Germany: SK Pforzheim (Baden-Württemberg)

• 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 10:25:58 CEST

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

overview(country="Germany", subregion="SK Pforzheim", weeks=5);

overview(country="Germany", subregion="SK Pforzheim");

compare_plot(country="Germany", subregion="SK Pforzheim", dates="2020-03-15:");

# load the data
cases, deaths = germany_get_region(landkreis="SK Pforzheim")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="SK Pforzheim")
print(f'Population of country="Germany", subregion="SK Pforzheim": {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 Pforzheim": 125529 people

	total cases	daily new cases	total deaths	daily new deaths
date
2023-03-06	59389	5	347	0
2023-03-03	59384	4	347	0
2023-03-02	59380	7	347	0
2023-03-01	59373	9	347	0
2023-02-28	59364	24	347	0
2023-02-27	59340	56	347	0
2023-02-24	59284	12	347	0
2023-02-23	59272	13	347	0
2023-02-22	59259	18	347	0
2023-02-21	59241	31	347	0
2023-02-20	59210	35	347	0
2023-02-17	59175	19	347	0
2023-02-16	59156	19	347	0
2023-02-15	59137	25	347	0
2023-02-14	59112	18	347	0
2023-02-13	59094	33	347	0
2023-02-10	59061	7	347	0
2023-02-09	59054	9	347	0
2023-02-08	59045	18	347	0
2023-02-07	59027	12	347	0
2023-02-06	59015	12	347	0
2023-02-05	59003	4	347	0
2023-02-03	58999	13	347	0
2023-02-02	58986	8	347	0
2023-02-01	58978	20	347	0
2023-01-31	58958	11	347	0
2023-01-30	58947	27	347	0
2023-01-27	58920	3	347	0
2023-01-26	58917	11	347	0
2023-01-25	58906	6	347	0
2023-01-24	58900	10	347	0
2023-01-23	58890	2	347	0
2023-01-22	58888	8	347	0
2023-01-20	58880	5	347	0
2023-01-19	58875	6	347	0
2023-01-18	58869	5	347	0
2023-01-17	58864	4	347	0
2023-01-16	58860	28	347	0
2023-01-13	58832	17	347	1
2023-01-12	58815	10	346	0
2023-01-11	58805	15	346	0
2023-01-10	58790	22	346	0
2023-01-09	58768	47	346	0
2023-01-07	58721	1	346	0
2023-01-05	58720	19	346	1
2023-01-04	58701	19	345	0
2023-01-03	58682	20	345	0
2023-01-02	58662	47	345	1
2022-12-31	58615	14	344	0
2022-12-30	58601	28	344	1
2022-12-29	58573	26	343	0
2022-12-28	58547	72	343	0
2022-12-27	58475	50	343	0
2022-12-24	58425	1	343	0
2022-12-23	58424	29	343	0
2022-12-22	58395	28	343	0
2022-12-21	58367	56	343	0
2022-12-20	58311	55	343	0
2022-12-19	58256	90	343	0
2022-12-16	58166	34	343	0
2022-12-15	58132	33	343	0
2022-12-14	58099	38	343	0
2022-12-13	58061	56	343	0
2022-12-12	58005	62	343	0
2022-12-09	57943	35	343	0
2022-12-08	57908	34	343	0
2022-12-07	57874	19	343	0
2022-12-06	57855	60	343	1
2022-12-05	57795	63	342	0
2022-12-02	57732	29	342	0
2022-12-01	57703	42	342	0
2022-11-30	57661	42	342	0
2022-11-29	57619	37	342	0
2022-11-28	57582	54	342	0
2022-11-25	57528	29	342	0
2022-11-24	57499	41	342	0
2022-11-23	57458	33	342	0
2022-11-22	57425	48	342	0
2022-11-21	57377	67	342	0
2022-11-18	57310	16	342	0
2022-11-17	57294	30	342	0
2022-11-16	57264	37	342	0
2022-11-15	57227	47	342	0
2022-11-14	57180	83	342	0
2022-11-13	57097	1	342	0
2022-11-11	57096	27	342	0
2022-11-10	57069	37	342	0
2022-11-09	57032	49	342	0
2022-11-08	56983	71	342	0
2022-11-07	56912	84	342	0
2022-11-05	56828	1	342	0
2022-11-04	56827	50	342	0
2022-11-03	56777	91	342	0
2022-11-02	56686	165	342	0
2022-10-31	56521	21	342	0
2022-10-28	56500	48	342	0
2022-10-27	56452	23	342	0
2022-10-26	56429	53	342	0
2022-10-25	56376	101	342	0
2022-10-24	56275	132	342	0
2022-10-23	56143	11	342	0
2022-10-22	56132	36	342	0
2022-10-21	56096	83	342	0
2022-10-20	56013	164	342	0
2022-10-19	55849	158	342	0
2022-10-18	55691	75	342	1
2022-10-17	55616	135	341	1
2022-10-16	55481	13	340	0
2022-10-15	55468	14	340	0
2022-10-14	55454	287	340	1
2022-10-13	55167	317	339	0
2022-10-12	54850	169	339	0
2022-10-11	54681	137	339	2
2022-10-10	54544	67	337	0
2022-10-09	54477	10	337	0
2022-10-08	54467	17	337	0
2022-10-07	54450	46	337	0
2022-10-06	54404	136	337	0
2022-10-05	54268	173	337	0
2022-10-04	54095	196	337	0
2022-10-03	53899	19	337	0
2022-10-02	53880	39	337	0
2022-10-01	53841	58	337	0
2022-09-30	53783	91	337	0
2022-09-29	53692	109	337	0
2022-09-28	53583	105	337	0
2022-09-27	53478	153	337	0
2022-09-26	53325	135	337	1
2022-09-25	53190	12	336	0
2022-09-24	53178	21	336	0
2022-09-23	53157	60	336	0
2022-09-22	53097	57	336	0
2022-09-21	53040	46	336	0
2022-09-20	52994	47	336	0
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2020-03-17	7	1	0	0
2020-03-16	6	2	0	0
2020-03-12	4	1	0	0

Explore the data in your web browser

• If you want to execute this notebook, click here to use myBinder
• and wait (~1 to 2 minutes)
• Then press SHIFT+RETURN to advance code cell to code cell
• See http://jupyter.org for more details on how to use Jupyter Notebook

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: 0:03:25.649223