import datetime
import time
start = datetime.datetime.now()
print(f"Notebook executed on: {start.strftime('%d/%m/%Y %H:%M:%S%Z')} {time.tzname[time.daylight]}")
Notebook executed on: 07/03/2023 10:26:08 CEST
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
overview(country="Germany", subregion="SK Mannheim", weeks=5);
overview(country="Germany", subregion="SK Mannheim");
compare_plot(country="Germany", subregion="SK Mannheim", dates="2020-03-15:");
# load the data
cases, deaths = germany_get_region(landkreis="SK Mannheim")
# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="SK Mannheim")
print(f'Population of country="Germany", subregion="SK Mannheim": {inhabitants} people')
# compose into one table
table = compose_dataframe_summary(cases, deaths)
# show tables with up to 1000 rows
pd.set_option("display.max_rows", 1000)
# display the table
table
Population of country="Germany", subregion="SK Mannheim": 311831 people
total cases | daily new cases | total deaths | daily new deaths | |
---|---|---|---|---|
date | ||||
2023-03-06 | 135900 | 9 | 549 | 0 |
2023-03-05 | 135891 | 13 | 549 | 0 |
2023-03-04 | 135878 | 28 | 549 | 0 |
2023-03-03 | 135850 | 32 | 549 | 0 |
2023-03-02 | 135818 | 47 | 549 | 0 |
... | ... | ... | ... | ... |
2020-03-10 | 11 | 1 | 0 | 0 |
2020-03-09 | 10 | 4 | 0 | 0 |
2020-03-06 | 6 | 1 | 0 | 0 |
2020-03-05 | 5 | 1 | 0 | 0 |
2020-03-04 | 4 | 2 | 0 | 0 |
1027 rows × 4 columns
print(f"Download of data from Johns Hopkins university: cases at {fetch_cases_last_execution()} and "
f"deaths at {fetch_deaths_last_execution()}.")
Download of data from Johns Hopkins university: cases at 07/03/2023 09:31:22 and deaths at 07/03/2023 09:31:21.
# to force a fresh download of data, run "clear_cache()"
print(f"Notebook execution took: {datetime.datetime.now()-start}")
Notebook execution took: 0:03:15.648020