Germany: SK Berlin Tempelhof-Schöneberg (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 12:06:50 CEST

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

overview(country="Germany", subregion="SK Berlin Tempelhof-Schöneberg", weeks=5);

overview(country="Germany", subregion="SK Berlin Tempelhof-Schöneberg");

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

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

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="SK Berlin Tempelhof-Schöneberg")
print(f'Population of country="Germany", subregion="SK Berlin Tempelhof-Schöneberg": {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 Tempelhof-Schöneberg": 342285 people

	total cases	daily new cases	total deaths	daily new deaths
date
2023-03-03	131919	16	677	0
2023-03-02	131903	31	677	0
2023-03-01	131872	52	677	0
2023-02-28	131820	59	677	0
2023-02-27	131761	89	677	0
2023-02-24	131672	38	677	0
2023-02-23	131634	41	677	0
2023-02-22	131593	41	677	0
2023-02-21	131552	56	677	0
2023-02-20	131496	65	677	0
2023-02-17	131431	47	677	0
2023-02-16	131384	42	677	0
2023-02-15	131342	65	677	2
2023-02-14	131277	54	675	0
2023-02-13	131223	67	675	1
2023-02-10	131156	81	674	0
2023-02-09	131075	37	674	0
2023-02-08	131038	44	674	0
2023-02-07	130994	54	674	0
2023-02-06	130940	56	674	0
2023-02-03	130884	30	674	1
2023-02-02	130854	33	673	0
2023-02-01	130821	42	673	0
2023-01-31	130779	46	673	0
2023-01-30	130733	51	673	1
2023-01-27	130682	32	672	0
2023-01-26	130650	36	672	0
2023-01-25	130614	31	672	0
2023-01-24	130583	50	672	0
2023-01-23	130533	66	672	0
2023-01-20	130467	35	672	0
2023-01-19	130432	19	672	0
2023-01-18	130413	42	672	1
2023-01-17	130371	58	671	0
2023-01-16	130313	79	671	1
2023-01-14	130234	1	670	0
2023-01-13	130233	31	670	2
2023-01-12	130202	41	668	0
2023-01-11	130161	55	668	0
2023-01-10	130106	50	668	0
2023-01-09	130056	120	668	0
2023-01-06	129936	47	668	0
2023-01-05	129889	81	668	2
2023-01-04	129808	96	666	1
2023-01-03	129712	127	665	2
2023-01-02	129585	152	663	0
2022-12-30	129433	103	663	1
2022-12-29	129330	130	662	2
2022-12-28	129200	129	660	0
2022-12-27	129071	349	660	2
2022-12-24	128722	2	658	0
2022-12-23	128720	153	658	0
2022-12-22	128567	147	658	0
2022-12-21	128420	183	658	0
2022-12-20	128237	198	658	0
2022-12-19	128039	303	658	1
2022-12-17	127736	1	657	0
2022-12-16	127735	191	657	2
2022-12-15	127544	109	655	1
2022-12-14	127435	262	654	3
2022-12-13	127173	218	651	1
2022-12-12	126955	352	650	4
2022-12-09	126603	163	646	0
2022-12-08	126440	136	646	1
2022-12-07	126304	140	645	1
2022-12-06	126164	187	644	0
2022-12-05	125977	294	644	1
2022-12-02	125683	196	643	3
2022-12-01	125487	113	640	0
2022-11-30	125374	124	640	1
2022-11-29	125250	205	639	1
2022-11-28	125045	254	638	2
2022-11-25	124791	131	636	1
2022-11-24	124660	104	635	1
2022-11-23	124556	130	634	1
2022-11-22	124426	113	633	0
2022-11-21	124313	166	633	1
2022-11-18	124147	180	632	2
2022-11-17	123967	86	630	1
2022-11-16	123881	3	629	0
2022-11-15	123878	166	629	0
2022-11-14	123712	155	629	2
2022-11-13	123557	1	627	0
2022-11-11	123556	91	627	0
2022-11-10	123465	72	627	2
2022-11-09	123393	153	625	0
2022-11-08	123240	122	625	2
2022-11-07	123118	198	623	0
2022-11-04	122920	127	623	0
2022-11-03	122793	111	623	2
2022-11-02	122682	124	621	0
2022-11-01	122558	173	621	2
2022-10-31	122385	289	619	1
2022-10-29	122096	1	618	0
2022-10-28	122095	136	618	0
2022-10-27	121959	135	618	0
2022-10-26	121824	236	618	1
2022-10-25	121588	233	617	1
2022-10-24	121355	335	616	1
2022-10-23	121020	1	615	0
2022-10-22	121019	1	615	0
2022-10-21	121018	250	615	1
2022-10-20	120768	251	614	1
2022-10-19	120517	326	613	1
2022-10-18	120191	298	612	1
2022-10-17	119893	426	611	0
2022-10-15	119467	4	611	0
2022-10-14	119463	283	611	0
2022-10-13	119180	213	611	2
2022-10-12	118967	284	609	0
2022-10-11	118683	372	609	1
2022-10-10	118311	488	608	1
2022-10-08	117823	1	607	0
2022-10-07	117822	299	607	0
2022-10-06	117523	312	607	0
2022-10-05	117211	353	607	1
2022-10-04	116858	491	606	2
2022-10-02	116367	2	604	0
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2022-09-29	116102	158	604	2
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2022-09-26	115540	265	601	0
2022-09-24	115275	3	601	0
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2020-03-14	24	2	0	0
2020-03-13	22	7	0	0
2020-03-12	15	5	0	0
2020-03-11	10	1	0	0
2020-03-10	9	4	0	0
2020-03-09	5	3	0	0
2020-03-06	2	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: 0:03:04.136292