def get_mapping():
""" Returns mapping between country names (keys) and ISO codes (values).
To be used for geo charts.
"""
df = pycovid.getCovidCases()
countries = df['name'].unique()
# Ugly, could be faster
mapping = {country: df.query("name == @country")['alpha-3'].unique()[0]
for country in countries}
return mapping
def get_data():
""" Download the data and return it as a 'wide' data frame
"""
df = pycovid.getCovidCases()
df.rename(columns={"alpha-3": "iso"}, inplace=True)
# The number of reported cases per day, country, and type
df_day = df.groupby(['country_region', 'iso', 'date', 'type']).sum()
# %%
# The cumulative sum
df_sum = df_day.groupby(['country_region', 'iso', 'type']
).transform(lambda x: x.cumsum())['cases']
df_sum = df_sum.reset_index()
# %%
# Switch to wide format (time series)
data = df_sum.pivot_table(values='cases',
columns=['type', 'iso', 'country_region'],
index=['date'])
data = data.fillna(method='ffill')
return data
def test_get_covid_cases_uk():
from pycovid import pycovid
cases = pycovid.getCovidCases(countries=['United Kingdom'])
assert len(cases)