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 10:25:02 CEST
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
overview(country="Germany", subregion="SK Stuttgart", weeks=5);
overview(country="Germany", subregion="SK Stuttgart");
compare_plot(country="Germany", subregion="SK Stuttgart", dates="2020-03-15:");
# load the data
cases, deaths = germany_get_region(landkreis="SK Stuttgart")
# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="SK Stuttgart")
print(f'Population of country="Germany", subregion="SK Stuttgart": {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 Stuttgart": 626275 people
total cases | daily new cases | total deaths | daily new deaths | |
---|---|---|---|---|
date | ||||
2023-03-06 | 253318 | 57 | 775 | 1 |
2023-03-03 | 253261 | 21 | 774 | 1 |
2023-03-02 | 253240 | 27 | 773 | 0 |
2023-03-01 | 253213 | 39 | 773 | 0 |
2023-02-28 | 253174 | 139 | 773 | 1 |
... | ... | ... | ... | ... |
2020-03-10 | 30 | 12 | 0 | 0 |
2020-03-09 | 18 | 8 | 0 | 0 |
2020-03-07 | 10 | 5 | 0 | 0 |
2020-03-06 | 5 | 2 | 0 | 0 |
2020-03-05 | 3 | 2 | 0 | 0 |
1052 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: 0:02:32.021152