Germany: LK Neu-Ulm (Bayern)

• 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 11:12:33 CEST

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

overview(country="Germany", subregion="LK Neu-Ulm", weeks=5);

overview(country="Germany", subregion="LK Neu-Ulm");

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

# load the data
cases, deaths = germany_get_region(landkreis="LK Neu-Ulm")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Neu-Ulm")
print(f'Population of country="Germany", subregion="LK Neu-Ulm": {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 Neu-Ulm": 177330 people

	total cases	daily new cases	total deaths	daily new deaths
date
2023-03-06	81712	11	312	0
2023-03-05	81701	1	312	0
2023-03-03	81700	9	312	0
2023-03-02	81691	15	312	0
2023-03-01	81676	33	312	0
2023-02-28	81643	18	312	0
2023-02-27	81625	46	312	0
2023-02-26	81579	1	312	0
2023-02-24	81578	20	312	0
2023-02-23	81558	36	312	0
2023-02-22	81522	33	312	0
2023-02-21	81489	46	312	0
2023-02-20	81443	71	312	1
2023-02-17	81372	21	311	0
2023-02-16	81351	34	311	0
2023-02-15	81317	54	311	1
2023-02-14	81263	60	310	0
2023-02-13	81203	44	310	0
2023-02-10	81159	29	310	0
2023-02-09	81130	23	310	0
2023-02-08	81107	31	310	0
2023-02-07	81076	30	310	0
2023-02-06	81046	40	310	0
2023-02-03	81006	24	310	0
2023-02-02	80982	21	310	0
2023-02-01	80961	39	310	0
2023-01-31	80922	36	310	0
2023-01-30	80886	27	310	0
2023-01-27	80859	17	310	0
2023-01-26	80842	22	310	0
2023-01-25	80820	24	310	0
2023-01-24	80796	27	310	0
2023-01-23	80769	32	310	0
2023-01-20	80737	19	310	0
2023-01-19	80718	5	310	0
2023-01-18	80713	13	310	0
2023-01-17	80700	22	310	0
2023-01-16	80678	23	310	0
2023-01-15	80655	1	310	0
2023-01-13	80654	16	310	0
2023-01-12	80638	21	310	0
2023-01-11	80617	28	310	0
2023-01-10	80589	32	310	1
2023-01-09	80557	32	309	2
2023-01-07	80525	1	307	0
2023-01-06	80524	1	307	0
2023-01-05	80523	15	307	0
2023-01-04	80508	23	307	0
2023-01-03	80485	22	307	0
2023-01-02	80463	44	307	0
2022-12-30	80419	25	307	0
2022-12-29	80394	20	307	0
2022-12-28	80374	55	307	0
2022-12-27	80319	63	307	1
2022-12-23	80256	33	306	0
2022-12-22	80223	24	306	0
2022-12-21	80199	47	306	0
2022-12-20	80152	46	306	0
2022-12-19	80106	69	306	1
2022-12-18	80037	1	305	0
2022-12-16	80036	32	305	0
2022-12-15	80004	35	305	1
2022-12-14	79969	51	304	2
2022-12-13	79918	53	302	0
2022-12-12	79865	55	302	1
2022-12-09	79810	29	301	1
2022-12-08	79781	28	300	0
2022-12-07	79753	52	300	0
2022-12-06	79701	116	300	0
2022-12-05	79585	38	300	0
2022-12-03	79547	1	300	0
2022-12-02	79546	36	300	0
2022-12-01	79510	20	300	0
2022-11-30	79490	32	300	0
2022-11-29	79458	26	300	0
2022-11-28	79432	45	300	1
2022-11-25	79387	23	299	0
2022-11-24	79364	27	299	0
2022-11-23	79337	34	299	0
2022-11-22	79303	56	299	0
2022-11-21	79247	64	299	0
2022-11-18	79183	38	299	0
2022-11-17	79145	39	299	0
2022-11-16	79106	59	299	0
2022-11-15	79047	59	299	1
2022-11-14	78988	75	298	0
2022-11-11	78913	36	298	0
2022-11-10	78877	49	298	0
2022-11-09	78828	53	298	0
2022-11-08	78775	78	298	0
2022-11-07	78697	102	298	1
2022-11-04	78595	45	297	0
2022-11-03	78550	65	297	0
2022-11-02	78485	112	297	0
2022-10-31	78373	84	297	1
2022-10-30	78289	1	296	0
2022-10-28	78288	53	296	0
2022-10-27	78235	91	296	0
2022-10-26	78144	101	296	1
2022-10-25	78043	152	295	0
2022-10-24	77891	214	295	2
2022-10-23	77677	1	293	0
2022-10-22	77676	2	293	0
2022-10-21	77674	108	293	0
2022-10-20	77566	146	293	0
2022-10-19	77420	213	293	0
2022-10-18	77207	234	293	0
2022-10-17	76973	316	293	1
2022-10-16	76657	3	292	0
2022-10-14	76654	164	292	0
2022-10-13	76490	219	292	2
2022-10-12	76271	265	290	0
2022-10-11	76006	329	290	0
2022-10-10	75677	467	290	1
2022-10-09	75210	7	289	0
2022-10-08	75203	2	289	0
2022-10-07	75201	240	289	0
2022-10-06	74961	331	289	1
2022-10-05	74630	362	288	0
2022-10-04	74268	353	288	0
2022-10-03	73915	2	288	0
2022-10-02	73913	1	288	0
2022-09-30	73912	170	288	0
2022-09-29	73742	200	288	0
2022-09-28	73542	233	288	1
2022-09-27	73309	191	287	0
2022-09-26	73118	222	287	0
2022-09-25	72896	2	287	0
2022-09-24	72894	2	287	0
2022-09-23	72892	80	287	0
2022-09-22	72812	99	287	0
2022-09-21	72713	88	287	0
2022-09-20	72625	74	287	0
2022-09-19	72551	97	287	0
2022-09-18	72454	1	287	0
2022-09-17	72453	3	287	0
2022-09-16	72450	49	287	0
2022-09-15	72401	62	287	0
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2022-09-12	72220	82	287	0
2022-09-11	72138	4	287	0
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2022-09-04	71837	1	287	0
2022-09-02	71836	37	287	0
2022-09-01	71799	29	287	0
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2022-08-30	71730	52	287	0
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2020-03-19	26	8	2	1
2020-03-18	18	2	1	0
2020-03-17	16	6	1	0
2020-03-16	10	1	1	0
2020-03-14	9	2	1	0
2020-03-13	7	4	1	1
2020-03-11	3	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: 0:03:48.230821