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": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install -q -U requests              # we need these libraries\n",
    "!pip install -q -U beautifulsoup4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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\":!1,\"wgSeparatorTransformTable\":[\"\",\"\"],\"wgDigitTransformTable\":[\"\",\"\"],\"wgDefaultDateFormat\":\"dmy\",\"wgMonthNames\":[\"\",\"January\",\"February\",\"March\",\"April\",\"May\",\"June\",\"July\",\"August\",\"September\",\"October\",\"November\",\"December\"],\"wgRequestId\":\"f18c36ce-c181-48d5-b5e1-053486e1fd59\",\"wgCSPNonce\":!1,\"wgCanonicalNamespace\":\"\",\"wgCanonicalSpecialPageName\":!1,\"wgNamespaceNumber\":0,\"wgPageName\":\"COVID-19_pandemic_by_country_and_territory\",\"wgTitle\":\"COVID-19 pandemic by country and territory\",\"wgCurRevisionId\":1039966951,\"wgRevisionId\":1039966951,\"wgArticleId\":62938755,\"wgIsArticle\":!0,\"wgIsRedirect\":!1,\"wgAction\":\"view\",\"wgUserName\":null,\"wgUserGroups\":[\"*\"],\"wgCategories\":[\"Articles containing potentially dated statements from December 2020\",\"All artic\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": 17,
   "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": 18,
   "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 Data and maps, showing cases and deaths   COVID-19 pandemicConfirmed deaths per 100,000 population, as of 15 August 2021   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, possibly via pangolins[1][2]Locat\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": 19,
   "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": 20,
   "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": 21,
   "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,<br/> as of 15 August 2021</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\" 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\" 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\" 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\" 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\" 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\" 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\" 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\" 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 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_1-0\"><a href=\"#cite_note-reuters223180-1\">[1]</a></sup><sup class=\"reference\" id=\"cite_ref-2\"><a href=\"#cite_note-2\">[2]</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_3-0\"><a href=\"#cite_note-auto-3\">[3]</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:r994658806\">.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\">1 December 2019<span style=\"display:none\"> (<span class=\"bday dtstart published updated\">2019-12-01</span>)</span><sup class=\"reference\" id=\"cite_ref-auto_3-1\"><a href=\"#cite_note-auto-3\">[3]</a></sup> – present<br/>(1 year, 8 months, 3 weeks and 2 days)</td></tr><tr><th class=\"infobox-label\" scope=\"row\">Confirmed cases</th><td class=\"infobox-data\">212,610,603<sup class=\"reference\" id=\"cite_ref-JHU_ticker_4-0\"><a href=\"#cite_note-JHU_ticker-4\">[4]</a></sup></td></tr><tr><th class=\"infobox-label\" scope=\"row\"><div style=\"display:inline-block; padding:0.1em 0;line-height:1.2em;\">Deaths</div></th><td class=\"infobox-data\">4,442,545<sup class=\"reference\" id=\"cite_ref-JHU_ticker_4-1\"><a href=\"#cite_note-JHU_ticker-4\">[4]</a></sup></td></tr></tbody></table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 21,
     "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": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "tables = pd.read_html(CovidURL)"
   ]
  },
  {
   "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": 26,
   "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, as of...</td>\n",
       "      <td>Confirmed deaths per 100,000 population, as of...</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[1][2]</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[3]</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>1 December 2019[3] – present(1 year, 8 months,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Confirmed cases</td>\n",
       "      <td>212,610,603[4]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Deaths</td>\n",
       "      <td>4,442,545[4]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   COVID-19 pandemic  \\\n",
       "0  Confirmed deaths per 100,000 population, as of...   \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",
       "\n",
       "                                 COVID-19 pandemic.1  \n",
       "0  Confirmed deaths per 100,000 population, as of...  \n",
       "1                                           COVID-19  \n",
       "2                                         SARS-CoV-2  \n",
       "3        Probably bats, possibly via pangolins[1][2]  \n",
       "4                                          Worldwide  \n",
       "5                                           China[3]  \n",
       "6  Wuhan, Hubei, China.mw-parser-output .geo-defa...  \n",
       "7  1 December 2019[3] – present(1 year, 8 months,...  \n",
       "8                                     212,610,603[4]  \n",
       "9                                       4,442,545[4]  "
      ]
     },
     "execution_count": 26,
     "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[9]` (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": 10,
   "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>Confirmed cases</th>\n",
       "      <th>Deaths</th>\n",
       "      <th>Case fatality rate</th>\n",
       "      <th>Deaths per 100,000 population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Peru</td>\n",
       "      <td>2142153</td>\n",
       "      <td>197879</td>\n",
       "      <td>9.2%</td>\n",
       "      <td>608.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Hungary</td>\n",
       "      <td>811121</td>\n",
       "      <td>30052</td>\n",
       "      <td>3.7%</td>\n",
       "      <td>307.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Bosnia and Herzegovina</td>\n",
       "      <td>209909</td>\n",
       "      <td>9740</td>\n",
       "      <td>4.6%</td>\n",
       "      <td>295.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Czech Republic</td>\n",
       "      <td>1677619</td>\n",
       "      <td>30385</td>\n",
       "      <td>1.8%</td>\n",
       "      <td>284.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>North Macedonia</td>\n",
       "      <td>169202</td>\n",
       "      <td>5695</td>\n",
       "      <td>3.4%</td>\n",
       "      <td>273.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>20583994</td>\n",
       "      <td>574848</td>\n",
       "      <td>2.8%</td>\n",
       "      <td>272.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Montenegro</td>\n",
       "      <td>110377</td>\n",
       "      <td>1680</td>\n",
       "      <td>1.5%</td>\n",
       "      <td>270.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>San Marino</td>\n",
       "      <td>5261</td>\n",
       "      <td>90</td>\n",
       "      <td>1.7%</td>\n",
       "      <td>265.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Bulgaria</td>\n",
       "      <td>443186</td>\n",
       "      <td>18532</td>\n",
       "      <td>4.2%</td>\n",
       "      <td>265.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Colombia</td>\n",
       "      <td>4892235</td>\n",
       "      <td>124315</td>\n",
       "      <td>2.5%</td>\n",
       "      <td>246.95</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Country  Confirmed cases  Deaths Case fatality rate  \\\n",
       "0                    Peru          2142153  197879               9.2%   \n",
       "1                 Hungary           811121   30052               3.7%   \n",
       "2  Bosnia and Herzegovina           209909    9740               4.6%   \n",
       "3          Czech Republic          1677619   30385               1.8%   \n",
       "4         North Macedonia           169202    5695               3.4%   \n",
       "5                  Brazil         20583994  574848               2.8%   \n",
       "6              Montenegro           110377    1680               1.5%   \n",
       "7              San Marino             5261      90               1.7%   \n",
       "8                Bulgaria           443186   18532               4.2%   \n",
       "9                Colombia          4892235  124315               2.5%   \n",
       "\n",
       "   Deaths per 100,000 population  \n",
       "0                         608.66  \n",
       "1                         307.60  \n",
       "2                         295.06  \n",
       "3                         284.78  \n",
       "4                         273.34  \n",
       "5                         272.38  \n",
       "6                         270.04  \n",
       "7                         265.80  \n",
       "8                         265.66  \n",
       "9                         246.95  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_deaths = tables[9]\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": 11,
   "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 per 100,000 population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Peru</td>\n",
       "      <td>608.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Hungary</td>\n",
       "      <td>307.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Bosnia and Herzegovina</td>\n",
       "      <td>295.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Czech Republic</td>\n",
       "      <td>284.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>North Macedonia</td>\n",
       "      <td>273.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>272.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Montenegro</td>\n",
       "      <td>270.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>San Marino</td>\n",
       "      <td>265.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Bulgaria</td>\n",
       "      <td>265.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Colombia</td>\n",
       "      <td>246.95</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Country  Deaths per 100,000 population\n",
       "0                    Peru                         608.66\n",
       "1                 Hungary                         307.60\n",
       "2  Bosnia and Herzegovina                         295.06\n",
       "3          Czech Republic                         284.78\n",
       "4         North Macedonia                         273.34\n",
       "5                  Brazil                         272.38\n",
       "6              Montenegro                         270.04\n",
       "7              San Marino                         265.80\n",
       "8                Bulgaria                         265.66\n",
       "9                Colombia                         246.95"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_death_rate = df_deaths[['Country','Deaths per 100,000 population']].sort_values('Deaths per 100,000 population', 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": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df_deaths.sort_values('Deaths', ascending=False)\n",
    "df = df.head(30)\n",
    "countries = df['Country']\n",
    "deaths = df['Deaths']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 264,
       "width": 519
      },
      "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=(8,3))\n",
    "barcontainers = ax.bar(countries,deaths, color='#FEE08F')\n",
    "for rect in barcontainers.patches:\n",
    "    rect.set_linewidth(.5)\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."
   ]
  }
 ],
 "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}