Importing and preparing the data

We will be looking at data from the following countries:

Italy
Austria
Germany
Belgium
France
United Kingdom

We begin by importing the data, and adding calculating some new features so that we can compare the data from different countries. For example we calculate 'confirmed cases per 100k population', 'deaths per 100k' and 'new cases' since these are not initially in the dataset.

from covid19dh import covid19
import altair as alt
import datetime

countries = ["Italy", 
             "Austria",
             "Germany",
             "Belgium",
             "France",
             "United Kingdom",
             "Switzerland"
            ]

yesterday = datetime.date.today() - datetime.timedelta(days=1)

x, src = covid19(countries, raw=True, verbose=False, end=yesterday, cache=False)

x_small = x.loc[:, ['administrative_area_level_1', 'date', 'vaccines', 'confirmed','tests', 'recovered', 'deaths', 'population']]
x_small.rename(columns={'administrative_area_level_1': 'id'}, inplace=True)

x_small['confirmed_per'] = 100000 * x_small['confirmed'] / x_small['population']
x_small['deaths_per'] = 100000 * x_small['deaths'] / x_small['population']
x_small['ratio'] = 100 * (x_small['deaths']) / (x_small['confirmed'])
x_small['tests_per'] = 100000 * (x_small['tests']) / (x_small['population'])
x_small['vaccines_per'] = x_small['vaccines'] / x_small['population']

x_small['new_cases']=x_small.groupby('id').confirmed.diff().fillna(0)
x_small['new_cases_per']=x_small.groupby('id').confirmed_per.diff().fillna(0)

Here is a random sample of 5 rows from the dataset.

x_small.tail()

Plotting the data

We will first look at the total numbers of cases and deaths in each country, before moving on to cases and deaths per 100k population.

In each of the charts below, you can click on the legend to filter the lines shown

Total cases per 100,000

leg_selection = alt.selection_multi(fields=['id'], bind='legend')

alt.Chart(x_small).mark_line().encode(
    x=alt.X("yearmonthdate(date):T", axis=alt.Axis(title='Date')),
    y=alt.Y("confirmed_per:Q", axis=alt.Axis(title='Confirmed per 100k')),
    tooltip=['id', 'confirmed_per'],
    color=alt.Color('id', legend=alt.Legend(title="Countries")),
    opacity=alt.condition(leg_selection, alt.value(1), alt.value(0.2))
).add_selection(leg_selection).properties(title='Total number of cases per 100,000 population for selected European Countries', width=600).interactive()

	id	date	vaccines	confirmed	tests	recovered	deaths	population	confirmed_per	deaths_per	ratio	tests_per	vaccines_per	new_cases	new_cases_per
53866	Italy	2021-09-13	81189832.0	4609205.0	87573881.0	4353346.0	129955.0	60421760	7628.385866	215.079799	2.819467	144937.653256	1.343718	2792.0	4.620852
53867	Italy	2021-09-14	81427413.0	4613214.0	87892474.0	4360847.0	130027.0	60421760	7635.020893	215.198961	2.818577	145464.935149	1.347650	4009.0	6.635027
53868	Italy	2021-09-15	81649249.0	4618040.0	88210140.0	4369453.0	130100.0	60421760	7643.008082	215.319779	2.817212	145990.682827	1.351322	4826.0	7.987189
53869	Italy	2021-09-16	81861700.0	4623155.0	88516407.0	4376646.0	130167.0	60421760	7651.473575	215.430666	2.815545	146497.564785	1.354838	5115.0	8.465493
53870	Italy	2021-09-17	82070636.0	4627699.0	88800986.0	4383195.0	130233.0	60421760	7658.994045	215.539898	2.814206	146968.552389	1.358296	4544.0	7.520469