covid19-eda-apr2020.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# COVID-19 Data Exploration\n",
    "\n",
    "Data collected from:\n",
    "\n",
    "https://www.ecdc.europa.eu/en/publications-data/download-todays-data-geographic-distribution-covid-19-cases-worldwide\n",
    "\n",
    "(c) AJEsteves, April 2020 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy  as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
      "                                 Dload  Upload   Total   Spent    Left  Speed\n",
      "100  726k  100  726k    0     0   690k      0  0:00:01  0:00:01 --:--:--  691k\n"
     ]
    }
   ],
   "source": [
    "!curl https://opendata.ecdc.europa.eu/covid19/casedistribution/csv/ > covid19_opendata_ecdc_europa.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "covdf = pd.read_csv('covid19_opendata_ecdc_europa.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dateRep</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>cases</th>\n",
       "      <th>deaths</th>\n",
       "      <th>countriesAndTerritories</th>\n",
       "      <th>geoId</th>\n",
       "      <th>countryterritoryCode</th>\n",
       "      <th>popData2018</th>\n",
       "      <th>continentExp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>24/04/2020</td>\n",
       "      <td>24</td>\n",
       "      <td>4</td>\n",
       "      <td>2020</td>\n",
       "      <td>105</td>\n",
       "      <td>2</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AF</td>\n",
       "      <td>AFG</td>\n",
       "      <td>37172386.0</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>23/04/2020</td>\n",
       "      <td>23</td>\n",
       "      <td>4</td>\n",
       "      <td>2020</td>\n",
       "      <td>84</td>\n",
       "      <td>4</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AF</td>\n",
       "      <td>AFG</td>\n",
       "      <td>37172386.0</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>22/04/2020</td>\n",
       "      <td>22</td>\n",
       "      <td>4</td>\n",
       "      <td>2020</td>\n",
       "      <td>61</td>\n",
       "      <td>1</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AF</td>\n",
       "      <td>AFG</td>\n",
       "      <td>37172386.0</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>21/04/2020</td>\n",
       "      <td>21</td>\n",
       "      <td>4</td>\n",
       "      <td>2020</td>\n",
       "      <td>35</td>\n",
       "      <td>2</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AF</td>\n",
       "      <td>AFG</td>\n",
       "      <td>37172386.0</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20/04/2020</td>\n",
       "      <td>20</td>\n",
       "      <td>4</td>\n",
       "      <td>2020</td>\n",
       "      <td>88</td>\n",
       "      <td>3</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AF</td>\n",
       "      <td>AFG</td>\n",
       "      <td>37172386.0</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      dateRep  day  month  year  cases  deaths countriesAndTerritories geoId  \\\n",
       "0  24/04/2020   24      4  2020    105       2             Afghanistan    AF   \n",
       "1  23/04/2020   23      4  2020     84       4             Afghanistan    AF   \n",
       "2  22/04/2020   22      4  2020     61       1             Afghanistan    AF   \n",
       "3  21/04/2020   21      4  2020     35       2             Afghanistan    AF   \n",
       "4  20/04/2020   20      4  2020     88       3             Afghanistan    AF   \n",
       "\n",
       "  countryterritoryCode  popData2018 continentExp  \n",
       "0                  AFG   37172386.0         Asia  \n",
       "1                  AFG   37172386.0         Asia  \n",
       "2                  AFG   37172386.0         Asia  \n",
       "3                  AFG   37172386.0         Asia  \n",
       "4                  AFG   37172386.0         Asia  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covdf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dateRep</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>cases</th>\n",
       "      <th>deaths</th>\n",
       "      <th>countriesAndTerritories</th>\n",
       "      <th>geoId</th>\n",
       "      <th>countryterritoryCode</th>\n",
       "      <th>popData2018</th>\n",
       "      <th>continentExp</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12796</th>\n",
       "      <td>25/03/2020</td>\n",
       "      <td>25</td>\n",
       "      <td>3</td>\n",
       "      <td>2020</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>ZW</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>14439018.0</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12797</th>\n",
       "      <td>24/03/2020</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "      <td>2020</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>ZW</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>14439018.0</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12798</th>\n",
       "      <td>23/03/2020</td>\n",
       "      <td>23</td>\n",
       "      <td>3</td>\n",
       "      <td>2020</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>ZW</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>14439018.0</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12799</th>\n",
       "      <td>22/03/2020</td>\n",
       "      <td>22</td>\n",
       "      <td>3</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>ZW</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>14439018.0</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12800</th>\n",
       "      <td>21/03/2020</td>\n",
       "      <td>21</td>\n",
       "      <td>3</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>ZW</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>14439018.0</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "          dateRep  day  month  year  cases  deaths countriesAndTerritories  \\\n",
       "12796  25/03/2020   25      3  2020      0       0                Zimbabwe   \n",
       "12797  24/03/2020   24      3  2020      0       1                Zimbabwe   \n",
       "12798  23/03/2020   23      3  2020      0       0                Zimbabwe   \n",
       "12799  22/03/2020   22      3  2020      1       0                Zimbabwe   \n",
       "12800  21/03/2020   21      3  2020      1       0                Zimbabwe   \n",
       "\n",
       "      geoId countryterritoryCode  popData2018 continentExp  \n",
       "12796    ZW                  ZWE   14439018.0       Africa  \n",
       "12797    ZW                  ZWE   14439018.0       Africa  \n",
       "12798    ZW                  ZWE   14439018.0       Africa  \n",
       "12799    ZW                  ZWE   14439018.0       Africa  \n",
       "12800    ZW                  ZWE   14439018.0       Africa  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covdf.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 12801 entries, 0 to 12800\n",
      "Data columns (total 11 columns):\n",
      " #   Column                   Non-Null Count  Dtype  \n",
      "---  ------                   --------------  -----  \n",
      " 0   dateRep                  12801 non-null  object \n",
      " 1   day                      12801 non-null  int64  \n",
      " 2   month                    12801 non-null  int64  \n",
      " 3   year                     12801 non-null  int64  \n",
      " 4   cases                    12801 non-null  int64  \n",
      " 5   deaths                   12801 non-null  int64  \n",
      " 6   countriesAndTerritories  12801 non-null  object \n",
      " 7   geoId                    12760 non-null  object \n",
      " 8   countryterritoryCode     12664 non-null  object \n",
      " 9   popData2018              12694 non-null  float64\n",
      " 10  continentExp             12801 non-null  object \n",
      "dtypes: float64(1), int64(5), object(5)\n",
      "memory usage: 1.1+ MB\n"
     ]
    }
   ],
   "source": [
    "covdf.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>cases</th>\n",
       "      <th>deaths</th>\n",
       "      <th>popData2018</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>12801.000000</td>\n",
       "      <td>12801.000000</td>\n",
       "      <td>12801.000000</td>\n",
       "      <td>12801.000000</td>\n",
       "      <td>12801.000000</td>\n",
       "      <td>1.269400e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>15.787595</td>\n",
       "      <td>2.952816</td>\n",
       "      <td>2019.994766</td>\n",
       "      <td>208.431763</td>\n",
       "      <td>14.861026</td>\n",
       "      <td>5.716106e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.468072</td>\n",
       "      <td>1.266747</td>\n",
       "      <td>0.072159</td>\n",
       "      <td>1488.808879</td>\n",
       "      <td>118.515802</td>\n",
       "      <td>1.876496e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2019.000000</td>\n",
       "      <td>-9.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>9.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2020.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.866376e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>16.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2020.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.956011e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>23.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2020.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.797855e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>31.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>2020.000000</td>\n",
       "      <td>37289.000000</td>\n",
       "      <td>4928.000000</td>\n",
       "      <td>1.392730e+09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                day         month          year         cases        deaths  \\\n",
       "count  12801.000000  12801.000000  12801.000000  12801.000000  12801.000000   \n",
       "mean      15.787595      2.952816   2019.994766    208.431763     14.861026   \n",
       "std        8.468072      1.266747      0.072159   1488.808879    118.515802   \n",
       "min        1.000000      1.000000   2019.000000     -9.000000      0.000000   \n",
       "25%        9.000000      2.000000   2020.000000      0.000000      0.000000   \n",
       "50%       16.000000      3.000000   2020.000000      1.000000      0.000000   \n",
       "75%       23.000000      4.000000   2020.000000     25.000000      0.000000   \n",
       "max       31.000000     12.000000   2020.000000  37289.000000   4928.000000   \n",
       "\n",
       "        popData2018  \n",
       "count  1.269400e+04  \n",
       "mean   5.716106e+07  \n",
       "std    1.876496e+08  \n",
       "min    1.000000e+03  \n",
       "25%    2.866376e+06  \n",
       "50%    9.956011e+06  \n",
       "75%    3.797855e+07  \n",
       "max    1.392730e+09  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covdf.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
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       "      <th>min</th>\n",
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       "      <th>max</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>day</th>\n",
       "      <td>12801.0</td>\n",
       "      <td>1.578759e+01</td>\n",
       "      <td>8.468072e+00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>3.100000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <td>12801.0</td>\n",
       "      <td>2.952816e+00</td>\n",
       "      <td>1.266747e+00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.200000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <td>12801.0</td>\n",
       "      <td>2.019995e+03</td>\n",
       "      <td>7.215940e-02</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>2.020000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cases</th>\n",
       "      <td>12801.0</td>\n",
       "      <td>2.084318e+02</td>\n",
       "      <td>1.488809e+03</td>\n",
       "      <td>-9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>3.728900e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>deaths</th>\n",
       "      <td>12801.0</td>\n",
       "      <td>1.486103e+01</td>\n",
       "      <td>1.185158e+02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.928000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>popData2018</th>\n",
       "      <td>12694.0</td>\n",
       "      <td>5.716106e+07</td>\n",
       "      <td>1.876496e+08</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>2866376.0</td>\n",
       "      <td>9956011.0</td>\n",
       "      <td>37978548.0</td>\n",
       "      <td>1.392730e+09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               count          mean           std     min        25%  \\\n",
       "day          12801.0  1.578759e+01  8.468072e+00     1.0        9.0   \n",
       "month        12801.0  2.952816e+00  1.266747e+00     1.0        2.0   \n",
       "year         12801.0  2.019995e+03  7.215940e-02  2019.0     2020.0   \n",
       "cases        12801.0  2.084318e+02  1.488809e+03    -9.0        0.0   \n",
       "deaths       12801.0  1.486103e+01  1.185158e+02     0.0        0.0   \n",
       "popData2018  12694.0  5.716106e+07  1.876496e+08  1000.0  2866376.0   \n",
       "\n",
       "                   50%         75%           max  \n",
       "day               16.0        23.0  3.100000e+01  \n",
       "month              3.0         4.0  1.200000e+01  \n",
       "year            2020.0      2020.0  2.020000e+03  \n",
       "cases              1.0        25.0  3.728900e+04  \n",
       "deaths             0.0         0.0  4.928000e+03  \n",
       "popData2018  9956011.0  37978548.0  1.392730e+09  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covdf.describe().transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MEAN:     208.43\n",
      "STD DEV:  1488.81\n",
      "VARIANCE: 2216551.88\n",
      "MINIMUM:  -9.00\n",
      "MAXIMUM:  37289.00\n"
     ]
    }
   ],
   "source": [
    "print(f\"MEAN:     {covdf['cases'].mean():.2f}\")\n",
    "print(f\"STD DEV:  {covdf['cases'].std():.2f}\")\n",
    "print(f\"VARIANCE: {covdf['cases'].var():.2f}\")\n",
    "print(f\"MINIMUM:  {covdf['cases'].min():.2f}\")\n",
    "print(f\"MAXIMUM:  {covdf['cases'].max():.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Select the most relevant columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "    }\n",
       "\n",
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       "        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>dateRep</th>\n",
       "      <th>cases</th>\n",
       "      <th>deaths</th>\n",
       "      <th>countriesAndTerritories</th>\n",
       "      <th>popData2018</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>24/04/2020</td>\n",
       "      <td>105</td>\n",
       "      <td>2</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>37172386.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>23/04/2020</td>\n",
       "      <td>84</td>\n",
       "      <td>4</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>37172386.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>22/04/2020</td>\n",
       "      <td>61</td>\n",
       "      <td>1</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>37172386.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>21/04/2020</td>\n",
       "      <td>35</td>\n",
       "      <td>2</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>37172386.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20/04/2020</td>\n",
       "      <td>88</td>\n",
       "      <td>3</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>37172386.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      dateRep  cases  deaths countriesAndTerritories  popData2018\n",
       "0  24/04/2020    105       2             Afghanistan   37172386.0\n",
       "1  23/04/2020     84       4             Afghanistan   37172386.0\n",
       "2  22/04/2020     61       1             Afghanistan   37172386.0\n",
       "3  21/04/2020     35       2             Afghanistan   37172386.0\n",
       "4  20/04/2020     88       3             Afghanistan   37172386.0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covdf1 = covdf[['dateRep', 'cases', 'deaths', 'countriesAndTerritories', 'popData2018']]\n",
    "covdf1.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Filter rows for Portugal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dateRep</th>\n",
       "      <th>cases</th>\n",
       "      <th>deaths</th>\n",
       "      <th>countriesAndTerritories</th>\n",
       "      <th>popData2018</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9593</th>\n",
       "      <td>24/04/2020</td>\n",
       "      <td>371</td>\n",
       "      <td>35</td>\n",
       "      <td>Portugal</td>\n",
       "      <td>10281762.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9594</th>\n",
       "      <td>23/04/2020</td>\n",
       "      <td>603</td>\n",
       "      <td>23</td>\n",
       "      <td>Portugal</td>\n",
       "      <td>10281762.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9595</th>\n",
       "      <td>22/04/2020</td>\n",
       "      <td>516</td>\n",
       "      <td>27</td>\n",
       "      <td>Portugal</td>\n",
       "      <td>10281762.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9596</th>\n",
       "      <td>21/04/2020</td>\n",
       "      <td>657</td>\n",
       "      <td>21</td>\n",
       "      <td>Portugal</td>\n",
       "      <td>10281762.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9597</th>\n",
       "      <td>20/04/2020</td>\n",
       "      <td>521</td>\n",
       "      <td>27</td>\n",
       "      <td>Portugal</td>\n",
       "      <td>10281762.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         dateRep  cases  deaths countriesAndTerritories  popData2018\n",
       "9593  24/04/2020    371      35                Portugal   10281762.0\n",
       "9594  23/04/2020    603      23                Portugal   10281762.0\n",
       "9595  22/04/2020    516      27                Portugal   10281762.0\n",
       "9596  21/04/2020    657      21                Portugal   10281762.0\n",
       "9597  20/04/2020    521      27                Portugal   10281762.0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pt_covdf = covdf1[covdf1['countriesAndTerritories']=='Portugal']\n",
    "pt_covdf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 53 entries, 9593 to 9645\n",
      "Data columns (total 5 columns):\n",
      " #   Column                   Non-Null Count  Dtype  \n",
      "---  ------                   --------------  -----  \n",
      " 0   dateRep                  53 non-null     object \n",
      " 1   cases                    53 non-null     int64  \n",
      " 2   deaths                   53 non-null     int64  \n",
      " 3   countriesAndTerritories  53 non-null     object \n",
      " 4   popData2018              53 non-null     float64\n",
      "dtypes: float64(1), int64(2), object(2)\n",
      "memory usage: 2.5+ KB\n"
     ]
    }
   ],
   "source": [
    "pt_covdf.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>dateRep</th>\n",
       "      <th>cases</th>\n",
       "      <th>deaths</th>\n",
       "      <th>countriesAndTerritories</th>\n",
       "      <th>popData2018</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9593</td>\n",
       "      <td>24/04/2020</td>\n",
       "      <td>371</td>\n",
       "      <td>35</td>\n",
       "      <td>Portugal</td>\n",
       "      <td>10281762.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9594</td>\n",
       "      <td>23/04/2020</td>\n",
       "      <td>603</td>\n",
       "      <td>23</td>\n",
       "      <td>Portugal</td>\n",
       "      <td>10281762.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9595</td>\n",
       "      <td>22/04/2020</td>\n",
       "      <td>516</td>\n",
       "      <td>27</td>\n",
       "      <td>Portugal</td>\n",
       "      <td>10281762.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9596</td>\n",
       "      <td>21/04/2020</td>\n",
       "      <td>657</td>\n",
       "      <td>21</td>\n",
       "      <td>Portugal</td>\n",
       "      <td>10281762.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9597</td>\n",
       "      <td>20/04/2020</td>\n",
       "      <td>521</td>\n",
       "      <td>27</td>\n",
       "      <td>Portugal</td>\n",
       "      <td>10281762.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index     dateRep  cases  deaths countriesAndTerritories  popData2018\n",
       "0   9593  24/04/2020    371      35                Portugal   10281762.0\n",
       "1   9594  23/04/2020    603      23                Portugal   10281762.0\n",
       "2   9595  22/04/2020    516      27                Portugal   10281762.0\n",
       "3   9596  21/04/2020    657      21                Portugal   10281762.0\n",
       "4   9597  20/04/2020    521      27                Portugal   10281762.0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pt_covdf1 = pt_covdf.reset_index()\n",
    "\n",
    "pt_covdf1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dateRep</th>\n",
       "      <th>cases</th>\n",
       "      <th>deaths</th>\n",
       "      <th>countriesAndTerritories</th>\n",
       "      <th>popData2018</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9595</th>\n",
       "      <td>22/04/2020</td>\n",
       "      <td>516</td>\n",
       "      <td>27</td>\n",
       "      <td>Portugal</td>\n",
       "      <td>10281762.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9597</th>\n",
       "      <td>20/04/2020</td>\n",
       "      <td>521</td>\n",
       "      <td>27</td>\n",
       "      <td>Portugal</td>\n",
       "      <td>10281762.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         dateRep  cases  deaths countriesAndTerritories  popData2018\n",
       "9595  22/04/2020    516      27                Portugal   10281762.0\n",
       "9597  20/04/2020    521      27                Portugal   10281762.0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pt_covdf1A = pt_covdf.iloc[[2,4]]\n",
    "\n",
    "pt_covdf1A.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "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",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dateRep</th>\n",
       "      <th>cases</th>\n",
       "      <th>deaths</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>24/04/2020</td>\n",
       "      <td>371</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>23/04/2020</td>\n",
       "      <td>603</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>22/04/2020</td>\n",
       "      <td>516</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>21/04/2020</td>\n",
       "      <td>657</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20/04/2020</td>\n",
       "      <td>521</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      dateRep  cases  deaths\n",
       "0  24/04/2020    371      35\n",
       "1  23/04/2020    603      23\n",
       "2  22/04/2020    516      27\n",
       "3  21/04/2020    657      21\n",
       "4  20/04/2020    521      27"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pt_covdf2 = pt_covdf1.drop(['index','countriesAndTerritories','popData2018'],axis=1)\n",
    "\n",
    "pt_covdf2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sort data by ascending value of \"dateRep\" column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dateRep</th>\n",
       "      <th>cases</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-04-24</td>\n",
       "      <td>371</td>\n",
       "      <td>35</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-04-23</td>\n",
       "      <td>603</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-04-22</td>\n",
       "      <td>516</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-04-21</td>\n",
       "      <td>657</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-04-20</td>\n",
       "      <td>521</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     dateRep  cases  deaths\n",
       "0 2020-04-24    371      35\n",
       "1 2020-04-23    603      23\n",
       "2 2020-04-22    516      27\n",
       "3 2020-04-21    657      21\n",
       "4 2020-04-20    521      27"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pt_covdf2['dateRep'] = pd.to_datetime(pt_covdf2.dateRep,dayfirst=True)\n",
    "\n",
    "pt_covdf2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": false
   },
   "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>dateRep</th>\n",
       "      <th>cases</th>\n",
       "      <th>deaths</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>2020-03-03</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>2020-03-04</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>2020-03-05</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>2020-03-06</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>2020-03-07</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      dateRep  cases  deaths\n",
       "52 2020-03-03      2       0\n",
       "51 2020-03-04      2       0\n",
       "50 2020-03-05      1       0\n",
       "49 2020-03-06      4       0\n",
       "48 2020-03-07      4       0"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pt_covdf3 = pt_covdf2.sort_values(by=['dateRep'], axis=0, ascending=True)\n",
    "\n",
    "pt_covdf3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>dateRep</th>\n",
       "      <th>cases</th>\n",
       "      <th>deaths</th>\n",
       "      <th>accum_cases</th>\n",
       "      <th>accum_deaths</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-03-03</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-03-04</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-03-05</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-03-06</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-03-07</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     dateRep  cases  deaths  accum_cases  accum_deaths\n",
       "0 2020-03-03      2       0            2             0\n",
       "1 2020-03-04      2       0            4             0\n",
       "2 2020-03-05      1       0            5             0\n",
       "3 2020-03-06      4       0            9             0\n",
       "4 2020-03-07      4       0           13             0"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pt_covdf3 = pt_covdf3.reset_index()\n",
    "pt_covdf3 = pt_covdf3.drop(['index'],axis=1)\n",
    "\n",
    "pt_covdf3.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compute accumulated sum of `cases` and `deaths`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>index</th>\n",
       "      <th>dateRep</th>\n",
       "      <th>cases</th>\n",
       "      <th>deaths</th>\n",
       "      <th>accum_cases</th>\n",
       "      <th>accum_deaths</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>4</td>\n",
       "      <td>2020-04-20</td>\n",
       "      <td>521</td>\n",
       "      <td>27</td>\n",
       "      <td>20206</td>\n",
       "      <td>714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>3</td>\n",
       "      <td>2020-04-21</td>\n",
       "      <td>657</td>\n",
       "      <td>21</td>\n",
       "      <td>20863</td>\n",
       "      <td>735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>2</td>\n",
       "      <td>2020-04-22</td>\n",
       "      <td>516</td>\n",
       "      <td>27</td>\n",
       "      <td>21379</td>\n",
       "      <td>762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>1</td>\n",
       "      <td>2020-04-23</td>\n",
       "      <td>603</td>\n",
       "      <td>23</td>\n",
       "      <td>21982</td>\n",
       "      <td>785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>0</td>\n",
       "      <td>2020-04-24</td>\n",
       "      <td>371</td>\n",
       "      <td>35</td>\n",
       "      <td>22353</td>\n",
       "      <td>820</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    index    dateRep  cases  deaths  accum_cases  accum_deaths\n",
       "48      4 2020-04-20    521      27        20206           714\n",
       "49      3 2020-04-21    657      21        20863           735\n",
       "50      2 2020-04-22    516      27        21379           762\n",
       "51      1 2020-04-23    603      23        21982           785\n",
       "52      0 2020-04-24    371      35        22353           820"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pt_covdf3['accum_cases']  = pt_covdf3['cases'].cumsum()\n",
    "pt_covdf3['accum_deaths'] = pt_covdf3['deaths'].cumsum()\n",
    "\n",
    "pt_covdf3.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.gca()\n",
    "\n",
    "pt_covdf3.plot(kind='line',x='dateRep',y='cases', figsize=(12,6), color='red', ax=ax)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.gca()\n",
    "\n",
    "pt_covdf3.plot(kind='line',x='dateRep',y='cases',figsize=(12,6),color='blue', ax=ax)\n",
    "# plt.ylabel('New Cases')\n",
    "\n",
    "pt_covdf3.plot(kind='line',x='dateRep',y='deaths',figsize=(12,6),color='red', ax=ax)\n",
    "plt.title('CoVID-19: Portugal')\n",
    "#plt.ylabel('Deaths')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.dates as mdates\n",
    "from matplotlib.dates import DateFormatter\n",
    "\n",
    "fig, axs = plt.subplots(2, 1, figsize=(14, 10))\n",
    "\n",
    "# Define the date format\n",
    "date_form = DateFormatter(\"%m-%d\")\n",
    "\n",
    "axs[0].plot(pt_covdf3['dateRep'], pt_covdf3['cases'], 'bD-')\n",
    "axs[0].set_xlabel('date')\n",
    "axs[0].set_ylabel('New cases')\n",
    "axs[0].grid(True)\n",
    "axs[0].xaxis.set_major_formatter(date_form)\n",
    "axs[0].xaxis.set_minor_locator(mdates.DayLocator())\n",
    "axs[0].set_title('Daily new cases')\n",
    "\n",
    "axs[1].plot(pt_covdf3['dateRep'], pt_covdf3['deaths'], \"ro-\")\n",
    "axs[1].set_xlabel('date')\n",
    "axs[1].set_ylabel('Deaths')\n",
    "axs[1].grid(True)\n",
    "axs[1].xaxis.set_major_formatter(date_form)\n",
    "axs[1].xaxis.set_minor_locator(mdates.DayLocator())\n",
    "axs[1].set_title('Daily deaths')\n",
    "\n",
    "fig.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.dates as mdates\n",
    "from   matplotlib.dates import DateFormatter\n",
    "\n",
    "fig, axs = plt.subplots(2, 1, figsize=(14, 10))\n",
    "\n",
    "# Define the date format\n",
    "date_form = DateFormatter(\"%m-%d\")\n",
    "\n",
    "axs[0].plot(pt_covdf3['dateRep'], pt_covdf3['accum_cases'], 'bD-')\n",
    "axs[0].set_xlabel('date')\n",
    "axs[0].set_ylabel('Total cases')\n",
    "axs[0].grid(True)\n",
    "axs[0].xaxis.set_major_formatter(date_form)\n",
    "axs[0].xaxis.set_minor_locator(mdates.DayLocator())\n",
    "axs[0].set_title('Total cases')\n",
    "\n",
    "axs[1].plot(pt_covdf3['dateRep'], pt_covdf3['accum_deaths'], \"ro-\")\n",
    "axs[1].set_xlabel('date')\n",
    "axs[1].set_ylabel('Total deaths')\n",
    "axs[1].grid(True)\n",
    "axs[1].xaxis.set_major_formatter(date_form)\n",
    "axs[1].xaxis.set_minor_locator(mdates.DayLocator())\n",
    "axs[1].set_title('Total deaths')\n",
    "\n",
    "fig.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}