Germany: LK Rhein-Erft-Kreis (Nordrhein-Westfalen)

• Homepage of project: https://oscovida.github.io
• Plots are explained at http://oscovida.github.io/plots.html
• Execute this Jupyter Notebook using myBinder

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 14:02:23 CEST

%config InlineBackend.figure_formats = ['svg']
from oscovida import *

overview(country="Germany", subregion="LK Rhein-Erft-Kreis", weeks=5);

overview(country="Germany", subregion="LK Rhein-Erft-Kreis");

compare_plot(country="Germany", subregion="LK Rhein-Erft-Kreis", dates="2020-03-15:");

# load the data
cases, deaths = germany_get_region(landkreis="LK Rhein-Erft-Kreis")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Rhein-Erft-Kreis")
print(f'Population of country="Germany", subregion="LK Rhein-Erft-Kreis": {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="LK Rhein-Erft-Kreis": 471891 people

	total cases	daily new cases	total deaths	daily new deaths
date
2023-03-06	232271	9	704	0
2023-03-05	232262	18	704	0
2023-03-04	232244	23	704	0
2023-03-03	232221	95	704	1
2023-03-02	232126	67	703	0
...	...	...	...	...
2020-03-14	21	2	0	0
2020-03-13	19	9	0	0
2020-03-12	10	4	0	0
2020-03-11	6	2	0	0
2020-03-10	4	3	0	0

1038 rows × 4 columns

Explore the data in your web browser

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• and wait (~1 to 2 minutes)
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Acknowledgements:

• Johns Hopkins University provides data for countries
• Robert Koch Institute provides data for within Germany
• Atlo Team for gathering and providing data from Hungary (https://atlo.team/koronamonitor/)
• Open source and scientific computing community for the data tools
• Github for hosting repository and html files
• Project Jupyter for the Notebook and binder service
• The H2020 project Photon and Neutron Open Science Cloud (PaNOSC)

---

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: 0:02:38.441101