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
53599	Italy	2021-09-10	80587942.0	4596558.0	86852737.0	4338241.0	129828.0	60421760	7607.454665	214.869610	2.824461	143744.136218	1.333757	5617.0	9.296320
53600	Italy	2021-09-11	80791152.0	4601749.0	87186478.0	4344238.0	129885.0	60421760	7616.045941	214.963947	2.822514	144296.488550	1.337120	5191.0	8.591276
53601	Italy	2021-09-12	80922217.0	4606413.0	87453836.0	4349160.0	129919.0	60421760	7623.765014	215.020218	2.820394	144738.974833	1.339289	4664.0	7.719073
53602	Italy	2021-09-13	81176739.0	4609205.0	87573881.0	4353346.0	129955.0	60421760	7628.385866	215.079799	2.819467	144937.653256	1.343502	2792.0	4.620852
53603	Italy	2021-09-14	81409122.0	4613214.0	87892474.0	4360847.0	130027.0	60421760	7635.020893	215.198961	2.818577	145464.935149	1.347348	4009.0	6.635027