notes/day-one-lab.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Day One: Sample data acquisition skill\n",
    "\n",
    "I'm teaching this class from the fundamentals upwards, which means that we study data formats and how the Internet works so that we can properly learn to scrape data from the web and use web APIs.   A problem with that approach is that it leaves some of the really cool stuff towards the end of the class. To mitigate this, I like to motivate the work you will do over the next few weeks by providing a simple example of how easy it is to go collected data from a cooperative website.  Most websites are uncooperative and so we have to learn to deal with those, which we will do during the class.  For now, let's figure out how to scrape some [data on the coronavirus from Wikipedia](https://en.wikipedia.org/wiki/COVID-19_pandemic_by_country_and_territory).\n",
    "\n",
    "I'd like everyone to read through this notebook and manually type all the code into your own notebook. This will give you some idea of where we are going and how straightforward it is in many cases.  It's a good idea NOT to download this notebook--try creating your own and retype the commands."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inspect the first table element\n",
    "\n",
    "Using Chrome, go to URL:\n",
    "\n",
    "[https://en.wikipedia.org/wiki/COVID-19_pandemic_by_country_and_territory](https://en.wikipedia.org/wiki/COVID-19_pandemic_by_country_and_territory)\n",
    "\n",
    "and then right-click on the start of the table in the right gutter of the Wikipedia page and select <b>Inspect</b> from the drop-down menu. It should something that looks like this:\n",
    "\n",
    "<img src=\"figures/covid-inspect.png\" width=\"70%\">\n",
    "\n",
    "That shows you the raw HTML and what it corresponds to visually.  The next step is to use a program to extract that HTML."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get the raw HTML from the website"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[33mWARNING: Retrying (Retry(total=4, connect=None, read=None, redirect=None, status=None)) after connection broken by 'ReadTimeoutError(\"HTTPSConnectionPool(host='pypi.org', port=443): Read timed out. (read timeout=15)\")': /simple/requests/\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "!pip install -q -U requests              # we need these libraries\n",
    "!pip install -q -U beautifulsoup4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<!DOCTYPE html>\n",
      "<html class=\"client-nojs\" lang=\"en\" dir=\"ltr\">\n",
      "<head>\n",
      "<meta charset=\"UTF-8\"/>\n",
      "<title>COVID-19 pandemic by country and territory - Wikipedia</title>\n",
      "<script>document.documentElement.className=\"client-js\";RLCONF={\"wgBreakFrames\":false,\"wgSeparatorTransformTable\":[\"\",\"\"],\"wgDigitTransformTable\":[\"\",\"\"],\"wgDefaultDateFormat\":\"dmy\",\"wgMonthNames\":[\"\",\"January\",\"February\",\"March\",\"April\",\"May\",\"June\",\"July\",\"August\",\"September\",\"October\",\"November\",\"December\"],\"wgRequestId\":\"38b871e3-930f-4e81-8582-14653472bb3e\",\"wgCSPNonce\":false,\"wgCanonicalNamespace\":\"\",\"wgCanonicalSpecialPageName\":false,\"wgNamespaceNumber\":0,\"wgPageName\":\"COVID-19_pandemic_by_country_and_territory\",\"wgTitle\":\"COVID-19 pandemic by country and territory\",\"wgCurRevisionId\":1105745864,\"wgRevisionId\":1105745864,\"wgArticleId\":62938755,\"wgIsArticle\":true,\"wgIsRedirect\":false,\"wgAction\":\"view\",\"wgUserName\":null,\"wgUserGroups\":[\"*\"],\"wgCategories\":[\"CS1 European Spanish-language sources (es-es)\",\"CS1 Russian-langu\n"
     ]
    }
   ],
   "source": [
    "import requests\n",
    "\n",
    "CovidURL = \"https://en.wikipedia.org/wiki/COVID-19_pandemic_by_country_and_territory\"\n",
    "response = requests.get(CovidURL)\n",
    "print(response.text[0:1000])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get specific tag using BeautifulSoup\n",
    "\n",
    "Now let's treat the text as HTML not just English text.  Then we can ask for a specific tag such as the title:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TITLE <title>COVID-19 pandemic by country and territory - Wikipedia</title>\n"
     ]
    }
   ],
   "source": [
    "from bs4 import BeautifulSoup\n",
    "\n",
    "soup = BeautifulSoup(response.text, \"html.parser\")\n",
    "title = soup.find('title')\n",
    "print(\"TITLE\", title)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Get all text elements from all HTML tags\n",
    "\n",
    "We can also ask for all of the text elements not inside HTML tags:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    COVID-19 pandemic by country and territory - Wikipedia                                           COVID-19 pandemic by country and territory  From Wikipedia, the free encyclopedia    Jump to navigation Jump to search General overview of worldwide cases and deaths   COVID-19 pandemicConfirmed deaths per 100,000 population,[1][note 1] as of 18 August 2022   555+  100–555  18–100  3.3–18  0.6–3.3  0.1–0.6  <0.1  No deaths or no dataDiseaseCOVID-19Virus strainSARS-CoV-2SourceProbably bats, possib\n"
     ]
    }
   ],
   "source": [
    "print(soup.text[0:500].replace('\\n', ' '))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Find all tables\n",
    "\n",
    "BeautifulSoup has a mechanism to find all of the tables (HTML `table` text) in an HTML document:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "tables = soup.findAll('table')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first table looks like this on the page:\n",
    " \n",
    "<img src=\"figures/covid-table-0.png\" width=\"30%\">\n",
    "\n",
    "and we can get the HTML representing each table:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<table class=\"infobox\"><tbody><tr><th class=\"infobox-above\" colspan=\"2\" style=\"background:#FFCCCB\">COVID-19 pandemic</th></tr><tr><td class=\"infobox-image\" colspan=\"2\" style=\"border-bottom:#aaa 1px solid;\"><a class=\"image\" href=\"/wiki/File:COVID-19_Outbreak_World_Map_Total_Deaths_per_Capita.svg\"><img alt=\"COVID-19 Outbreak World Map Total Deaths per Capita.svg\" data-file-height=\"1398\" data-file-width=\"2754\" decoding=\"async\" height=\"168\" src=\"//upload.wikimedia.org/wikipedia/commons/thumb/8/88/COVID-19_Outbreak_World_Map_Total_Deaths_per_Capita.svg/330px-COVID-19_Outbreak_World_Map_Total_Deaths_per_Capita.svg.png\" srcset=\"//upload.wikimedia.org/wikipedia/commons/thumb/8/88/COVID-19_Outbreak_World_Map_Total_Deaths_per_Capita.svg/495px-COVID-19_Outbreak_World_Map_Total_Deaths_per_Capita.svg.png 1.5x, //upload.wikimedia.org/wikipedia/commons/thumb/8/88/COVID-19_Outbreak_World_Map_Total_Deaths_per_Capita.svg/660px-COVID-19_Outbreak_World_Map_Total_Deaths_per_Capita.svg.png 2x\" width=\"330\"/>\n"
     ]
    }
   ],
   "source": [
    "t = str(tables[0])\n",
    "print(t[0:1000])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That is raw HTML representing the table, but we can use a Jupyter notebook trick to display that text as HTML:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"infobox\"><tbody><tr><th class=\"infobox-above\" colspan=\"2\" style=\"background:#FFCCCB\">COVID-19 pandemic</th></tr><tr><td class=\"infobox-image\" colspan=\"2\" style=\"border-bottom:#aaa 1px solid;\"><a class=\"image\" href=\"/wiki/File:COVID-19_Outbreak_World_Map_Total_Deaths_per_Capita.svg\"><img alt=\"COVID-19 Outbreak World Map Total Deaths per Capita.svg\" data-file-height=\"1398\" data-file-width=\"2754\" decoding=\"async\" height=\"168\" src=\"//upload.wikimedia.org/wikipedia/commons/thumb/8/88/COVID-19_Outbreak_World_Map_Total_Deaths_per_Capita.svg/330px-COVID-19_Outbreak_World_Map_Total_Deaths_per_Capita.svg.png\" srcset=\"//upload.wikimedia.org/wikipedia/commons/thumb/8/88/COVID-19_Outbreak_World_Map_Total_Deaths_per_Capita.svg/495px-COVID-19_Outbreak_World_Map_Total_Deaths_per_Capita.svg.png 1.5x, //upload.wikimedia.org/wikipedia/commons/thumb/8/88/COVID-19_Outbreak_World_Map_Total_Deaths_per_Capita.svg/660px-COVID-19_Outbreak_World_Map_Total_Deaths_per_Capita.svg.png 2x\" width=\"330\"/></a><div class=\"infobox-caption\" style=\"text-align:left;\"><div class=\"center\" style=\"width:auto; margin-left:auto; margin-right:auto;\">Confirmed deaths per 100,000 population,<sup class=\"reference\" id=\"cite_ref-deaths-per-million-map_1-0\"><a href=\"#cite_note-deaths-per-million-map-1\">[1]</a></sup><sup class=\"reference\" id=\"cite_ref-OWID-JHU_2-0\"><a href=\"#cite_note-OWID-JHU-2\">[note 1]</a></sup><br/> as of 18 August 2022</div>\n",
       "<style data-mw-deduplicate=\"TemplateStyles:r998391716\">.mw-parser-output .div-col{margin-top:0.3em;column-width:30em}.mw-parser-output .div-col-small{font-size:90%}.mw-parser-output .div-col-rules{column-rule:1px solid #aaa}.mw-parser-output .div-col dl,.mw-parser-output .div-col ol,.mw-parser-output .div-col ul{margin-top:0}.mw-parser-output .div-col li,.mw-parser-output .div-col dd{page-break-inside:avoid;break-inside:avoid-column}</style><div class=\"div-col\" style=\"column-width: 8em;column-gap: 0;padding-left:3em;padding-bottom:5px;\"><div class=\"plainlist\"><ul><li style=\"margin: 0;\"><style data-mw-deduplicate=\"TemplateStyles:r981673959\">.mw-parser-output .legend{page-break-inside:avoid;break-inside:avoid-column}.mw-parser-output .legend-color{display:inline-block;min-width:1.25em;height:1.25em;line-height:1.25;margin:1px 0;text-align:center;border:1px solid black;background-color:transparent;color:black}.mw-parser-output .legend-text{}</style><div class=\"legend\"><span class=\"legend-color mw-no-invert\" style=\"background-color:#080414; color:white;\"> </span> 555+</div></li><li style=\"margin: 0;\"><link href=\"mw-data:TemplateStyles:r981673959\" rel=\"mw-deduplicated-inline-style\"/><div class=\"legend\"><span class=\"legend-color mw-no-invert\" style=\"background-color:#402050; color:white;\"> </span> 100–555</div></li><li style=\"margin: 0;\"><link href=\"mw-data:TemplateStyles:r981673959\" rel=\"mw-deduplicated-inline-style\"/><div class=\"legend\"><span class=\"legend-color mw-no-invert\" style=\"background-color:#253494; color:white;\"> </span> 18–100</div></li><li style=\"margin: 0;\"><link href=\"mw-data:TemplateStyles:r981673959\" rel=\"mw-deduplicated-inline-style\"/><div class=\"legend\"><span class=\"legend-color mw-no-invert\" style=\"background-color:#2c7fb8; color:black;\"> </span> 3.3–18</div></li><li style=\"margin: 0;\"><link href=\"mw-data:TemplateStyles:r981673959\" rel=\"mw-deduplicated-inline-style\"/><div class=\"legend\"><span class=\"legend-color mw-no-invert\" style=\"background-color:#41b6c4; color:black;\"> </span> 0.6–3.3</div></li><li style=\"margin: 0;\"><link href=\"mw-data:TemplateStyles:r981673959\" rel=\"mw-deduplicated-inline-style\"/><div class=\"legend\"><span class=\"legend-color mw-no-invert\" style=\"background-color:#7fcdbb; color:black;\"> </span> 0.1–0.6</div></li><li style=\"margin: 0;\"><link href=\"mw-data:TemplateStyles:r981673959\" rel=\"mw-deduplicated-inline-style\"/><div class=\"legend\"><span class=\"legend-color mw-no-invert\" style=\"background-color:#c7e9b4; color:black;\"> </span> &lt;0.1</div></li><li style=\"margin: 0;\"><link href=\"mw-data:TemplateStyles:r981673959\" rel=\"mw-deduplicated-inline-style\"/><div class=\"legend\"><span class=\"legend-color mw-no-invert\" style=\"background-color:#e0e0e0; color:black;\"> </span> No deaths or no data</div></li></ul></div></div></div></td></tr><tr><th class=\"infobox-label\" scope=\"row\">Disease</th><td class=\"infobox-data\"><a href=\"/wiki/COVID-19\" title=\"COVID-19\">COVID-19</a></td></tr><tr><th class=\"infobox-label\" scope=\"row\">Virus strain</th><td class=\"infobox-data\"><a class=\"mw-redirect\" href=\"/wiki/Severe_acute_respiratory_syndrome_coronavirus_2\" title=\"Severe acute respiratory syndrome coronavirus 2\">SARS-CoV-2</a></td></tr><tr><th class=\"infobox-label\" scope=\"row\">Source</th><td class=\"infobox-data\">Probably <a href=\"/wiki/Bat\" title=\"Bat\">bats</a>, possibly via <a href=\"/wiki/Pangolin\" title=\"Pangolin\">pangolins</a><sup class=\"reference\" id=\"cite_ref-reuters223180_3-0\"><a href=\"#cite_note-reuters223180-3\">[2]</a></sup><sup class=\"reference\" id=\"cite_ref-4\"><a href=\"#cite_note-4\">[3]</a></sup></td></tr><tr><th class=\"infobox-label\" scope=\"row\">Location</th><td class=\"infobox-data\"><a href=\"/wiki/World\" title=\"World\">Worldwide</a></td></tr><tr><th class=\"infobox-label\" scope=\"row\">First outbreak</th><td class=\"infobox-data\"><a href=\"/wiki/China\" title=\"China\">China</a><sup class=\"reference\" id=\"cite_ref-auto_5-0\"><a href=\"#cite_note-auto-5\">[4]</a></sup></td></tr><tr><th class=\"infobox-label\" scope=\"row\"><a href=\"/wiki/Index_case\" title=\"Index case\">Index case</a></th><td class=\"infobox-data\"><a href=\"/wiki/Wuhan\" title=\"Wuhan\">Wuhan</a>, <a href=\"/wiki/Hubei\" title=\"Hubei\">Hubei</a>, <a href=\"/wiki/China\" title=\"China\">China</a><br/><style data-mw-deduplicate=\"TemplateStyles:r1073938472\">.mw-parser-output .geo-default,.mw-parser-output .geo-dms,.mw-parser-output .geo-dec{display:inline}.mw-parser-output .geo-nondefault,.mw-parser-output .geo-multi-punct{display:none}.mw-parser-output .longitude,.mw-parser-output .latitude{white-space:nowrap}</style><span class=\"plainlinks nourlexpansion\"><a class=\"external text\" href=\"//geohack.toolforge.org/geohack.php?pagename=COVID-19_pandemic_by_country_and_territory&amp;params=30_37_11_N_114_15_28_E_type:adm2nd_region:CN-42\"><span class=\"geo-default\"><span class=\"geo-dms\" title=\"Maps, aerial photos, and other data for this location\"><span class=\"latitude\">30°37′11″N</span> <span class=\"longitude\">114°15′28″E</span></span></span><span class=\"geo-multi-punct\">﻿ / ﻿</span><span class=\"geo-nondefault\"><span class=\"geo-dec\" title=\"Maps, aerial photos, and other data for this location\">30.61972°N 114.25778°E</span><span style=\"display:none\">﻿ / <span class=\"geo\">30.61972; 114.25778</span></span></span></a></span></td></tr><tr><th class=\"infobox-label\" scope=\"row\">Date</th><td class=\"infobox-data\">17 November 2019<span style=\"display:none\"> (<span class=\"bday dtstart published updated\">2019-11-17</span>)</span><sup class=\"reference\" id=\"cite_ref-auto_5-1\"><a href=\"#cite_note-auto-5\">[4]</a></sup> – present<br/>(2 years, 9 months and 1 week)</td></tr><tr><th class=\"infobox-label\" scope=\"row\"><span class=\"nowrap\">Confirmed cases</span></th><td class=\"infobox-data\">597,579,325<sup class=\"reference\" id=\"cite_ref-Template:COVID-19_data_6-0\"><a href=\"#cite_note-Template:COVID-19_data-6\">[5]</a></sup></td></tr><tr><th class=\"infobox-label\" scope=\"row\"><div style=\"display:inline-block; line-height:1.2em; padding:.1em 0;\">Deaths</div></th><td class=\"infobox-data\">6,457,679<sup class=\"reference\" id=\"cite_ref-Template:COVID-19_data_6-1\"><a href=\"#cite_note-Template:COVID-19_data-6\">[5]</a></sup></td></tr><tr><th class=\"infobox-label\" scope=\"row\">Vaccinations</th><td class=\"infobox-data plainlist\"><div class=\"plainlist\"><ul><li>5,338,640,851<sup class=\"reference\" id=\"cite_ref-Template:COVID-19_data_6-2\"><a href=\"#cite_note-Template:COVID-19_data-6\">[5]</a></sup> (total vaccinated)</li><li>4,907,734,699<sup class=\"reference\" id=\"cite_ref-Template:COVID-19_data_6-3\"><a href=\"#cite_note-Template:COVID-19_data-6\">[5]</a></sup> (fully vaccinated)</li><li>12,508,737,191<sup class=\"reference\" id=\"cite_ref-Template:COVID-19_data_6-4\"><a href=\"#cite_note-Template:COVID-19_data-6\">[5]</a></sup> (doses administered)</li></ul></div></td></tr></tbody></table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import HTML # IPython is the underlying Python interpreter used by this notebook\n",
    "HTML(t)                          # Render the text in t as HTML"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using Pandas to read a URL to extract tables\n",
    "\n",
    "Pandas has a built-in mechanism to read a URL and extract all the table tags into dataframes. Extremely handy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "tables = pd.read_html(CovidURL)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Summary table\n",
    "\n",
    "The first table again looks like this on the page:\n",
    " \n",
    "<img src=\"figures/covid-table-0.png\" width=\"30%\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And pandas can pull that into a data frame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>COVID-19 pandemic</th>\n",
       "      <th>COVID-19 pandemic.1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Confirmed deaths per 100,000 population,[1][no...</td>\n",
       "      <td>Confirmed deaths per 100,000 population,[1][no...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Disease</td>\n",
       "      <td>COVID-19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Virus strain</td>\n",
       "      <td>SARS-CoV-2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Source</td>\n",
       "      <td>Probably bats, possibly via pangolins[2][3]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Location</td>\n",
       "      <td>Worldwide</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>First outbreak</td>\n",
       "      <td>China[4]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Index case</td>\n",
       "      <td>Wuhan, Hubei, China.mw-parser-output .geo-defa...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Date</td>\n",
       "      <td>17 November 2019[4] – present(2 years, 9 month...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Confirmed cases</td>\n",
       "      <td>597,579,325[5]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Deaths</td>\n",
       "      <td>6,457,679[5]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Vaccinations</td>\n",
       "      <td>5,338,640,851[5] (total vaccinated)4,907,734,6...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    COVID-19 pandemic  \\\n",
       "0   Confirmed deaths per 100,000 population,[1][no...   \n",
       "1                                             Disease   \n",
       "2                                        Virus strain   \n",
       "3                                              Source   \n",
       "4                                            Location   \n",
       "5                                      First outbreak   \n",
       "6                                          Index case   \n",
       "7                                                Date   \n",
       "8                                     Confirmed cases   \n",
       "9                                              Deaths   \n",
       "10                                       Vaccinations   \n",
       "\n",
       "                                  COVID-19 pandemic.1  \n",
       "0   Confirmed deaths per 100,000 population,[1][no...  \n",
       "1                                            COVID-19  \n",
       "2                                          SARS-CoV-2  \n",
       "3         Probably bats, possibly via pangolins[2][3]  \n",
       "4                                           Worldwide  \n",
       "5                                            China[4]  \n",
       "6   Wuhan, Hubei, China.mw-parser-output .geo-defa...  \n",
       "7   17 November 2019[4] – present(2 years, 9 month...  \n",
       "8                                      597,579,325[5]  \n",
       "9                                        6,457,679[5]  \n",
       "10  5,338,640,851[5] (total vaccinated)4,907,734,6...  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tables[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Death rate table\n",
    "\n",
    "The death rate per country table is `table[12]` (found by trial-and-error) and looks like:\n",
    "\n",
    "<img src=\"figures/covid-table-1.png\" width=\"40%\">\n",
    "\n",
    "and we get a nice data frame from it too:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Country</th>\n",
       "      <th>Deaths / million</th>\n",
       "      <th>Deaths</th>\n",
       "      <th>Cases</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>World[a]</td>\n",
       "      <td>816.0</td>\n",
       "      <td>6457679.0</td>\n",
       "      <td>597579325.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Peru</td>\n",
       "      <td>6388.0</td>\n",
       "      <td>215379.0</td>\n",
       "      <td>4075910.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Bulgaria</td>\n",
       "      <td>5454.0</td>\n",
       "      <td>37562.0</td>\n",
       "      <td>1236660.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Bosnia and Herzegovina</td>\n",
       "      <td>4892.0</td>\n",
       "      <td>16004.0</td>\n",
       "      <td>394161.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Hungary</td>\n",
       "      <td>4849.0</td>\n",
       "      <td>47083.0</td>\n",
       "      <td>2021648.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>North Macedonia</td>\n",
       "      <td>4495.0</td>\n",
       "      <td>9455.0</td>\n",
       "      <td>338549.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Georgia</td>\n",
       "      <td>4492.0</td>\n",
       "      <td>16883.0</td>\n",
       "      <td>1723087.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Montenegro</td>\n",
       "      <td>4408.0</td>\n",
       "      <td>2768.0</td>\n",
       "      <td>273563.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Croatia</td>\n",
       "      <td>4087.0</td>\n",
       "      <td>16595.0</td>\n",
       "      <td>1207353.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Czech Republic</td>\n",
       "      <td>3877.0</td>\n",
       "      <td>40756.0</td>\n",
       "      <td>4029829.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Unnamed: 0                 Country  Deaths / million     Deaths        Cases\n",
       "0        NaN                World[a]             816.0  6457679.0  597579325.0\n",
       "1        NaN                    Peru            6388.0   215379.0    4075910.0\n",
       "2        NaN                Bulgaria            5454.0    37562.0    1236660.0\n",
       "3        NaN  Bosnia and Herzegovina            4892.0    16004.0     394161.0\n",
       "4        NaN                 Hungary            4849.0    47083.0    2021648.0\n",
       "5        NaN         North Macedonia            4495.0     9455.0     338549.0\n",
       "6        NaN                 Georgia            4492.0    16883.0    1723087.0\n",
       "7        NaN              Montenegro            4408.0     2768.0     273563.0\n",
       "8        NaN                 Croatia            4087.0    16595.0    1207353.0\n",
       "9        NaN          Czech Republic            3877.0    40756.0    4029829.0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_deaths = tables[12]._convert(numeric=True)\n",
    "df_deaths.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As with any data frame, we can sort values and select columns of interest:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Deaths / million</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Peru</td>\n",
       "      <td>6388.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Bulgaria</td>\n",
       "      <td>5454.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bosnia and Herzegovina</td>\n",
       "      <td>4892.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Hungary</td>\n",
       "      <td>4849.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>North Macedonia</td>\n",
       "      <td>4495.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Georgia</td>\n",
       "      <td>4492.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Montenegro</td>\n",
       "      <td>4408.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Croatia</td>\n",
       "      <td>4087.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Czech Republic</td>\n",
       "      <td>3877.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Moldova</td>\n",
       "      <td>3819.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Country  Deaths / million\n",
       "1                     Peru            6388.0\n",
       "2                 Bulgaria            5454.0\n",
       "3   Bosnia and Herzegovina            4892.0\n",
       "4                  Hungary            4849.0\n",
       "5          North Macedonia            4495.0\n",
       "6                  Georgia            4492.0\n",
       "7               Montenegro            4408.0\n",
       "8                  Croatia            4087.0\n",
       "9           Czech Republic            3877.0\n",
       "10                 Moldova            3819.0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_death_rate = df_deaths[['Country','Deaths / million']].sort_values('Deaths / million', ascending=False)\n",
    "df_death_rate.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once we have a nice data structure like a data frame, it's very easy to build a plot, which is sometimes more satisfying than what we see in some report where we got the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df_deaths.sort_values('Deaths / million', ascending=False)\n",
    "df = df.head(30)\n",
    "deaths = df['Deaths / million']\n",
    "countries = df['Country']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 396,
       "width": 506
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=(8,5))\n",
    "barcontainers = ax.bar(countries,deaths, color='#FEE08F')\n",
    "for rect in barcontainers.patches:\n",
    "    rect.set_linewidth(.3)\n",
    "    rect.set_edgecolor('grey')\n",
    "\n",
    "ax.set_xticks(countries)\n",
    "ax.set_xticklabels(countries, rotation=45, fontsize=8, horizontalalignment=\"right\")\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['left'].set_linewidth(.5)\n",
    "ax.spines['bottom'].set_linewidth(.5)\n",
    "\n",
    "ax.set_title(\"Total deaths per country\")\n",
    "ax.set_ylabel(\"Deaths\")\n",
    "ax.set_xlabel(\"Country\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ok, well hopefully that was fun and gives you some idea of what we can do with just a few simple skills."
   ]
  }
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