Germany: SK Berlin Steglitz-Zehlendorf (Berlin)

• 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 12:06:07 CEST

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

overview(country="Germany", subregion="SK Berlin Steglitz-Zehlendorf", weeks=5);

overview(country="Germany", subregion="SK Berlin Steglitz-Zehlendorf");

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

# load the data
cases, deaths = germany_get_region(landkreis="SK Berlin Steglitz-Zehlendorf")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="SK Berlin Steglitz-Zehlendorf")
print(f'Population of country="Germany", subregion="SK Berlin Steglitz-Zehlendorf": {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 Berlin Steglitz-Zehlendorf": 292255 people

	total cases	daily new cases	total deaths	daily new deaths
date
2023-03-06	107122	21	630	0
2023-03-03	107101	17	630	0
2023-03-02	107084	26	630	1
2023-03-01	107058	59	629	1
2023-02-28	106999	70	628	0
2023-02-27	106929	66	628	0
2023-02-24	106863	42	628	0
2023-02-23	106821	41	628	0
2023-02-22	106780	42	628	0
2023-02-21	106738	77	628	2
2023-02-20	106661	54	626	0
2023-02-17	106607	32	626	0
2023-02-16	106575	22	626	0
2023-02-15	106553	44	626	0
2023-02-14	106509	48	626	0
2023-02-13	106461	67	626	2
2023-02-10	106394	38	624	0
2023-02-09	106356	26	624	0
2023-02-08	106330	33	624	0
2023-02-07	106297	46	624	0
2023-02-06	106251	43	624	1
2023-02-03	106208	32	623	0
2023-02-02	106176	25	623	0
2023-02-01	106151	39	623	0
2023-01-31	106112	77	623	1
2023-01-30	106035	48	622	0
2023-01-27	105987	30	622	0
2023-01-26	105957	46	622	0
2023-01-25	105911	35	622	1
2023-01-24	105876	39	621	0
2023-01-23	105837	37	621	0
2023-01-20	105800	40	621	0
2023-01-19	105760	12	621	0
2023-01-18	105748	27	621	1
2023-01-17	105721	49	620	0
2023-01-16	105672	55	620	0
2023-01-13	105617	28	620	0
2023-01-12	105589	44	620	0
2023-01-11	105545	60	620	1
2023-01-10	105485	48	619	0
2023-01-09	105437	85	619	2
2023-01-06	105352	44	617	0
2023-01-05	105308	73	617	0
2023-01-04	105235	107	617	0
2023-01-03	105128	131	617	1
2023-01-02	104997	136	616	2
2022-12-31	104861	1	614	0
2022-12-30	104860	109	614	5
2022-12-29	104751	170	609	3
2022-12-28	104581	171	606	3
2022-12-27	104410	194	603	1
2022-12-23	104216	104	602	1
2022-12-22	104112	164	601	2
2022-12-21	103948	149	599	1
2022-12-20	103799	206	598	1
2022-12-19	103593	231	597	2
2022-12-17	103362	1	595	0
2022-12-16	103361	185	595	1
2022-12-15	103176	174	594	2
2022-12-14	103002	133	592	1
2022-12-13	102869	172	591	0
2022-12-12	102697	277	591	0
2022-12-11	102420	1	591	0
2022-12-10	102419	1	591	0
2022-12-09	102418	123	591	1
2022-12-08	102295	160	590	0
2022-12-07	102135	130	590	0
2022-12-06	102005	213	590	2
2022-12-05	101792	212	588	1
2022-12-03	101580	2	587	0
2022-12-02	101578	134	587	2
2022-12-01	101444	156	585	1
2022-11-30	101288	146	584	0
2022-11-29	101142	135	584	0
2022-11-28	101007	224	584	1
2022-11-25	100783	106	583	1
2022-11-24	100677	100	582	0
2022-11-23	100577	120	582	0
2022-11-22	100457	83	582	0
2022-11-21	100374	153	582	1
2022-11-18	100221	103	581	0
2022-11-17	100118	142	581	0
2022-11-16	99976	98	581	0
2022-11-15	99878	81	581	0
2022-11-14	99797	174	581	1
2022-11-11	99623	83	580	0
2022-11-10	99540	70	580	2
2022-11-09	99470	103	578	0
2022-11-08	99367	131	578	0
2022-11-07	99236	136	578	0
2022-11-04	99100	70	578	0
2022-11-03	99030	117	578	1
2022-11-02	98913	108	577	0
2022-11-01	98805	167	577	0
2022-10-31	98638	268	577	0
2022-10-30	98370	2	577	0
2022-10-29	98368	7	577	0
2022-10-28	98361	152	577	3
2022-10-27	98209	167	574	1
2022-10-26	98042	269	573	0
2022-10-25	97773	260	573	1
2022-10-24	97513	257	572	0
2022-10-23	97256	5	572	0
2022-10-22	97251	6	572	0
2022-10-21	97245	392	572	1
2022-10-20	96853	332	571	1
2022-10-19	96521	289	570	0
2022-10-18	96232	305	570	2
2022-10-17	95927	323	568	1
2022-10-16	95604	2	567	0
2022-10-15	95602	15	567	0
2022-10-14	95587	278	567	1
2022-10-13	95309	270	566	0
2022-10-12	95039	288	566	0
2022-10-11	94751	343	566	1
2022-10-10	94408	378	565	3
2022-10-09	94030	2	562	0
2022-10-07	94028	376	562	2
2022-10-06	93652	344	560	0
2022-10-05	93308	354	560	1
2022-10-04	92954	199	559	3
2022-09-30	92755	223	556	4
2022-09-29	92532	136	552	0
2022-09-28	92396	201	552	0
2022-09-27	92195	215	552	2
2022-09-26	91980	141	550	1
2022-09-25	91839	2	549	0
2022-09-24	91837	1	549	0
2022-09-23	91836	170	549	2
2022-09-22	91666	148	547	0
2022-09-21	91518	119	547	0
2022-09-20	91399	98	547	1
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2022-09-06	90039	178	544	0
2022-09-05	89861	196	544	0
2022-09-03	89665	2	544	0
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2022-08-19	88422	90	542	2
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2020-04-12	391	4	6	0
2020-04-11	387	15	6	0
2020-04-10	372	9	6	1
2020-04-09	363	13	5	1
2020-04-08	350	16	4	1
2020-04-07	334	17	3	0
2020-04-06	317	12	3	2
2020-04-05	305	4	1	0
2020-04-04	301	11	1	0
2020-04-03	290	18	1	0
2020-04-02	272	19	1	0
2020-04-01	253	24	1	0
2020-03-31	229	27	1	0
2020-03-30	202	8	1	0
2020-03-29	194	12	1	0
2020-03-28	182	40	1	0
2020-03-27	142	6	1	0
2020-03-26	136	19	1	0
2020-03-25	117	8	1	0
2020-03-24	109	8	1	0
2020-03-23	101	6	1	0
2020-03-22	95	3	1	1
2020-03-21	92	15	0	0
2020-03-20	77	11	0	0
2020-03-19	66	13	0	0
2020-03-18	53	10	0	0
2020-03-17	43	6	0	0
2020-03-16	37	6	0	0
2020-03-15	31	3	0	0
2020-03-14	28	6	0	0
2020-03-13	22	4	0	0
2020-03-12	18	6	0	0
2020-03-11	12	6	0	0
2020-03-10	6	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: 0:03:25.124201