25,fontsize=15,color='red')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0-14 years old %          27.34\n",
       "15-24 years %             17.90\n",
       "25-54 years %             41.08\n",
       "55-64 years %              7.45\n",
       "65 years and above %       6.24\n",
       "GDP (PPP)               7200.00\n",
       "Total GDP (PPP)         9447.00\n",
       "GDP growth rate (%)        6.70\n",
       "Internet users (%)        29.50\n",
       "Name:  India , dtype: float64"
      ]
     },
     "execution_count": 248,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_combined.loc[' India ']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['0-14 years old %', '15-24 years %', '25-54 years %', '55-64 years %',\n",
       "       '65 years and above %', 'GDP (PPP)', 'Total GDP (PPP)',\n",
       "       'GDP growth rate (%)', 'Internet users (%)'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 249,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_combined.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\tirtha\\python\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:3: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2307c1b1a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2307c40e7f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2307c277390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2307c409128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2307c2d5cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "filter1=df_combined['GDP (PPP)']>15000\n",
    "filter2=df_combined['GDP (PPP)']<50000\n",
    "df_filtered = df_combined[filter1][filter2]\n",
    "\n",
    "for c in df_filtered.columns[0:5]:\n",
    "    df_filtered.plot.scatter(x=c, y='Internet users (%)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.2"
  },
  "latex_envs": {
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