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 12:52:04 CEST
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
overview(country="Germany", subregion="SK Frankfurt am Main", weeks=5);
overview(country="Germany", subregion="SK Frankfurt am Main");
compare_plot(country="Germany", subregion="SK Frankfurt am Main", dates="2020-03-15:");
# load the data
cases, deaths = germany_get_region(landkreis="SK Frankfurt am Main")
# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="SK Frankfurt am Main")
print(f'Population of country="Germany", subregion="SK Frankfurt am Main": {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 Frankfurt am Main": 759224 people
total cases | daily new cases | total deaths | daily new deaths | |
---|---|---|---|---|
date | ||||
2023-03-06 | 334012 | 36 | 1595 | 0 |
2023-03-03 | 333976 | 72 | 1595 | 0 |
2023-03-02 | 333904 | 67 | 1595 | 0 |
2023-03-01 | 333837 | 152 | 1595 | 0 |
2023-02-28 | 333685 | 223 | 1595 | 0 |
... | ... | ... | ... | ... |
2020-03-11 | 13 | 3 | 0 | 0 |
2020-03-10 | 10 | 3 | 0 | 0 |
2020-03-09 | 7 | 1 | 0 | 0 |
2020-03-05 | 6 | 2 | 0 | 0 |
2020-03-04 | 4 | 1 | 0 | 0 |
1029 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: 0:03:10.922059