Germany: LK Unna (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 14:09:41 CEST

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

overview(country="Germany", subregion="LK Unna", weeks=5);

overview(country="Germany", subregion="LK Unna");

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

# load the data
cases, deaths = germany_get_region(landkreis="LK Unna")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Unna")
print(f'Population of country="Germany", subregion="LK Unna": {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 Unna": 393063 people

	total cases	daily new cases	total deaths	daily new deaths
date
2023-03-06	167915	57	757	0
2023-03-03	167858	32	757	0
2023-03-02	167826	37	757	0
2023-03-01	167789	79	757	0
2023-02-28	167710	137	757	0
2023-02-27	167573	125	757	0
2023-02-24	167448	70	757	0
2023-02-23	167378	83	757	1
2023-02-22	167295	94	756	0
2023-02-21	167201	94	756	0
2023-02-20	167107	140	756	1
2023-02-17	166967	70	755	0
2023-02-16	166897	79	755	0
2023-02-15	166818	75	755	0
2023-02-14	166743	112	755	0
2023-02-13	166631	123	755	0
2023-02-10	166508	37	755	0
2023-02-09	166471	71	755	0
2023-02-08	166400	65	755	0
2023-02-07	166335	87	755	0
2023-02-06	166248	107	755	1
2023-02-03	166141	54	754	0
2023-02-02	166087	69	754	0
2023-02-01	166018	71	754	0
2023-01-31	165947	118	754	0
2023-01-30	165829	96	754	0
2023-01-27	165733	51	754	0
2023-01-26	165682	84	754	0
2023-01-25	165598	103	754	0
2023-01-24	165495	85	754	0
2023-01-23	165410	119	754	0
2023-01-20	165291	30	754	0
2023-01-19	165261	59	754	0
2023-01-18	165202	67	754	0
2023-01-17	165135	108	754	0
2023-01-16	165027	103	754	1
2023-01-13	164924	58	753	0
2023-01-12	164866	96	753	0
2023-01-11	164770	111	753	0
2023-01-10	164659	163	753	1
2023-01-09	164496	213	752	1
2023-01-06	164283	83	751	0
2023-01-05	164200	155	751	1
2023-01-04	164045	149	750	0
2023-01-03	163896	171	750	1
2023-01-02	163725	316	749	1
2022-12-31	163409	1	748	0
2022-12-30	163408	172	748	0
2022-12-29	163236	239	748	0
2022-12-28	162997	234	748	2
2022-12-27	162763	405	746	2
2022-12-23	162358	220	744	0
2022-12-22	162138	248	744	0
2022-12-21	161890	315	744	1
2022-12-20	161575	384	743	0
2022-12-19	161191	437	743	2
2022-12-16	160754	225	741	1
2022-12-15	160529	282	740	0
2022-12-14	160247	213	740	0
2022-12-13	160034	286	740	0
2022-12-12	159748	387	740	0
2022-12-09	159361	205	740	2
2022-12-08	159156	185	738	1
2022-12-07	158971	205	737	0
2022-12-06	158766	253	737	0
2022-12-05	158513	282	737	1
2022-12-02	158231	167	736	2
2022-12-01	158064	127	734	0
2022-11-30	157937	188	734	1
2022-11-29	157749	257	733	0
2022-11-28	157492	279	733	0
2022-11-25	157213	126	733	0
2022-11-24	157087	170	733	0
2022-11-23	156917	209	733	0
2022-11-22	156708	238	733	1
2022-11-21	156470	318	732	0
2022-11-19	156152	1	732	0
2022-11-18	156151	139	732	1
2022-11-17	156012	182	731	0
2022-11-16	155830	171	731	1
2022-11-15	155659	275	730	0
2022-11-14	155384	345	730	0
2022-11-13	155039	2	730	0
2022-11-12	155037	3	730	0
2022-11-11	155034	188	730	1
2022-11-10	154846	262	729	1
2022-11-09	154584	261	728	0
2022-11-08	154323	371	728	0
2022-11-07	153952	411	728	0
2022-11-05	153541	3	728	0
2022-11-04	153538	336	728	0
2022-11-03	153202	440	728	0
2022-11-02	152762	564	728	1
2022-11-01	152198	2	727	0
2022-10-31	152196	508	727	0
2022-10-30	151688	1	727	0
2022-10-29	151687	3	727	0
2022-10-28	151684	313	727	0
2022-10-27	151371	512	727	0
2022-10-26	150859	395	727	1
2022-10-25	150464	568	726	0
2022-10-24	149896	706	726	0
2022-10-21	149190	410	726	0
2022-10-20	148780	545	726	1
2022-10-19	148235	836	725	0
2022-10-18	147399	708	725	0
2022-10-17	146691	695	725	1
2022-10-16	145996	1	724	0
2022-10-14	145995	384	724	0
2022-10-13	145611	587	724	0
2022-10-12	145024	709	724	1
2022-10-11	144315	522	723	1
2022-10-10	143793	429	722	0
2022-10-09	143364	5	722	0
2022-10-07	143359	452	722	0
2022-10-06	142907	615	722	1
2022-10-05	142292	582	721	0
2022-10-04	141710	582	721	3
2022-10-02	141128	1	718	0
2022-09-30	141127	259	718	1
2022-09-29	140868	324	717	1
2022-09-28	140544	435	716	1
2022-09-27	140109	423	715	1
2022-09-26	139686	310	714	0
2022-09-24	139376	2	714	0
2022-09-23	139374	250	714	0
2022-09-22	139124	226	714	0
2022-09-21	138898	239	714	0
2022-09-20	138659	267	714	0
2022-09-19	138392	224	714	1
2022-09-16	138168	185	713	0
2022-09-15	137983	133	713	0
2022-09-14	137850	229	713	0
2022-09-13	137621	238	713	1
2022-09-12	137383	263	712	0
2022-09-09	137120	147	712	0
2022-09-08	136973	166	712	0
2022-09-07	136807	211	712	0
2022-09-06	136596	259	712	0
2022-09-05	136337	274	712	1
2022-09-02	136063	177	711	0
2022-09-01	135886	234	711	1
2022-08-31	135652	266	710	0
2022-08-30	135386	276	710	0
2022-08-29	135110	395	710	2
2022-08-28	134715	5	708	0
2022-08-26	134710	180	708	0
2022-08-25	134530	283	708	1
2022-08-24	134247	317	707	0
2022-08-23	133930	296	707	1
2022-08-22	133634	387	706	1
2022-08-19	133247	275	705	0
2022-08-18	132972	275	705	1
2022-08-17	132697	320	704	1
2022-08-16	132377	394	703	1
2022-08-15	131983	423	702	1
2022-08-12	131560	260	701	0
2022-08-11	131300	313	701	1
2022-08-10	130987	373	700	1
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2022-08-04	129517	324	697	0
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2022-07-29	128029	555	697	1
2022-07-28	127474	45	696	0
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2022-07-26	127086	418	696	0
2022-07-25	126668	624	696	1
2022-07-24	126044	1	695	0
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2020-03-21	65	13	1	1
2020-03-20	52	2	0	0
2020-03-19	50	8	0	0
2020-03-18	42	12	0	0
2020-03-17	30	12	0	0
2020-03-16	18	3	0	0
2020-03-12	15	5	0	0
2020-03-11	10	4	0	0
2020-03-10	6	2	0	0
2020-03-07	4	3	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: 0:03:07.700002