COVID19.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analysis of COVID-19 in UK, GER, FR, IT, GR and BG using API. Compare the cases and deaths across time and cumulative cases on the last date of the study. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "import json\n",
    "import pandas as pd\n",
    "import scipy\n",
    "import datetime as dt\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import altair as alt\n",
    "\n",
    "import numpy as np\n",
    "from pandas.plotting import register_matplotlib_converters \n",
    "register_matplotlib_converters()  \n",
    "from datetime import datetime,timedelta\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from scipy.optimize import curve_fit\n",
    "from scipy.optimize import fsolve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19666\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>cases</th>\n",
       "      <th>deaths</th>\n",
       "      <th>country</th>\n",
       "      <th>code</th>\n",
       "      <th>population</th>\n",
       "      <th>continentExp</th>\n",
       "      <th>cases_cum</th>\n",
       "      <th>deaths_cum</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>31</td>\n",
       "      <td>12</td>\n",
       "      <td>2019</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AF</td>\n",
       "      <td>37172386</td>\n",
       "      <td>Asia</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2020</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AF</td>\n",
       "      <td>37172386</td>\n",
       "      <td>Asia</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-01-02</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2020</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AF</td>\n",
       "      <td>37172386</td>\n",
       "      <td>Asia</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-01-03</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2020</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AF</td>\n",
       "      <td>37172386</td>\n",
       "      <td>Asia</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-01-04</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2020</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AF</td>\n",
       "      <td>37172386</td>\n",
       "      <td>Asia</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date  day  month  year  cases  deaths      country code  population  \\\n",
       "0  2019-12-31   31     12  2019      0       0  Afghanistan   AF    37172386   \n",
       "1  2020-01-01    1      1  2020      0       0  Afghanistan   AF    37172386   \n",
       "2  2020-01-02    2      1  2020      0       0  Afghanistan   AF    37172386   \n",
       "3  2020-01-03    3      1  2020      0       0  Afghanistan   AF    37172386   \n",
       "4  2020-01-04    4      1  2020      0       0  Afghanistan   AF    37172386   \n",
       "\n",
       "  continentExp  cases_cum  deaths_cum  \n",
       "0         Asia          0           0  \n",
       "1         Asia          0           0  \n",
       "2         Asia          0           0  \n",
       "3         Asia          0           0  \n",
       "4         Asia          0           0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# make a request of COVID-19 API data for all countries and covert JSON format to pandas dataframe \n",
    "payload = {'code': 'ALL'} #  If you want to query just Greece, replace this line with {'code': 'Greece'}\n",
    "URL = 'https://api.statworx.com/covid'\n",
    "data = requests.post(url=URL, data=json.dumps(payload))\n",
    "df = pd.DataFrame.from_dict(json.loads(data.text))\n",
    "print(len(df))\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert date column (str) to datetime\n",
    "df['date'] = pd.to_datetime(df['date'], format='%Y-%m-%d') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15579\n"
     ]
    },
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>cases</th>\n",
       "      <th>deaths</th>\n",
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       "      <th>population</th>\n",
       "      <th>continentExp</th>\n",
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       "      <th>deaths_cum</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>2020-03-01</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2020</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AF</td>\n",
       "      <td>37172386</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>2020-03-02</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2020</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AF</td>\n",
       "      <td>37172386</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>2020-03-08</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>2020</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AF</td>\n",
       "      <td>37172386</td>\n",
       "      <td>Asia</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>2020-03-11</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>2020</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AF</td>\n",
       "      <td>37172386</td>\n",
       "      <td>Asia</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>2020-03-15</td>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "      <td>2020</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AF</td>\n",
       "      <td>37172386</td>\n",
       "      <td>Asia</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date  day  month  year  cases  deaths      country code  population  \\\n",
       "61 2020-03-01    1      3  2020      0       0  Afghanistan   AF    37172386   \n",
       "62 2020-03-02    2      3  2020      0       0  Afghanistan   AF    37172386   \n",
       "63 2020-03-08    8      3  2020      3       0  Afghanistan   AF    37172386   \n",
       "64 2020-03-11   11      3  2020      3       0  Afghanistan   AF    37172386   \n",
       "65 2020-03-15   15      3  2020      3       0  Afghanistan   AF    37172386   \n",
       "\n",
       "   continentExp  cases_cum  deaths_cum  \n",
       "61         Asia          1           0  \n",
       "62         Asia          1           0  \n",
       "63         Asia          4           0  \n",
       "64         Asia          7           0  \n",
       "65         Asia         10           0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df[df['date'] >= pd.to_datetime('2020-03-01') ] \n",
    "print(len(df))\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "      <th>61</th>\n",
       "      <td>2020-03-01</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AF</td>\n",
       "      <td>37172386</td>\n",
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       "      <td>1</td>\n",
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       "      <th>62</th>\n",
       "      <td>2020-03-02</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AF</td>\n",
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       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>2020-03-08</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AF</td>\n",
       "      <td>37172386</td>\n",
       "      <td>Asia</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>2020-03-11</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AF</td>\n",
       "      <td>37172386</td>\n",
       "      <td>Asia</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>2020-03-15</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AF</td>\n",
       "      <td>37172386</td>\n",
       "      <td>Asia</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "         date  cases  deaths      country code  population continentExp  \\\n",
       "61 2020-03-01      0       0  Afghanistan   AF    37172386         Asia   \n",
       "62 2020-03-02      0       0  Afghanistan   AF    37172386         Asia   \n",
       "63 2020-03-08      3       0  Afghanistan   AF    37172386         Asia   \n",
       "64 2020-03-11      3       0  Afghanistan   AF    37172386         Asia   \n",
       "65 2020-03-15      3       0  Afghanistan   AF    37172386         Asia   \n",
       "\n",
       "    cases_cum  deaths_cum  \n",
       "61          1           0  \n",
       "62          1           0  \n",
       "63          4           0  \n",
       "64          7           0  \n",
       "65         10           0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.drop(['day', 'month', 'year'], axis=1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date            datetime64[ns]\n",
       "cases                    int64\n",
       "deaths                   int64\n",
       "country                 object\n",
       "code                    object\n",
       "population               int64\n",
       "continentExp            object\n",
       "cases_cum                int64\n",
       "deaths_cum               int64\n",
       "dtype: object"
      ]
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     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "      <td>2020-03-08</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>Bulgaria</td>\n",
       "      <td>BG</td>\n",
       "      <td>7024216</td>\n",
       "      <td>Europe</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-03-09</td>\n",
       "      <td>2</td>\n",
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       "      <td>Bulgaria</td>\n",
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       "      <th>2</th>\n",
       "      <td>2020-03-12</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Bulgaria</td>\n",
       "      <td>BG</td>\n",
       "      <td>7024216</td>\n",
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       "      <td>7</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-03-13</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>Bulgaria</td>\n",
       "      <td>BG</td>\n",
       "      <td>7024216</td>\n",
       "      <td>Europe</td>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-03-14</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>Bulgaria</td>\n",
       "      <td>BG</td>\n",
       "      <td>7024216</td>\n",
       "      <td>Europe</td>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        date  cases  deaths   country code  population continentExp  \\\n",
       "0 2020-03-08      2       0  Bulgaria   BG     7024216       Europe   \n",
       "1 2020-03-09      2       0  Bulgaria   BG     7024216       Europe   \n",
       "2 2020-03-12      3       1  Bulgaria   BG     7024216       Europe   \n",
       "3 2020-03-13     16       0  Bulgaria   BG     7024216       Europe   \n",
       "4 2020-03-14      8       0  Bulgaria   BG     7024216       Europe   \n",
       "\n",
       "   cases_cum  deaths_cum  \n",
       "0          2           0  \n",
       "1          4           0  \n",
       "2          7           1  \n",
       "3         23           1  \n",
       "4         31           1  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# select 6 countries and reset the index of this dataframe \n",
    "df = (df.loc[df['country'].isin(['United_Kingdom', 'Germany', 'France', 'Italy','Greece', 'Bulgaria'])]\n",
    "      .reset_index()\n",
    "      .drop(['index'], axis=1)\n",
    "     )\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().values.any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert population in millions (for visualization only)\n",
    "df['population_millions']=df['population']/10000000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x11be1bf90>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_pop=sns.catplot(x='country', y='population_millions', data= df,kind=\"bar\", height=7)\n",
    "plot_pop.set_axis_labels(\"Country\", \"Population (in millions)\")  "
   ]
  },
  {
   "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>cases</th>\n",
       "      <th>deaths</th>\n",
       "      <th>population</th>\n",
       "      <th>cases_cum</th>\n",
       "      <th>deaths_cum</th>\n",
       "      <th>population_millions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>517.000000</td>\n",
       "      <td>517.000000</td>\n",
       "      <td>5.170000e+02</td>\n",
       "      <td>517.000000</td>\n",
       "      <td>517.000000</td>\n",
       "      <td>517.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1595.800774</td>\n",
       "      <td>207.282398</td>\n",
       "      <td>4.997875e+07</td>\n",
       "      <td>71799.794971</td>\n",
       "      <td>8559.123791</td>\n",
       "      <td>4.997875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1903.182777</td>\n",
       "      <td>294.549968</td>\n",
       "      <td>2.896318e+07</td>\n",
       "      <td>81051.805045</td>\n",
       "      <td>11622.229057</td>\n",
       "      <td>2.896318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-525.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.024216e+06</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.702422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>39.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.072767e+07</td>\n",
       "      <td>1652.000000</td>\n",
       "      <td>53.000000</td>\n",
       "      <td>1.072767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>665.000000</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>6.648899e+07</td>\n",
       "      <td>23980.000000</td>\n",
       "      <td>860.000000</td>\n",
       "      <td>6.648899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2931.000000</td>\n",
       "      <td>319.000000</td>\n",
       "      <td>6.698724e+07</td>\n",
       "      <td>141919.000000</td>\n",
       "      <td>17167.000000</td>\n",
       "      <td>6.698724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>8719.000000</td>\n",
       "      <td>2004.000000</td>\n",
       "      <td>8.292792e+07</td>\n",
       "      <td>265227.000000</td>\n",
       "      <td>37048.000000</td>\n",
       "      <td>8.292792</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             cases       deaths    population      cases_cum    deaths_cum  \\\n",
       "count   517.000000   517.000000  5.170000e+02     517.000000    517.000000   \n",
       "mean   1595.800774   207.282398  4.997875e+07   71799.794971   8559.123791   \n",
       "std    1903.182777   294.549968  2.896318e+07   81051.805045  11622.229057   \n",
       "min    -525.000000     0.000000  7.024216e+06       2.000000      0.000000   \n",
       "25%      39.000000     2.000000  1.072767e+07    1652.000000     53.000000   \n",
       "50%     665.000000    64.000000  6.648899e+07   23980.000000    860.000000   \n",
       "75%    2931.000000   319.000000  6.698724e+07  141919.000000  17167.000000   \n",
       "max    8719.000000  2004.000000  8.292792e+07  265227.000000  37048.000000   \n",
       "\n",
       "       population_millions  \n",
       "count           517.000000  \n",
       "mean              4.997875  \n",
       "std               2.896318  \n",
       "min               0.702422  \n",
       "25%               1.072767  \n",
       "50%               6.648899  \n",
       "75%               6.698724  \n",
       "max               8.292792  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>cases</th>\n",
       "      <th>deaths</th>\n",
       "      <th>population</th>\n",
       "      <th>cases_cum</th>\n",
       "      <th>deaths_cum</th>\n",
       "      <th>population_millions</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Bulgaria</th>\n",
       "      <td>31.139241</td>\n",
       "      <td>1.683544</td>\n",
       "      <td>7024216.0</td>\n",
       "      <td>1052.417722</td>\n",
       "      <td>48.240506</td>\n",
       "      <td>0.702422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>1653.386364</td>\n",
       "      <td>324.181818</td>\n",
       "      <td>66987244.0</td>\n",
       "      <td>80320.920455</td>\n",
       "      <td>13725.806818</td>\n",
       "      <td>6.698724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>2037.579545</td>\n",
       "      <td>94.875000</td>\n",
       "      <td>82927922.0</td>\n",
       "      <td>100489.761364</td>\n",
       "      <td>3542.250000</td>\n",
       "      <td>8.292792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Greece</th>\n",
       "      <td>33.581395</td>\n",
       "      <td>2.011628</td>\n",
       "      <td>10727668.0</td>\n",
       "      <td>1738.325581</td>\n",
       "      <td>87.081395</td>\n",
       "      <td>1.072767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>2609.852273</td>\n",
       "      <td>374.250000</td>\n",
       "      <td>60431283.0</td>\n",
       "      <td>135144.147727</td>\n",
       "      <td>17820.454545</td>\n",
       "      <td>6.043128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United_Kingdom</th>\n",
       "      <td>3013.738636</td>\n",
       "      <td>421.000000</td>\n",
       "      <td>66488991.0</td>\n",
       "      <td>103225.363636</td>\n",
       "      <td>15067.931818</td>\n",
       "      <td>6.648899</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      cases      deaths  population      cases_cum  \\\n",
       "country                                                              \n",
       "Bulgaria          31.139241    1.683544   7024216.0    1052.417722   \n",
       "France          1653.386364  324.181818  66987244.0   80320.920455   \n",
       "Germany         2037.579545   94.875000  82927922.0  100489.761364   \n",
       "Greece            33.581395    2.011628  10727668.0    1738.325581   \n",
       "Italy           2609.852273  374.250000  60431283.0  135144.147727   \n",
       "United_Kingdom  3013.738636  421.000000  66488991.0  103225.363636   \n",
       "\n",
       "                  deaths_cum  population_millions  \n",
       "country                                            \n",
       "Bulgaria           48.240506             0.702422  \n",
       "France          13725.806818             6.698724  \n",
       "Germany          3542.250000             8.292792  \n",
       "Greece             87.081395             1.072767  \n",
       "Italy           17820.454545             6.043128  \n",
       "United_Kingdom  15067.931818             6.648899  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('country').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
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     "execution_count": 13,
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   "source": [
    "# define a function to plot number of cases and deaths (and their comulatives) over time for each country\n",
    "def country_plot(y):\n",
    "    my_tooltips = ['country', 'date', 'cases_cum', 'deaths_cum', 'deaths', 'cases']\n",
    "    return alt.Chart(df).mark_line(point=True).encode(\n",
    "        x='date', y = y, color='country',\n",
    "        tooltip = my_tooltips\n",
    "    ).properties(\n",
    "        width=1000,\n",
    "        height=300\n",
    "    )\n",
    "\n",
    "country_plot('cases')\n",
    "# Looks like there is a data quality issue. Why in the UK at 21-May-2020 there are -525 (negative) number of cases?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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   "execution_count": 15,
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66488991, \"continentExp\": \"Europe\", \"cases_cum\": 148377, \"deaths_cum\": 23635, \"population_millions\": 6.6488991}, {\"date\": \"2020-04-27T00:00:00\", \"cases\": 4463, \"deaths\": 420, \"country\": \"United_Kingdom\", \"code\": \"UK\", \"population\": 66488991, \"continentExp\": \"Europe\", \"cases_cum\": 152840, \"deaths_cum\": 24055, \"population_millions\": 6.6488991}, {\"date\": \"2020-04-28T00:00:00\", \"cases\": 4309, \"deaths\": 338, \"country\": \"United_Kingdom\", \"code\": \"UK\", \"population\": 66488991, \"continentExp\": \"Europe\", \"cases_cum\": 157149, \"deaths_cum\": 24393, \"population_millions\": 6.6488991}, {\"date\": \"2020-04-29T00:00:00\", \"cases\": 3996, \"deaths\": 909, \"country\": \"United_Kingdom\", \"code\": \"UK\", \"population\": 66488991, \"continentExp\": \"Europe\", \"cases_cum\": 161145, \"deaths_cum\": 25302, \"population_millions\": 6.6488991}, {\"date\": \"2020-04-30T00:00:00\", \"cases\": 4076, \"deaths\": 795, \"country\": \"United_Kingdom\", \"code\": \"UK\", \"population\": 66488991, \"continentExp\": \"Europe\", \"cases_cum\": 165221, \"deaths_cum\": 26097, \"population_millions\": 6.6488991}, {\"date\": \"2020-05-01T00:00:00\", \"cases\": 6032, \"deaths\": 674, \"country\": \"United_Kingdom\", \"code\": \"UK\", \"population\": 66488991, \"continentExp\": \"Europe\", \"cases_cum\": 171253, \"deaths_cum\": 26771, \"population_millions\": 6.6488991}, {\"date\": \"2020-05-02T00:00:00\", \"cases\": 6201, \"deaths\": 739, \"country\": \"United_Kingdom\", \"code\": \"UK\", \"population\": 66488991, \"continentExp\": \"Europe\", \"cases_cum\": 177454, \"deaths_cum\": 27510, \"population_millions\": 6.6488991}, {\"date\": \"2020-05-03T00:00:00\", \"cases\": 4806, \"deaths\": 621, \"country\": \"United_Kingdom\", \"code\": \"UK\", \"population\": 66488991, \"continentExp\": \"Europe\", \"cases_cum\": 182260, \"deaths_cum\": 28131, \"population_millions\": 6.6488991}, {\"date\": \"2020-05-04T00:00:00\", \"cases\": 4339, \"deaths\": 315, \"country\": \"United_Kingdom\", \"code\": \"UK\", \"population\": 66488991, \"continentExp\": \"Europe\", \"cases_cum\": 186599, \"deaths_cum\": 28446, \"population_millions\": 6.6488991}, {\"date\": \"2020-05-05T00:00:00\", \"cases\": 3985, \"deaths\": 288, \"country\": \"United_Kingdom\", \"code\": \"UK\", \"population\": 66488991, \"continentExp\": \"Europe\", \"cases_cum\": 190584, \"deaths_cum\": 28734, \"population_millions\": 6.6488991}, {\"date\": \"2020-05-06T00:00:00\", \"cases\": 4406, \"deaths\": 693, \"country\": \"United_Kingdom\", \"code\": \"UK\", \"population\": 66488991, \"continentExp\": \"Europe\", \"cases_cum\": 194990, \"deaths_cum\": 29427, \"population_millions\": 6.6488991}, {\"date\": \"2020-05-07T00:00:00\", \"cases\": 6211, \"deaths\": 649, \"country\": \"United_Kingdom\", \"code\": \"UK\", \"population\": 66488991, \"continentExp\": \"Europe\", \"cases_cum\": 201201, \"deaths_cum\": 30076, \"population_millions\": 6.6488991}, {\"date\": \"2020-05-08T00:00:00\", \"cases\": 5514, \"deaths\": 539, \"country\": \"United_Kingdom\", \"code\": \"UK\", \"population\": 66488991, \"continentExp\": \"Europe\", \"cases_cum\": 206715, \"deaths_cum\": 30615, \"population_millions\": 6.6488991}, {\"date\": \"2020-05-09T00:00:00\", \"cases\": 4649, \"deaths\": 626, \"country\": \"United_Kingdom\", \"code\": \"UK\", \"population\": 66488991, \"continentExp\": \"Europe\", \"cases_cum\": 211364, \"deaths_cum\": 31241, \"population_millions\": 6.6488991}, {\"date\": \"2020-05-10T00:00:00\", \"cases\": 3896, \"deaths\": 346, \"country\": \"United_Kingdom\", \"code\": \"UK\", \"population\": 66488991, \"continentExp\": \"Europe\", \"cases_cum\": 215260, \"deaths_cum\": 31587, \"population_millions\": 6.6488991}, {\"date\": \"2020-05-11T00:00:00\", \"cases\": 3923, \"deaths\": 268, \"country\": \"United_Kingdom\", \"code\": \"UK\", \"population\": 66488991, \"continentExp\": \"Europe\", \"cases_cum\": 219183, \"deaths_cum\": 31855, \"population_millions\": 6.6488991}, {\"date\": \"2020-05-12T00:00:00\", \"cases\": 3877, \"deaths\": 210, \"country\": \"United_Kingdom\", \"code\": \"UK\", \"population\": 66488991, \"continentExp\": \"Europe\", \"cases_cum\": 223060, \"deaths_cum\": 32065, \"population_millions\": 6.6488991}, {\"date\": \"2020-05-13T00:00:00\", \"cases\": 3403, \"deaths\": 627, \"country\": \"United_Kingdom\", \"code\": \"UK\", \"population\": 66488991, \"continentExp\": \"Europe\", \"cases_cum\": 226463, \"deaths_cum\": 32692, \"population_millions\": 6.6488991}, {\"date\": \"2020-05-14T00:00:00\", \"cases\": 3242, \"deaths\": 494, \"country\": \"United_Kingdom\", \"code\": \"UK\", \"population\": 66488991, \"continentExp\": \"Europe\", \"cases_cum\": 229705, \"deaths_cum\": 33186, \"population_millions\": 6.6488991}, {\"date\": \"2020-05-15T00:00:00\", \"cases\": 3446, \"deaths\": 428, \"country\": \"United_Kingdom\", \"code\": \"UK\", \"population\": 66488991, \"continentExp\": \"Europe\", \"cases_cum\": 233151, \"deaths_cum\": 33614, \"population_millions\": 6.6488991}, {\"date\": \"2020-05-16T00:00:00\", \"cases\": 3560, \"deaths\": 384, \"country\": \"United_Kingdom\", \"code\": \"UK\", \"population\": 66488991, \"continentExp\": \"Europe\", \"cases_cum\": 236711, \"deaths_cum\": 33998, \"population_millions\": 6.6488991}, {\"date\": \"2020-05-17T00:00:00\", \"cases\": 3450, \"deaths\": 468, \"country\": \"United_Kingdom\", \"code\": \"UK\", \"population\": 66488991, \"continentExp\": \"Europe\", \"cases_cum\": 240161, \"deaths_cum\": 34466, \"population_millions\": 6.6488991}, {\"date\": \"2020-05-18T00:00:00\", \"cases\": 3534, \"deaths\": 170, \"country\": \"United_Kingdom\", \"code\": \"UK\", \"population\": 66488991, \"continentExp\": \"Europe\", \"cases_cum\": 243695, \"deaths_cum\": 34636, \"population_millions\": 6.6488991}, {\"date\": \"2020-05-19T00:00:00\", \"cases\": 2711, \"deaths\": 160, \"country\": \"United_Kingdom\", \"code\": \"UK\", \"population\": 66488991, \"continentExp\": \"Europe\", \"cases_cum\": 246406, \"deaths_cum\": 34796, \"population_millions\": 6.6488991}, {\"date\": \"2020-05-20T00:00:00\", \"cases\": 2412, \"deaths\": 545, \"country\": \"United_Kingdom\", \"code\": \"UK\", \"population\": 66488991, \"continentExp\": \"Europe\", \"cases_cum\": 248818, \"deaths_cum\": 35341, \"population_millions\": 6.6488991}, {\"date\": \"2020-05-21T00:00:00\", \"cases\": -525, \"deaths\": 363, \"country\": \"United_Kingdom\", \"code\": \"UK\", \"population\": 66488991, \"continentExp\": \"Europe\", \"cases_cum\": 248293, \"deaths_cum\": 35704, \"population_millions\": 6.6488991}, {\"date\": \"2020-05-22T00:00:00\", \"cases\": 2615, \"deaths\": 338, \"country\": \"United_Kingdom\", \"code\": \"UK\", \"population\": 66488991, \"continentExp\": \"Europe\", \"cases_cum\": 250908, \"deaths_cum\": 36042, \"population_millions\": 6.6488991}, {\"date\": \"2020-05-23T00:00:00\", \"cases\": 3287, \"deaths\": 351, \"country\": \"United_Kingdom\", \"code\": \"UK\", \"population\": 66488991, \"continentExp\": \"Europe\", \"cases_cum\": 254195, \"deaths_cum\": 36393, \"population_millions\": 6.6488991}, {\"date\": \"2020-05-24T00:00:00\", \"cases\": 2959, \"deaths\": 282, \"country\": \"United_Kingdom\", \"code\": \"UK\", \"population\": 66488991, \"continentExp\": \"Europe\", \"cases_cum\": 257154, \"deaths_cum\": 36675, \"population_millions\": 6.6488991}, {\"date\": \"2020-05-25T00:00:00\", \"cases\": 2405, \"deaths\": 118, \"country\": \"United_Kingdom\", \"code\": \"UK\", \"population\": 66488991, \"continentExp\": \"Europe\", \"cases_cum\": 259559, \"deaths_cum\": 36793, \"population_millions\": 6.6488991}, {\"date\": \"2020-05-26T00:00:00\", \"cases\": 1625, \"deaths\": 121, \"country\": \"United_Kingdom\", \"code\": \"UK\", \"population\": 66488991, \"continentExp\": \"Europe\", \"cases_cum\": 261184, \"deaths_cum\": 36914, \"population_millions\": 6.6488991}, {\"date\": \"2020-05-27T00:00:00\", \"cases\": 4043, \"deaths\": 134, \"country\": \"United_Kingdom\", \"code\": \"UK\", \"population\": 66488991, \"continentExp\": \"Europe\", \"cases_cum\": 265227, \"deaths_cum\": 37048, \"population_millions\": 6.6488991}]}}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.Chart(...)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "country_plot('deaths_cum')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>date</th>\n",
       "      <th>cases</th>\n",
       "      <th>deaths</th>\n",
       "      <th>code</th>\n",
       "      <th>population</th>\n",
       "      <th>continentExp</th>\n",
       "      <th>cases_cum</th>\n",
       "      <th>deaths_cum</th>\n",
       "      <th>population_millions</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Bulgaria</th>\n",
       "      <td>2020-05-27</td>\n",
       "      <td>91</td>\n",
       "      <td>6</td>\n",
       "      <td>BG</td>\n",
       "      <td>7024216</td>\n",
       "      <td>Europe</td>\n",
       "      <td>2460</td>\n",
       "      <td>133</td>\n",
       "      <td>0.702422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>2020-05-27</td>\n",
       "      <td>7578</td>\n",
       "      <td>2004</td>\n",
       "      <td>FR</td>\n",
       "      <td>66987244</td>\n",
       "      <td>Europe</td>\n",
       "      <td>145555</td>\n",
       "      <td>28530</td>\n",
       "      <td>6.698724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>2020-05-27</td>\n",
       "      <td>6294</td>\n",
       "      <td>315</td>\n",
       "      <td>DE</td>\n",
       "      <td>82927922</td>\n",
       "      <td>Europe</td>\n",
       "      <td>179364</td>\n",
       "      <td>8349</td>\n",
       "      <td>8.292792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Greece</th>\n",
       "      <td>2020-05-27</td>\n",
       "      <td>156</td>\n",
       "      <td>9</td>\n",
       "      <td>EL</td>\n",
       "      <td>10727668</td>\n",
       "      <td>Europe</td>\n",
       "      <td>2892</td>\n",
       "      <td>173</td>\n",
       "      <td>1.072767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>2020-05-27</td>\n",
       "      <td>6557</td>\n",
       "      <td>971</td>\n",
       "      <td>IT</td>\n",
       "      <td>60431283</td>\n",
       "      <td>Europe</td>\n",
       "      <td>230555</td>\n",
       "      <td>32955</td>\n",
       "      <td>6.043128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United_Kingdom</th>\n",
       "      <td>2020-05-27</td>\n",
       "      <td>8719</td>\n",
       "      <td>1172</td>\n",
       "      <td>UK</td>\n",
       "      <td>66488991</td>\n",
       "      <td>Europe</td>\n",
       "      <td>265227</td>\n",
       "      <td>37048</td>\n",
       "      <td>6.648899</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     date  cases  deaths code  population continentExp  \\\n",
       "country                                                                  \n",
       "Bulgaria       2020-05-27     91       6   BG     7024216       Europe   \n",
       "France         2020-05-27   7578    2004   FR    66987244       Europe   \n",
       "Germany        2020-05-27   6294     315   DE    82927922       Europe   \n",
       "Greece         2020-05-27    156       9   EL    10727668       Europe   \n",
       "Italy          2020-05-27   6557     971   IT    60431283       Europe   \n",
       "United_Kingdom 2020-05-27   8719    1172   UK    66488991       Europe   \n",
       "\n",
       "                cases_cum  deaths_cum  population_millions  \n",
       "country                                                     \n",
       "Bulgaria             2460         133             0.702422  \n",
       "France             145555       28530             6.698724  \n",
       "Germany            179364        8349             8.292792  \n",
       "Greece               2892         173             1.072767  \n",
       "Italy              230555       32955             6.043128  \n",
       "United_Kingdom     265227       37048             6.648899  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('country').max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>date</th>\n",
       "      <th>cases</th>\n",
       "      <th>deaths</th>\n",
       "      <th>code</th>\n",
       "      <th>population</th>\n",
       "      <th>continentExp</th>\n",
       "      <th>cases_cum</th>\n",
       "      <th>deaths_cum</th>\n",
       "      <th>population_millions</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Bulgaria</th>\n",
       "      <td>2020-05-27</td>\n",
       "      <td>91</td>\n",
       "      <td>6</td>\n",
       "      <td>BG</td>\n",
       "      <td>7024216</td>\n",
       "      <td>Europe</td>\n",
       "      <td>2460</td>\n",
       "      <td>133</td>\n",
       "      <td>0.702422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>2020-05-27</td>\n",
       "      <td>7578</td>\n",
       "      <td>2004</td>\n",
       "      <td>FR</td>\n",
       "      <td>66987244</td>\n",
       "      <td>Europe</td>\n",
       "      <td>145555</td>\n",
       "      <td>28530</td>\n",
       "      <td>6.698724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>2020-05-27</td>\n",
       "      <td>6294</td>\n",
       "      <td>315</td>\n",
       "      <td>DE</td>\n",
       "      <td>82927922</td>\n",
       "      <td>Europe</td>\n",
       "      <td>179364</td>\n",
       "      <td>8349</td>\n",
       "      <td>8.292792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Greece</th>\n",
       "      <td>2020-05-27</td>\n",
       "      <td>156</td>\n",
       "      <td>9</td>\n",
       "      <td>EL</td>\n",
       "      <td>10727668</td>\n",
       "      <td>Europe</td>\n",
       "      <td>2892</td>\n",
       "      <td>173</td>\n",
       "      <td>1.072767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>2020-05-27</td>\n",
       "      <td>6557</td>\n",
       "      <td>971</td>\n",
       "      <td>IT</td>\n",
       "      <td>60431283</td>\n",
       "      <td>Europe</td>\n",
       "      <td>230555</td>\n",
       "      <td>32955</td>\n",
       "      <td>6.043128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United_Kingdom</th>\n",
       "      <td>2020-05-27</td>\n",
       "      <td>8719</td>\n",
       "      <td>1172</td>\n",
       "      <td>UK</td>\n",
       "      <td>66488991</td>\n",
       "      <td>Europe</td>\n",
       "      <td>265227</td>\n",
       "      <td>37048</td>\n",
       "      <td>6.648899</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     date  cases  deaths code  population continentExp  \\\n",
       "country                                                                  \n",
       "Bulgaria       2020-05-27     91       6   BG     7024216       Europe   \n",
       "France         2020-05-27   7578    2004   FR    66987244       Europe   \n",
       "Germany        2020-05-27   6294     315   DE    82927922       Europe   \n",
       "Greece         2020-05-27    156       9   EL    10727668       Europe   \n",
       "Italy          2020-05-27   6557     971   IT    60431283       Europe   \n",
       "United_Kingdom 2020-05-27   8719    1172   UK    66488991       Europe   \n",
       "\n",
       "                cases_cum  deaths_cum  population_millions  \n",
       "country                                                     \n",
       "Bulgaria             2460         133             0.702422  \n",
       "France             145555       28530             6.698724  \n",
       "Germany            179364        8349             8.292792  \n",
       "Greece               2892         173             1.072767  \n",
       "Italy              230555       32955             6.043128  \n",
       "United_Kingdom     265227       37048             6.648899  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_last_date = df.groupby('country').max()\n",
    "df_last_date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# percetage of cumulative cases and cumulative deaths from the population\n",
    "df_last_date.loc[:,'cases_cum_percentage'] = df_last_date['cases_cum']/df_last_date['population'] * 100\n",
    "df_last_date.loc[:,'deaths_cum_percentage'] = df_last_date['deaths_cum']/df_last_date['population'] * 100\n",
    "# percetage of cumulative deaths from the cumulative cases\n",
    "df_last_date.loc[:,'deaths_cum_percentage_cases'] = df_last_date['deaths_cum']/df_last_date['cases_cum'] * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>cases</th>\n",
       "      <th>deaths</th>\n",
       "      <th>code</th>\n",
       "      <th>population</th>\n",
       "      <th>continentExp</th>\n",
       "      <th>cases_cum</th>\n",
       "      <th>deaths_cum</th>\n",
       "      <th>population_millions</th>\n",
       "      <th>cases_cum_percentage</th>\n",
       "      <th>deaths_cum_percentage</th>\n",
       "      <th>deaths_cum_percentage_cases</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Bulgaria</th>\n",
       "      <td>2020-05-27</td>\n",
       "      <td>91</td>\n",
       "      <td>6</td>\n",
       "      <td>BG</td>\n",
       "      <td>7024216</td>\n",
       "      <td>Europe</td>\n",
       "      <td>2460</td>\n",
       "      <td>133</td>\n",
       "      <td>0.702422</td>\n",
       "      <td>0.035022</td>\n",
       "      <td>0.001893</td>\n",
       "      <td>5.406504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>2020-05-27</td>\n",
       "      <td>7578</td>\n",
       "      <td>2004</td>\n",
       "      <td>FR</td>\n",
       "      <td>66987244</td>\n",
       "      <td>Europe</td>\n",
       "      <td>145555</td>\n",
       "      <td>28530</td>\n",
       "      <td>6.698724</td>\n",
       "      <td>0.217288</td>\n",
       "      <td>0.042590</td>\n",
       "      <td>19.600838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>2020-05-27</td>\n",
       "      <td>6294</td>\n",
       "      <td>315</td>\n",
       "      <td>DE</td>\n",
       "      <td>82927922</td>\n",
       "      <td>Europe</td>\n",
       "      <td>179364</td>\n",
       "      <td>8349</td>\n",
       "      <td>8.292792</td>\n",
       "      <td>0.216289</td>\n",
       "      <td>0.010068</td>\n",
       "      <td>4.654780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Greece</th>\n",
       "      <td>2020-05-27</td>\n",
       "      <td>156</td>\n",
       "      <td>9</td>\n",
       "      <td>EL</td>\n",
       "      <td>10727668</td>\n",
       "      <td>Europe</td>\n",
       "      <td>2892</td>\n",
       "      <td>173</td>\n",
       "      <td>1.072767</td>\n",
       "      <td>0.026958</td>\n",
       "      <td>0.001613</td>\n",
       "      <td>5.982019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>2020-05-27</td>\n",
       "      <td>6557</td>\n",
       "      <td>971</td>\n",
       "      <td>IT</td>\n",
       "      <td>60431283</td>\n",
       "      <td>Europe</td>\n",
       "      <td>230555</td>\n",
       "      <td>32955</td>\n",
       "      <td>6.043128</td>\n",
       "      <td>0.381516</td>\n",
       "      <td>0.054533</td>\n",
       "      <td>14.293769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United_Kingdom</th>\n",
       "      <td>2020-05-27</td>\n",
       "      <td>8719</td>\n",
       "      <td>1172</td>\n",
       "      <td>UK</td>\n",
       "      <td>66488991</td>\n",
       "      <td>Europe</td>\n",
       "      <td>265227</td>\n",
       "      <td>37048</td>\n",
       "      <td>6.648899</td>\n",
       "      <td>0.398904</td>\n",
       "      <td>0.055721</td>\n",
       "      <td>13.968412</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     date  cases  deaths code  population continentExp  \\\n",
       "country                                                                  \n",
       "Bulgaria       2020-05-27     91       6   BG     7024216       Europe   \n",
       "France         2020-05-27   7578    2004   FR    66987244       Europe   \n",
       "Germany        2020-05-27   6294     315   DE    82927922       Europe   \n",
       "Greece         2020-05-27    156       9   EL    10727668       Europe   \n",
       "Italy          2020-05-27   6557     971   IT    60431283       Europe   \n",
       "United_Kingdom 2020-05-27   8719    1172   UK    66488991       Europe   \n",
       "\n",
       "                cases_cum  deaths_cum  population_millions  \\\n",
       "country                                                      \n",
       "Bulgaria             2460         133             0.702422   \n",
       "France             145555       28530             6.698724   \n",
       "Germany            179364        8349             8.292792   \n",
       "Greece               2892         173             1.072767   \n",
       "Italy              230555       32955             6.043128   \n",
       "United_Kingdom     265227       37048             6.648899   \n",
       "\n",
       "                cases_cum_percentage  deaths_cum_percentage  \\\n",
       "country                                                       \n",
       "Bulgaria                    0.035022               0.001893   \n",
       "France                      0.217288               0.042590   \n",
       "Germany                     0.216289               0.010068   \n",
       "Greece                      0.026958               0.001613   \n",
       "Italy                       0.381516               0.054533   \n",
       "United_Kingdom              0.398904               0.055721   \n",
       "\n",
       "                deaths_cum_percentage_cases  \n",
       "country                                      \n",
       "Bulgaria                           5.406504  \n",
       "France                            19.600838  \n",
       "Germany                            4.654780  \n",
       "Greece                             5.982019  \n",
       "Italy                             14.293769  \n",
       "United_Kingdom                    13.968412  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df_last_date"
   ]
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   "execution_count": 21,
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    "my_tooltips = ['country','cases_cum_percentage', 'deaths_cum_percentage', 'deaths_cum_percentage_cases' ]\n",
    "\n",
    "alt.Chart(df_last_date.reset_index()).transform_fold(\n",
    "    fold=['cases_cum_percentage','deaths_cum_percentage']).mark_bar().encode(\n",
    "    y='country:N',\n",
    "    x='value:Q',\n",
    "    color='key:N',\n",
    "    tooltip = my_tooltips).properties(\n",
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    "        height=300\n",
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   "execution_count": 22,
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       "alt.Chart(...)"
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     },
     "execution_count": 22,
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   ],
   "source": [
    "alt.Chart(df_last_date.reset_index()).mark_bar().encode(\n",
    "    y='country:N',\n",
    "    x='deaths_cum_percentage_cases:Q',\n",
    "    tooltip = my_tooltips).properties(\n",
    "        width=1000,\n",
    "        height=300\n",
    "    )"
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  }
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