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 09:36:54 CEST
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
overview("Montenegro", weeks=5);
overview("Montenegro");
compare_plot("Montenegro", normalise=True);
# load the data
cases, deaths = get_country_data("Montenegro")
# get population of the region for future normalisation:
inhabitants = population("Montenegro")
print(f'Population of "Montenegro": {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 "Montenegro": 628062 people
total cases | daily new cases | total deaths | daily new deaths | |
---|---|---|---|---|
2023-03-06 | 288630 | 78 | 2807 | 2 |
2023-03-05 | 288552 | 0 | 2805 | 0 |
2023-03-04 | 288552 | 62 | 2805 | 0 |
2023-03-03 | 288490 | 67 | 2805 | 1 |
2023-03-02 | 288423 | 62 | 2804 | 0 |
... | ... | ... | ... | ... |
2020-01-27 | 0 | 0 | 0 | 0 |
2020-01-26 | 0 | 0 | 0 | 0 |
2020-01-25 | 0 | 0 | 0 | 0 |
2020-01-24 | 0 | 0 | 0 | 0 |
2020-01-23 | 0 | 0 | 0 | 0 |
1139 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: 0:00:17.259093