Visualisation Part 2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualisation Part 2: Story with data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this activity we will be working on the visualisation of data to understand our world. We will focus on the use of vaccines, especially on claims that they do not work or are not necessary (https://www.medicalnewstoday.com/articles/325371#myths-and-facts)\n",
    "\n",
    "\n",
    "### How does this notebook work?\n",
    "\n",
    "Run the cells with code written on them. To do this, you can select them and press Shift + Enter or press the \"Play\" button on the left side of the cell (if you do not see this, hover with the mouse on the cell and it should appear). Remember that all cells need to be run in order, even those in which you did not need to write any code. If you think you might have skipped one, on the left side of the cell you will find the line number, which corresponds to the order in which you have ran the cells. Keep in mind that if you run a cell and it gets numbered as 4, if you run the same cell immediately after it will be renumbered to 5.\n",
    "\n",
    "Looking at the code you will notice some parts are incomplete and have '\\_\\_' written instead. This means that you need to complete that part of the code. In some other parts, you will need to write your own code. This will be specified.\n",
    "\n",
    "You will also find hyperlinks to documentation on different functions that we will use. It is recommended to look at them to familiarise yourself with what you are doing and how they work.\n",
    "\n",
    "There are also questions to be completed in text cells. Click twice on them to start editing them or select them and press Enter, and press Shift+Enter when you are finished to go back to reading mode. The questions can be answered in one or two sentences in general."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy \n",
    "import os\n",
    "import matplotlib.pyplot \n",
    "import matplotlib.patches \n",
    "import math\n",
    "import seaborn \n",
    "import pandas \n",
    "from collections import Counter\n",
    "#Make sure the helper_functions.py file is in the same folder as this notebook\n",
    "from helper_functions import get_vgs_proportion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here you have links to the libraries we have imported:\n",
    "\n",
    "[OS library](https://docs.python.org/3/library/os.html): Library with different useful functions to interact with the Operating System. We will be using this for loading the datasets.\n",
    "\n",
    "[Pandas](https://pandas.pydata.org/): Pandas is a tool for data analysis and manipulation.\n",
    "\n",
    "[Seaborn](https://seaborn.pydata.org/): Seaborn is a library specialised in statistical data visualisation.\n",
    "\n",
    "[Numpy](https://numpy.org/): Numpy is the Python library for mathematics. We will use it for performing operations on our data.\n",
    "\n",
    "[Matplotlib](https://matplotlib.org/): Matplotlib is another library specilised in visualisation for Python.\n",
    "\n",
    "[Math module](https://docs.python.org/3/library/math.html): This module provides mathematical functions defined by the C standard."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You have heard the following claim: \"Vaccines do not work. Flu comes every year even though there are vaccines.\" (You can check [here](https://www.nhs.uk/conditions/vaccinations/how-flu-vaccine-works/) why this is the case). You have also read that polio has almost been eradicated thanks to vaccines, and that this is actually possible (you can check this [here](https://www.who.int/features/factfiles/polio/en/)). These two statements are contradictory, so you decide to search for data on polio and vaccines to determine if they are useful or not by yourself.\n",
    "\n",
    "We will be then using two datasets. The first contains data on polio cases in all the countries in the world from 1980 to 2019, and the second contains data on vaccination worldwide in the same range of years. These are extracts from data you can find [here](https://www.who.int/immunization/monitoring_surveillance/data/en/)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When loading the datasets, make sure that they are in a folder called \"datasets\" in the same folder as this notebook. If this is not the case, you can [change the path](https://docs.python.org/3/library/os.path.html) to load the data from where it is stored."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Get the path to the files we will be using.\n",
    "path_polio = os.path.join(os.getcwd(), 'datasets', 'polio.csv')\n",
    "path_vaccines = os.path.join(os.getcwd(), 'datasets', 'vaccines_who.csv')\n",
    "#Load the data into the countries_info variable. This results in a DataFrame object.\n",
    "polio = pandas.read_csv(path_polio, delimiter = ';')\n",
    "vaccines = pandas.read_csv(path_vaccines, delimiter = ';')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will now look at the data we have loaded. We will check the columns they have\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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       "      <td>245.233129</td>\n",
       "      <td>320.670807</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>21.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>37.500000</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>57.500000</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>84.000000</td>\n",
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       "    <tr>\n",
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       "      <td>56.000000</td>\n",
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       "      <td>38090.000000</td>\n",
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      "text/plain": [
       "       who_region iso_code      country disease        2019        2018  \\\n",
       "count         194      194          194     194  168.000000  157.000000   \n",
       "unique          6      194          194       1         NaN         NaN   \n",
       "top           EUR      DMA  Timor-Leste   polio         NaN         NaN   \n",
       "freq           53        1            1     194         NaN         NaN   \n",
       "mean          NaN      NaN          NaN     NaN    2.190476    0.662420   \n",
       "std           NaN      NaN          NaN     NaN   12.500037    3.929758   \n",
       "min           NaN      NaN          NaN     NaN    0.000000    0.000000   \n",
       "25%           NaN      NaN          NaN     NaN    0.000000    0.000000   \n",
       "50%           NaN      NaN          NaN     NaN    0.000000    0.000000   \n",
       "75%           NaN      NaN          NaN     NaN    0.000000    0.000000   \n",
       "max           NaN      NaN          NaN     NaN  130.000000   34.000000   \n",
       "\n",
       "              2017        2016        2015        2014  ...          1989  \\\n",
       "count   168.000000  168.000000  167.000000  168.000000  ...    166.000000   \n",
       "unique         NaN         NaN         NaN         NaN  ...           NaN   \n",
       "top            NaN         NaN         NaN         NaN  ...           NaN   \n",
       "freq           NaN         NaN         NaN         NaN  ...           NaN   \n",
       "mean      0.571429    0.250000    0.634731    2.470238  ...    157.253012   \n",
       "std       5.946439    1.947669    4.715214   25.515740  ...   1143.757448   \n",
       "min       0.000000    0.000000    0.000000    0.000000  ...      0.000000   \n",
       "25%       0.000000    0.000000    0.000000    0.000000  ...      0.000000   \n",
       "50%       0.000000    0.000000    0.000000    0.000000  ...      0.000000   \n",
       "75%       0.000000    0.000000    0.000000    0.000000  ...     14.000000   \n",
       "max      74.000000   21.000000   56.000000  328.000000  ...  13915.000000   \n",
       "\n",
       "                1988          1987          1986          1985          1984  \\\n",
       "count     165.000000    163.000000    163.000000    163.000000    166.000000   \n",
       "unique           NaN           NaN           NaN           NaN           NaN   \n",
       "top              NaN           NaN           NaN           NaN           NaN   \n",
       "freq             NaN           NaN           NaN           NaN           NaN   \n",
       "mean      209.800000    243.453988    201.509202    236.092025    211.349398   \n",
       "std      1860.794668   2218.533133   1590.520830   1787.146103   1799.496525   \n",
       "min         0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "25%         0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "50%         1.000000      1.000000      2.000000      2.000000      3.500000   \n",
       "75%        21.000000     31.000000     37.500000     58.000000     57.500000   \n",
       "max     23800.000000  28264.000000  20150.000000  22570.000000  23107.000000   \n",
       "\n",
       "                1983          1982          1981          1980  \n",
       "count     163.000000    161.000000    161.000000    161.000000  \n",
       "unique           NaN           NaN           NaN           NaN  \n",
       "top              NaN           NaN           NaN           NaN  \n",
       "freq             NaN           NaN           NaN           NaN  \n",
       "mean      245.233129    320.670807    408.304348    326.894410  \n",
       "std      1952.820163   2175.493010   3105.037542   1658.420987  \n",
       "min         0.000000      0.000000      0.000000      0.000000  \n",
       "25%         0.000000      0.000000      0.000000      0.000000  \n",
       "50%         5.000000      8.000000      9.000000     11.000000  \n",
       "75%        63.000000     84.000000     98.000000    129.000000  \n",
       "max     24663.000000  26297.000000  38090.000000  18975.000000  \n",
       "\n",
       "[11 rows x 44 columns]"
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     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "polio.describe(include='all')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Through a quick scan of this table, we can already see a bit of the evolution of polio. The 'max' row shows us the maximum value in each of the columns. If we look at the value in 2019 we see it is 130 cases, which is much smaller than the maximum value of 1980, which is almost 19000. This already shows that polio has decreased, but we are looking for a relationship with the vaccine. It also does not show an evolution, as the maximum number can be from any country.\n",
    "\n",
    "We can see in the 'disease' column that the most common is polio and at the 'unique' row, we can see that there is only one disease. This means that our data covers just polio, and we will not need to specifically extract polio data. This could be different, as we will see in the vaccines data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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       "      <th>who_region</th>\n",
       "      <th>iso_code</th>\n",
       "      <th>country</th>\n",
       "      <th>continent</th>\n",
       "      <th>vaccine</th>\n",
       "      <th>year</th>\n",
       "      <th>percent_coverage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>57183</td>\n",
       "      <td>57183</td>\n",
       "      <td>57183</td>\n",
       "      <td>57183</td>\n",
       "      <td>57183</td>\n",
       "      <td>57183.000000</td>\n",
       "      <td>57158.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>6</td>\n",
       "      <td>194</td>\n",
       "      <td>194</td>\n",
       "      <td>6</td>\n",
       "      <td>50</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>AFR</td>\n",
       "      <td>PAN</td>\n",
       "      <td>Panama</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Pol3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>13868</td>\n",
       "      <td>428</td>\n",
       "      <td>428</td>\n",
       "      <td>15997</td>\n",
       "      <td>7023</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2005.647500</td>\n",
       "      <td>80.810630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.796585</td>\n",
       "      <td>22.773944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1966.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1999.000000</td>\n",
       "      <td>74.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2008.000000</td>\n",
       "      <td>90.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015.000000</td>\n",
       "      <td>97.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019.000000</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       who_region iso_code country continent vaccine          year  \\\n",
       "count       57183    57183   57183     57183   57183  57183.000000   \n",
       "unique          6      194     194         6      50           NaN   \n",
       "top           AFR      PAN  Panama    Africa    Pol3           NaN   \n",
       "freq        13868      428     428     15997    7023           NaN   \n",
       "mean          NaN      NaN     NaN       NaN     NaN   2005.647500   \n",
       "std           NaN      NaN     NaN       NaN     NaN     10.796585   \n",
       "min           NaN      NaN     NaN       NaN     NaN   1966.000000   \n",
       "25%           NaN      NaN     NaN       NaN     NaN   1999.000000   \n",
       "50%           NaN      NaN     NaN       NaN     NaN   2008.000000   \n",
       "75%           NaN      NaN     NaN       NaN     NaN   2015.000000   \n",
       "max           NaN      NaN     NaN       NaN     NaN   2019.000000   \n",
       "\n",
       "        percent_coverage  \n",
       "count       57158.000000  \n",
       "unique               NaN  \n",
       "top                  NaN  \n",
       "freq                 NaN  \n",
       "mean           80.810630  \n",
       "std            22.773944  \n",
       "min             1.000000  \n",
       "25%            74.000000  \n",
       "50%            90.000000  \n",
       "75%            97.000000  \n",
       "max           100.000000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vaccines.describe(include='all')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we can see that the most common vaccine is Pol3, which corresponds to the polio vaccine. But we can also see that there are 50 different vaccines, which means that we will need to extract the data that has Pol3 as a vaccine. We will use the [.loc[]](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.loc.html) function for this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>7023</td>\n",
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       "      <td>6</td>\n",
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       "      <th>freq</th>\n",
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       "      <td>2000.323508</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>11.476928</td>\n",
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       "      <td>1991.000000</td>\n",
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       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2001.000000</td>\n",
       "      <td>90.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2010.000000</td>\n",
       "      <td>96.000000</td>\n",
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       "      <th>max</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019.000000</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       who_region iso_code  country continent vaccine         year  \\\n",
       "count        7023     7023     7023      7023    7023  7023.000000   \n",
       "unique          6      194      194         6       1          NaN   \n",
       "top           EUR      HRV  Croatia    Africa    Pol3          NaN   \n",
       "freq         1785       54       54      1887    7023          NaN   \n",
       "mean          NaN      NaN      NaN       NaN     NaN  2000.323508   \n",
       "std           NaN      NaN      NaN       NaN     NaN    11.476928   \n",
       "min           NaN      NaN      NaN       NaN     NaN  1966.000000   \n",
       "25%           NaN      NaN      NaN       NaN     NaN  1991.000000   \n",
       "50%           NaN      NaN      NaN       NaN     NaN  2001.000000   \n",
       "75%           NaN      NaN      NaN       NaN     NaN  2010.000000   \n",
       "max           NaN      NaN      NaN       NaN     NaN  2019.000000   \n",
       "\n",
       "        percent_coverage  \n",
       "count        7023.000000  \n",
       "unique               NaN  \n",
       "top                  NaN  \n",
       "freq                 NaN  \n",
       "mean           80.772889  \n",
       "std            22.291851  \n",
       "min             1.000000  \n",
       "25%            74.000000  \n",
       "50%            90.000000  \n",
       "75%            96.000000  \n",
       "max           100.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vacc_polio = vaccines.loc[vaccines['vaccine']=='Pol3']\n",
    "vacc_polio.describe(include='all')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have all the information, let us first look at the evolution of polio and the vaccine in one country.We will now extract the data from the United Kingdom with the .loc\\[\\] function and visualise it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>EUR</td>\n",
       "      <td>GBR</td>\n",
       "      <td>United Kingdom of Great Britain and Northern I...</td>\n",
       "      <td>polio</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "</table>\n",
       "<p>1 rows × 44 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    who_region iso_code                                            country  \\\n",
       "183        EUR      GBR  United Kingdom of Great Britain and Northern I...   \n",
       "\n",
       "    disease  2019  2018  2017  2016  2015  2014  ...  1989  1988  1987  1986  \\\n",
       "183   polio   0.0   0.0   0.0   0.0   0.0   0.0  ...   0.0   1.0   0.0   0.0   \n",
       "\n",
       "     1985  1984  1983  1982  1981  1980  \n",
       "183   2.0   6.0   5.0   3.0   2.0   3.0  \n",
       "\n",
       "[1 rows x 44 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "polio_uk = polio.loc[polio['country']=='United Kingdom of Great Britain and Northern Ireland']\n",
    "polio_uk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>Pol3</td>\n",
       "      <td>1989</td>\n",
       "      <td>89.0</td>\n",
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       "      <td>EUR</td>\n",
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       "      <td>1991</td>\n",
       "      <td>92.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53994</th>\n",
       "      <td>EUR</td>\n",
       "      <td>GBR</td>\n",
       "      <td>United Kingdom of Great Britain and Northern I...</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Pol3</td>\n",
       "      <td>1992</td>\n",
       "      <td>94.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53995</th>\n",
       "      <td>EUR</td>\n",
       "      <td>GBR</td>\n",
       "      <td>United Kingdom of Great Britain and Northern I...</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Pol3</td>\n",
       "      <td>1993</td>\n",
       "      <td>94.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53996</th>\n",
       "      <td>EUR</td>\n",
       "      <td>GBR</td>\n",
       "      <td>United Kingdom of Great Britain and Northern I...</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Pol3</td>\n",
       "      <td>1994</td>\n",
       "      <td>94.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53997</th>\n",
       "      <td>EUR</td>\n",
       "      <td>GBR</td>\n",
       "      <td>United Kingdom of Great Britain and Northern I...</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Pol3</td>\n",
       "      <td>1995</td>\n",
       "      <td>94.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53998</th>\n",
       "      <td>EUR</td>\n",
       "      <td>GBR</td>\n",
       "      <td>United Kingdom of Great Britain and Northern I...</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Pol3</td>\n",
       "      <td>1996</td>\n",
       "      <td>96.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53999</th>\n",
       "      <td>EUR</td>\n",
       "      <td>GBR</td>\n",
       "      <td>United Kingdom of Great Britain and Northern I...</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Pol3</td>\n",
       "      <td>1997</td>\n",
       "      <td>92.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54000</th>\n",
       "      <td>EUR</td>\n",
       "      <td>GBR</td>\n",
       "      <td>United Kingdom of Great Britain and Northern I...</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Pol3</td>\n",
       "      <td>1998</td>\n",
       "      <td>91.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54001</th>\n",
       "      <td>EUR</td>\n",
       "      <td>GBR</td>\n",
       "      <td>United Kingdom of Great Britain and Northern I...</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Pol3</td>\n",
       "      <td>1999</td>\n",
       "      <td>92.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54002</th>\n",
       "      <td>EUR</td>\n",
       "      <td>GBR</td>\n",
       "      <td>United Kingdom of Great Britain and Northern I...</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Pol3</td>\n",
       "      <td>2000</td>\n",
       "      <td>91.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54003</th>\n",
       "      <td>EUR</td>\n",
       "      <td>GBR</td>\n",
       "      <td>United Kingdom of Great Britain and Northern I...</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Pol3</td>\n",
       "      <td>2001</td>\n",
       "      <td>91.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54004</th>\n",
       "      <td>EUR</td>\n",
       "      <td>GBR</td>\n",
       "      <td>United Kingdom of Great Britain and Northern I...</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Pol3</td>\n",
       "      <td>2002</td>\n",
       "      <td>91.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54005</th>\n",
       "      <td>EUR</td>\n",
       "      <td>GBR</td>\n",
       "      <td>United Kingdom of Great Britain and Northern I...</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Pol3</td>\n",
       "      <td>2003</td>\n",
       "      <td>91.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54006</th>\n",
       "      <td>EUR</td>\n",
       "      <td>GBR</td>\n",
       "      <td>United Kingdom of Great Britain and Northern I...</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Pol3</td>\n",
       "      <td>2004</td>\n",
       "      <td>92.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54007</th>\n",
       "      <td>EUR</td>\n",
       "      <td>GBR</td>\n",
       "      <td>United Kingdom of Great Britain and Northern I...</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Pol3</td>\n",
       "      <td>2005</td>\n",
       "      <td>91.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54008</th>\n",
       "      <td>EUR</td>\n",
       "      <td>GBR</td>\n",
       "      <td>United Kingdom of Great Britain and Northern I...</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Pol3</td>\n",
       "      <td>2006</td>\n",
       "      <td>92.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54009</th>\n",
       "      <td>EUR</td>\n",
       "      <td>GBR</td>\n",
       "      <td>United Kingdom of Great Britain and Northern I...</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Pol3</td>\n",
       "      <td>2007</td>\n",
       "      <td>92.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54010</th>\n",
       "      <td>EUR</td>\n",
       "      <td>GBR</td>\n",
       "      <td>United Kingdom of Great Britain and Northern I...</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Pol3</td>\n",
       "      <td>2008</td>\n",
       "      <td>92.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54011</th>\n",
       "      <td>EUR</td>\n",
       "      <td>GBR</td>\n",
       "      <td>United Kingdom of Great Britain and Northern I...</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Pol3</td>\n",
       "      <td>2009</td>\n",
       "      <td>93.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54012</th>\n",
       "      <td>EUR</td>\n",
       "      <td>GBR</td>\n",
       "      <td>United Kingdom of Great Britain and Northern I...</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Pol3</td>\n",
       "      <td>2010</td>\n",
       "      <td>94.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54013</th>\n",
       "      <td>EUR</td>\n",
       "      <td>GBR</td>\n",
       "      <td>United Kingdom of Great Britain and Northern I...</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Pol3</td>\n",
       "      <td>2011</td>\n",
       "      <td>95.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54014</th>\n",
       "      <td>EUR</td>\n",
       "      <td>GBR</td>\n",
       "      <td>United Kingdom of Great Britain and Northern I...</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Pol3</td>\n",
       "      <td>2012</td>\n",
       "      <td>95.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54015</th>\n",
       "      <td>EUR</td>\n",
       "      <td>GBR</td>\n",
       "      <td>United Kingdom of Great Britain and Northern I...</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Pol3</td>\n",
       "      <td>2013</td>\n",
       "      <td>95.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54016</th>\n",
       "      <td>EUR</td>\n",
       "      <td>GBR</td>\n",
       "      <td>United Kingdom of Great Britain and Northern I...</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Pol3</td>\n",
       "      <td>2014</td>\n",
       "      <td>95.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54017</th>\n",
       "      <td>EUR</td>\n",
       "      <td>GBR</td>\n",
       "      <td>United Kingdom of Great Britain and Northern I...</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Pol3</td>\n",
       "      <td>2015</td>\n",
       "      <td>95.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54018</th>\n",
       "      <td>EUR</td>\n",
       "      <td>GBR</td>\n",
       "      <td>United Kingdom of Great Britain and Northern I...</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Pol3</td>\n",
       "      <td>2016</td>\n",
       "      <td>94.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54019</th>\n",
       "      <td>EUR</td>\n",
       "      <td>GBR</td>\n",
       "      <td>United Kingdom of Great Britain and Northern I...</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Pol3</td>\n",
       "      <td>2017</td>\n",
       "      <td>94.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54020</th>\n",
       "      <td>EUR</td>\n",
       "      <td>GBR</td>\n",
       "      <td>United Kingdom of Great Britain and Northern I...</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Pol3</td>\n",
       "      <td>2018</td>\n",
       "      <td>94.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54021</th>\n",
       "      <td>EUR</td>\n",
       "      <td>GBR</td>\n",
       "      <td>United Kingdom of Great Britain and Northern I...</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Pol3</td>\n",
       "      <td>2019</td>\n",
       "      <td>93.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      who_region iso_code                                            country  \\\n",
       "53984        EUR      GBR  United Kingdom of Great Britain and Northern I...   \n",
       "53985        EUR      GBR  United Kingdom of Great Britain and Northern I...   \n",
       "53986        EUR      GBR  United Kingdom of Great Britain and Northern I...   \n",
       "53987        EUR      GBR  United Kingdom of Great Britain and Northern I...   \n",
       "53988        EUR      GBR  United Kingdom of Great Britain and Northern I...   \n",
       "53989        EUR      GBR  United Kingdom of Great Britain and Northern I...   \n",
       "53990        EUR      GBR  United Kingdom of Great Britain and Northern I...   \n",
       "53991        EUR      GBR  United Kingdom of Great Britain and Northern I...   \n",
       "53992        EUR      GBR  United Kingdom of Great Britain and Northern I...   \n",
       "53993        EUR      GBR  United Kingdom of Great Britain and Northern I...   \n",
       "53994        EUR      GBR  United Kingdom of Great Britain and Northern I...   \n",
       "53995        EUR      GBR  United Kingdom of Great Britain and Northern I...   \n",
       "53996        EUR      GBR  United Kingdom of Great Britain and Northern I...   \n",
       "53997        EUR      GBR  United Kingdom of Great Britain and Northern I...   \n",
       "53998        EUR      GBR  United Kingdom of Great Britain and Northern I...   \n",
       "53999        EUR      GBR  United Kingdom of Great Britain and Northern I...   \n",
       "54000        EUR      GBR  United Kingdom of Great Britain and Northern I...   \n",
       "54001        EUR      GBR  United Kingdom of Great Britain and Northern I...   \n",
       "54002        EUR      GBR  United Kingdom of Great Britain and Northern I...   \n",
       "54003        EUR      GBR  United Kingdom of Great Britain and Northern I...   \n",
       "54004        EUR      GBR  United Kingdom of Great Britain and Northern I...   \n",
       "54005        EUR      GBR  United Kingdom of Great Britain and Northern I...   \n",
       "54006        EUR      GBR  United Kingdom of Great Britain and Northern I...   \n",
       "54007        EUR      GBR  United Kingdom of Great Britain and Northern I...   \n",
       "54008        EUR      GBR  United Kingdom of Great Britain and Northern I...   \n",
       "54009        EUR      GBR  United Kingdom of Great Britain and Northern I...   \n",
       "54010        EUR      GBR  United Kingdom of Great Britain and Northern I...   \n",
       "54011        EUR      GBR  United Kingdom of Great Britain and Northern I...   \n",
       "54012        EUR      GBR  United Kingdom of Great Britain and Northern I...   \n",
       "54013        EUR      GBR  United Kingdom of Great Britain and Northern I...   \n",
       "54014        EUR      GBR  United Kingdom of Great Britain and Northern I...   \n",
       "54015        EUR      GBR  United Kingdom of Great Britain and Northern I...   \n",
       "54016        EUR      GBR  United Kingdom of Great Britain and Northern I...   \n",
       "54017        EUR      GBR  United Kingdom of Great Britain and Northern I...   \n",
       "54018        EUR      GBR  United Kingdom of Great Britain and Northern I...   \n",
       "54019        EUR      GBR  United Kingdom of Great Britain and Northern I...   \n",
       "54020        EUR      GBR  United Kingdom of Great Britain and Northern I...   \n",
       "54021        EUR      GBR  United Kingdom of Great Britain and Northern I...   \n",
       "\n",
       "      continent vaccine  year  percent_coverage  \n",
       "53984    Europe    Pol3  1981              71.0  \n",
       "53985    Europe    Pol3  1982              73.0  \n",
       "53986    Europe    Pol3  1983              83.0  \n",
       "53987    Europe    Pol3  1984              78.0  \n",
       "53988    Europe    Pol3  1986              85.0  \n",
       "53989    Europe    Pol3  1987              87.0  \n",
       "53990    Europe    Pol3  1988              87.0  \n",
       "53991    Europe    Pol3  1989              89.0  \n",
       "53992    Europe    Pol3  1990              90.0  \n",
       "53993    Europe    Pol3  1991              92.0  \n",
       "53994    Europe    Pol3  1992              94.0  \n",
       "53995    Europe    Pol3  1993              94.0  \n",
       "53996    Europe    Pol3  1994              94.0  \n",
       "53997    Europe    Pol3  1995              94.0  \n",
       "53998    Europe    Pol3  1996              96.0  \n",
       "53999    Europe    Pol3  1997              92.0  \n",
       "54000    Europe    Pol3  1998              91.0  \n",
       "54001    Europe    Pol3  1999              92.0  \n",
       "54002    Europe    Pol3  2000              91.0  \n",
       "54003    Europe    Pol3  2001              91.0  \n",
       "54004    Europe    Pol3  2002              91.0  \n",
       "54005    Europe    Pol3  2003              91.0  \n",
       "54006    Europe    Pol3  2004              92.0  \n",
       "54007    Europe    Pol3  2005              91.0  \n",
       "54008    Europe    Pol3  2006              92.0  \n",
       "54009    Europe    Pol3  2007              92.0  \n",
       "54010    Europe    Pol3  2008              92.0  \n",
       "54011    Europe    Pol3  2009              93.0  \n",
       "54012    Europe    Pol3  2010              94.0  \n",
       "54013    Europe    Pol3  2011              95.0  \n",
       "54014    Europe    Pol3  2012              95.0  \n",
       "54015    Europe    Pol3  2013              95.0  \n",
       "54016    Europe    Pol3  2014              95.0  \n",
       "54017    Europe    Pol3  2015              95.0  \n",
       "54018    Europe    Pol3  2016              94.0  \n",
       "54019    Europe    Pol3  2017              94.0  \n",
       "54020    Europe    Pol3  2018              94.0  \n",
       "54021    Europe    Pol3  2019              93.0  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vaccines_uk = vacc_polio.loc[vacc_polio['country']=='United Kingdom of Great Britain and Northern Ireland (the)']\n",
    "vaccines_uk"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have extracted the data for the United Kingdom, we con proceed to plot it. We will plot the number of polio cases and the percentage of vaccination. ***If vaccines worked, should the number of cases increase or dicrease over time as the vaccination goes up?***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2825cca2f60>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#As the years start from 2019 and go backwards, we need to reverse the values\n",
    "years = list(reversed(polio_uk.columns[4:-2]))\n",
    "cases = list(reversed(polio_uk.to_numpy()[0][4:-2]))\n",
    "\n",
    "#Just changing the size of the plot so it is easier to read\n",
    "matplotlib.pyplot.rcParams[\"figure.figsize\"] = (20,5)\n",
    "\n",
    "#Plot the data. The years are on the X axis, the percentages on the Y one\n",
    "matplotlib.pyplot.plot(years, cases, label = 'Cases')\n",
    "matplotlib.pyplot.plot(years, vaccines_uk['percent_coverage'], label='Percentage coverage')\n",
    "matplotlib.pyplot.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***Recall your answer to the previous question. Did the polio cases behave as you expected?***\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is difficult in this plot to know if the vaccine did much for the United Kingdom in regards to stop polio. This is because vaccine coverage was quite high from the beginnig of our data, and cases were already low. We will now take a different country with more initial cases and lower coverage: Bangladesh. We will be doing the same process as we did with the United Kingdom."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2825cd490b8>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "polio_bgl = polio.loc[polio['country']=='Bangladesh']\n",
    "vaccines_bgl = vacc_polio.loc[vacc_polio['country']=='Bangladesh']\n",
    "\n",
    "\n",
    "#As the years start from 2019 and go backwards, we need to reverse the values\n",
    "years = list(reversed(polio_bgl.columns[4:-2]))\n",
    "cases = list(reversed(polio_bgl.to_numpy()[0][4:-2]))\n",
    "\n",
    "#Just changing the size of the plot so it is easier to read\n",
    "matplotlib.pyplot.rcParams[\"figure.figsize\"] = (20,6)\n",
    "\n",
    "#Plot the data. The years are on the X axis, the percentages on the Y one\n",
    "matplotlib.pyplot.plot(years, cases, label='Cases')\n",
    "matplotlib.pyplot.plot(years, vaccines_bgl['percent_coverage'], label= 'Percentage coverage')\n",
    "matplotlib.pyplot.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***Is the effect of the vaccine visible in this case? Are you able to see a decrease of cases with the increase of the vaccine?***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we will look at worldwide data. As we have the data for each country, we need to calculate the global, so there is some processing to be done first. You do not need to understand the code of the processing, just know that we are adding all the cases in each year and then doing an average of the percentage of vaccination for each year over all the countries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "years = list(range(1980,2020))\n",
    "percentages = [] #List to store the average percentage coverage for each year\n",
    "cases = [] #List to store the number of cases in total for each year\n",
    "#For each of the years, go through the data of that year\n",
    "for year in years:\n",
    "    #Get the data in polio cases for that year and add them taking NaN values as 0\n",
    "    polio_cases = numpy.nansum(polio[str(year)])\n",
    "    \n",
    "    #Get the percentage coverage for all the countries\n",
    "    coverage = vaccines.loc[vaccines['year']==year]\n",
    "    #Calculate the percentage. Add all up and divide by the number of non-NaN values\n",
    "    avg = numpy.nansum(coverage['percent_coverage'])/numpy.count_nonzero(~numpy.isnan(coverage['percent_coverage']))\n",
    "    \n",
    "    #Add the results to the corresponding variables\n",
    "    percentages.append(avg)\n",
    "    cases.append(polio_cases)\n",
    "    \n",
    "#Create a dataframe with the data we have extracted\n",
    "global_data = pandas.DataFrame({'year':years, 'percentage_cov':percentages, 'cases':cases})"
   ]
  },
  {
   "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>year</th>\n",
       "      <th>percentage_cov</th>\n",
       "      <th>cases</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1980</td>\n",
       "      <td>46.038071</td>\n",
       "      <td>52630.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1981</td>\n",
       "      <td>48.500963</td>\n",
       "      <td>65737.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1982</td>\n",
       "      <td>50.197065</td>\n",
       "      <td>51628.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1983</td>\n",
       "      <td>54.637736</td>\n",
       "      <td>39973.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1984</td>\n",
       "      <td>54.693913</td>\n",
       "      <td>35084.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1985</td>\n",
       "      <td>56.766556</td>\n",
       "      <td>38483.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1986</td>\n",
       "      <td>60.615975</td>\n",
       "      <td>32846.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1987</td>\n",
       "      <td>64.051799</td>\n",
       "      <td>39683.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1988</td>\n",
       "      <td>67.672727</td>\n",
       "      <td>34617.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1989</td>\n",
       "      <td>70.582069</td>\n",
       "      <td>26104.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1990</td>\n",
       "      <td>76.875654</td>\n",
       "      <td>23053.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1991</td>\n",
       "      <td>76.013937</td>\n",
       "      <td>13274.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1992</td>\n",
       "      <td>74.645125</td>\n",
       "      <td>15304.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1993</td>\n",
       "      <td>75.305895</td>\n",
       "      <td>10500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1994</td>\n",
       "      <td>76.363533</td>\n",
       "      <td>8666.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1995</td>\n",
       "      <td>78.678419</td>\n",
       "      <td>7043.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1996</td>\n",
       "      <td>79.572816</td>\n",
       "      <td>4070.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1997</td>\n",
       "      <td>79.646279</td>\n",
       "      <td>5186.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1998</td>\n",
       "      <td>78.431946</td>\n",
       "      <td>6347.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1999</td>\n",
       "      <td>79.430041</td>\n",
       "      <td>7141.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2000</td>\n",
       "      <td>79.434200</td>\n",
       "      <td>2971.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2001</td>\n",
       "      <td>78.318016</td>\n",
       "      <td>496.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2002</td>\n",
       "      <td>81.331383</td>\n",
       "      <td>1922.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2003</td>\n",
       "      <td>81.731206</td>\n",
       "      <td>784.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2004</td>\n",
       "      <td>82.030284</td>\n",
       "      <td>1256.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2005</td>\n",
       "      <td>83.745882</td>\n",
       "      <td>2033.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2006</td>\n",
       "      <td>85.000575</td>\n",
       "      <td>2023.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2007</td>\n",
       "      <td>84.774609</td>\n",
       "      <td>1390.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2008</td>\n",
       "      <td>85.139254</td>\n",
       "      <td>1736.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2009</td>\n",
       "      <td>85.259761</td>\n",
       "      <td>1789.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>2010</td>\n",
       "      <td>85.315311</td>\n",
       "      <td>1412.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>2011</td>\n",
       "      <td>86.061553</td>\n",
       "      <td>717.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>2012</td>\n",
       "      <td>87.122333</td>\n",
       "      <td>294.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>2013</td>\n",
       "      <td>86.492634</td>\n",
       "      <td>482.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>2014</td>\n",
       "      <td>85.990995</td>\n",
       "      <td>415.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>2015</td>\n",
       "      <td>85.914318</td>\n",
       "      <td>106.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>2016</td>\n",
       "      <td>86.025881</td>\n",
       "      <td>42.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>2017</td>\n",
       "      <td>86.445866</td>\n",
       "      <td>96.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>2018</td>\n",
       "      <td>85.452012</td>\n",
       "      <td>104.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>2019</td>\n",
       "      <td>85.772713</td>\n",
       "      <td>368.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    year  percentage_cov    cases\n",
       "0   1980       46.038071  52630.0\n",
       "1   1981       48.500963  65737.0\n",
       "2   1982       50.197065  51628.0\n",
       "3   1983       54.637736  39973.0\n",
       "4   1984       54.693913  35084.0\n",
       "5   1985       56.766556  38483.0\n",
       "6   1986       60.615975  32846.0\n",
       "7   1987       64.051799  39683.0\n",
       "8   1988       67.672727  34617.0\n",
       "9   1989       70.582069  26104.0\n",
       "10  1990       76.875654  23053.0\n",
       "11  1991       76.013937  13274.0\n",
       "12  1992       74.645125  15304.0\n",
       "13  1993       75.305895  10500.0\n",
       "14  1994       76.363533   8666.0\n",
       "15  1995       78.678419   7043.0\n",
       "16  1996       79.572816   4070.0\n",
       "17  1997       79.646279   5186.0\n",
       "18  1998       78.431946   6347.0\n",
       "19  1999       79.430041   7141.0\n",
       "20  2000       79.434200   2971.0\n",
       "21  2001       78.318016    496.0\n",
       "22  2002       81.331383   1922.0\n",
       "23  2003       81.731206    784.0\n",
       "24  2004       82.030284   1256.0\n",
       "25  2005       83.745882   2033.0\n",
       "26  2006       85.000575   2023.0\n",
       "27  2007       84.774609   1390.0\n",
       "28  2008       85.139254   1736.0\n",
       "29  2009       85.259761   1789.0\n",
       "30  2010       85.315311   1412.0\n",
       "31  2011       86.061553    717.0\n",
       "32  2012       87.122333    294.0\n",
       "33  2013       86.492634    482.0\n",
       "34  2014       85.990995    415.0\n",
       "35  2015       85.914318    106.0\n",
       "36  2016       86.025881     42.0\n",
       "37  2017       86.445866     96.0\n",
       "38  2018       85.452012    104.0\n",
       "39  2019       85.772713    368.0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Look at the data we have created from the previous one\n",
    "global_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see from the previous table, we now have the global data we needed. As the number of polio cases is quite high in the earlier years, we will plot the percentage coverage and the cases in different scales ,but keeping them in the same plot for it to be easier to read. In the plot, the scale of the cases is to the left, and the scale of the percentage coverage is to the right."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax_left = matplotlib.pyplot.subplots()\n",
    "ax_right = ax_left.twinx()\n",
    "#Plot the cases\n",
    "plt1, = ax_left.plot(global_data['year'], global_data['cases'], label='Cases', color='black')\n",
    "#Plot the percentage coverage\n",
    "plt2, = ax_right.plot(global_data['year'], global_data['percentage_cov'], label='Percentage coverage', color='red')\n",
    "matplotlib.pyplot.legend([plt1,plt2],[\"Cases\", \"Average percentage coverage\"])\n",
    "matplotlib.pyplot.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***Is it clear by the plot if the polio cases decreased with the increase of the coverage of the vaccine?***\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also compute the correlation coefficient between the global cases and the average percentage coverage. Remember that the correlation coefficient measures the relationship between two variables. The value of this coefficient is between -1 and 1, being 0 no relationship at all. A positive value means that both variables increase together, and a negative value means that one increases while the other decreases. The closer the coefficient is to 1 or -1, the stronger the correlation is. \n",
    "\n",
    "***Knowing this, do you expect the correlation coefficient to be positive or negative? Why?***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To calculate the correlation coefficient, we will use the NumPy function [corrcoef()](https://numpy.org/doc/stable/reference/generated/numpy.corrcoef.html). This gives us a a matrix of coefficients, so we need to extract the coefficient between the two variables we feed into it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.9646409089929149"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numpy.corrcoef(global_data['percentage_cov'], global_data['cases'])[0][1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***Does the value have the sign you expected it to have? Do you think the variables are strongly correlated?*** \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " We have seen here that vaccines do help in preventing diseases. Nowadays, polio is almost extinct and the World Health Organisation believes it is possible to eradicate it if we keep the usage of vaccines. Unfortunately, movements like anti-vaxxers are causing [some diseases to come back](https://eu.usatoday.com/story/news/health/2019/03/28/anti-vaxxers-open-door-measles-mumps-old-time-diseases/3295390002/) in countries where they were considered extinct."
   ]
  }
 ],
 "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}