Germany: SK Wiesbaden (Hessen)

• 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 12:52:16 CEST

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

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

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

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

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

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="SK Wiesbaden")
print(f'Population of country="Germany", subregion="SK Wiesbaden": {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 Wiesbaden": 278950 people

	total cases	daily new cases	total deaths	daily new deaths
date
2023-03-06	134152	34	547	0
2023-03-03	134118	18	547	0
2023-03-02	134100	27	547	0
2023-03-01	134073	57	547	1
2023-02-28	134016	95	546	0
2023-02-27	133921	157	546	1
2023-02-24	133764	60	545	1
2023-02-23	133704	87	544	1
2023-02-22	133617	72	543	0
2023-02-21	133545	71	543	0
2023-02-20	133474	106	543	1
2023-02-17	133368	63	542	0
2023-02-16	133305	54	542	0
2023-02-15	133251	79	542	0
2023-02-14	133172	76	542	1
2023-02-13	133096	109	541	0
2023-02-10	132987	60	541	0
2023-02-09	132927	56	541	0
2023-02-08	132871	97	541	2
2023-02-07	132774	88	539	0
2023-02-06	132686	147	539	1
2023-02-03	132539	89	538	0
2023-02-02	132450	72	538	0
2023-02-01	132378	61	538	1
2023-01-31	132317	105	537	0
2023-01-30	132212	90	537	2
2023-01-27	132122	74	535	0
2023-01-26	132048	50	535	0
2023-01-25	131998	76	535	0
2023-01-24	131922	53	535	0
2023-01-23	131869	81	535	0
2023-01-20	131788	48	535	0
2023-01-19	131740	50	535	1
2023-01-18	131690	83	534	0
2023-01-17	131607	34	534	0
2023-01-16	131573	108	534	2
2023-01-13	131465	67	532	0
2023-01-12	131398	43	532	0
2023-01-11	131355	77	532	0
2023-01-10	131278	77	532	0
2023-01-09	131201	129	532	1
2023-01-06	131072	79	531	0
2023-01-05	130993	95	531	0
2023-01-04	130898	115	531	0
2023-01-03	130783	519	531	1
2023-01-02	130264	399	530	1
2022-12-23	129865	200	529	1
2022-12-22	129665	101	528	1
2022-12-21	129564	151	527	0
2022-12-20	129413	156	527	2
2022-12-19	129257	220	525	0
2022-12-16	129037	108	525	0
2022-12-15	128929	92	525	1
2022-12-14	128837	187	524	0
2022-12-13	128650	107	524	2
2022-12-12	128543	180	522	0
2022-12-09	128363	88	522	0
2022-12-08	128275	114	522	2
2022-12-07	128161	92	520	1
2022-12-06	128069	104	519	1
2022-12-05	127965	165	518	0
2022-12-02	127800	56	518	0
2022-12-01	127744	119	518	0
2022-11-30	127625	80	518	1
2022-11-29	127545	91	517	0
2022-11-28	127454	169	517	1
2022-11-25	127285	86	516	0
2022-11-24	127199	68	516	0
2022-11-23	127131	131	516	0
2022-11-22	127000	72	516	0
2022-11-21	126928	172	516	0
2022-11-18	126756	103	516	2
2022-11-17	126653	69	514	0
2022-11-16	126584	123	514	0
2022-11-15	126461	124	514	0
2022-11-14	126337	249	514	0
2022-11-11	126088	61	514	0
2022-11-10	126027	175	514	1
2022-11-09	125852	81	513	0
2022-11-08	125771	117	513	1
2022-11-07	125654	331	512	1
2022-11-04	125323	105	511	0
2022-11-03	125218	120	511	0
2022-11-02	125098	187	511	0
2022-11-01	124911	130	511	2
2022-10-31	124781	356	509	0
2022-10-28	124425	173	509	2
2022-10-27	124252	133	507	2
2022-10-26	124119	343	505	0
2022-10-25	123776	270	505	3
2022-10-24	123506	566	502	1
2022-10-21	122940	214	501	1
2022-10-20	122726	392	500	1
2022-10-19	122334	316	499	1
2022-10-18	122018	454	498	0
2022-10-17	121564	749	498	5
2022-10-14	120815	443	493	1
2022-10-13	120372	336	492	1
2022-10-12	120036	500	491	3
2022-10-11	119536	641	488	1
2022-10-10	118895	667	487	2
2022-10-07	118228	562	485	0
2022-10-06	117666	589	485	1
2022-10-05	117077	649	484	1
2022-10-04	116428	578	483	2
2022-09-30	115850	296	481	1
2022-09-29	115554	295	480	0
2022-09-28	115259	320	480	0
2022-09-27	114939	370	480	1
2022-09-26	114569	482	479	0
2022-09-23	114087	184	479	0
2022-09-22	113903	178	479	0
2022-09-21	113725	244	479	1
2022-09-20	113481	166	478	0
2022-09-19	113315	295	478	1
2022-09-16	113020	137	477	1
2022-09-15	112883	117	476	1
2022-09-14	112766	88	475	0
2022-09-13	112678	130	475	0
2022-09-12	112548	237	475	0
2022-09-09	112311	120	475	0
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2022-09-07	112085	137	475	1
2022-09-06	111948	146	474	0
2022-09-05	111802	289	474	0
2022-09-02	111513	108	474	1
2022-09-01	111405	93	473	0
2022-08-31	111312	157	473	0
2022-08-30	111155	220	473	1
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2022-08-19	109596	124	470	2
2022-08-18	109472	191	468	1
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2022-08-15	108844	307	465	0
2022-08-12	108537	197	465	0
2022-08-11	108340	131	465	1
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2022-08-09	108044	244	464	2
2022-08-08	107800	358	462	0
2022-08-05	107442	153	462	0
2022-08-04	107289	254	462	2
2022-08-03	107035	262	460	0
2022-08-02	106773	205	460	0
2022-08-01	106568	460	460	1
2022-07-29	106108	275	459	0
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2022-07-27	105534	352	459	0
2022-07-26	105182	652	459	1
2022-07-25	104530	537	458	2
2022-07-22	103993	489	456	1
2022-07-21	103504	625	455	3
2022-07-20	102879	445	452	0
2022-07-19	102434	697	452	0
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2020-03-13	7	1	0	0
2020-03-12	6	1	0	0
2020-03-11	5	4	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: 0:03:13.518076