Germany: SK München (Bayern)¶

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In [1]:
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 11:43:34 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="SK München", weeks=5);
2023-03-07T11:43:48.894475 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 80 80 90 90 100 100 110 110 7-day incidence rate (per 100K people) 73.7 SK München, Germany, last 5 weeks, last data point from 2023-03-06 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 10 20 30 daily change normalised per 100K 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.0 0.1 0.2 0.3 daily change normalised per 100K 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.8 0.8 1.0 1.0 1.2 1.2 R & growth factor (based on cases) Germany-SK München cases daily growth factor Germany-SK München cases daily growth factor (rolling mean) Germany-SK München estimated R (using cases) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.5 0.5 1.0 1.0 1.5 1.5 R & growth factor (based on deaths) Germany-SK München deaths daily growth factor Germany-SK München deaths daily growth factor (rolling mean) Germany-SK München estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 2000 4000 cases doubling time [days] Germany-SK München doubling time cases (rolling mean) Germany-SK München doubling time deaths (rolling mean) 0.0 148.8 297.5 446.3 daily change Germany-SK München new cases (rolling 7d mean) Germany-SK München new cases 0.000 1.488 2.975 4.463 daily change Germany-SK München new deaths (rolling 7d mean) Germany-SK München new deaths 0 872 1745 deaths doubling time [days]
In [4]:
overview(country="Germany", subregion="SK München");
2023-03-07T11:44:09.274495 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ Jan 20 May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 May 23 0 0 1000 1000 2000 2000 7-day incidence rate (per 100K people) 73.7 SK München, Germany, last data point from 2023-03-06 Jan 20 May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 May 23 0 200 400 daily change normalised per 100K Jan 20 May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 May 23 0.0 0.5 1.0 1.5 2.0 daily change normalised per 100K Jan 20 May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 May 23 0.8 0.8 1.0 1.0 1.2 1.2 R & growth factor (based on cases) Germany-SK München cases daily growth factor Germany-SK München cases daily growth factor (rolling mean) Germany-SK München estimated R (using cases) Jan 20 May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 May 23 0.5 0.5 1.0 1.0 1.5 1.5 R & growth factor (based on deaths) Germany-SK München deaths daily growth factor Germany-SK München deaths daily growth factor (rolling mean) Germany-SK München estimated R (using deaths) Jan 20 May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 May 23 0 2000 4000 6000 cases doubling time [days] Germany-SK München doubling time cases (rolling mean) Germany-SK München doubling time deaths (rolling mean) 0 2975 5951 daily change Germany-SK München new cases (rolling 7d mean) Germany-SK München new cases 0.00 7.44 14.88 22.32 29.75 daily change Germany-SK München new deaths (rolling 7d mean) Germany-SK München new deaths 0 914 1829 2743 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="SK München", dates="2020-03-15:");
2023-03-07T11:46:23.160240 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 2020-05 2020-09 2021-01 2021-05 2021-09 2022-01 2022-05 2022-09 2023-01 0.1 0.1 1 1 10 10 100 100 daily new cases (rolling 7-day mean) normalised by 100K people Daily cases (top) and deaths (below) for Germany: SK München SK München Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt 2020-05 2020-09 2021-01 2021-05 2021-09 2022-01 2022-05 2022-09 2023-01 0.001 0.001 0.01 0.01 0.1 0.1 1 1 daily new deaths (rolling 7-day mean) normalised by 100K people SK München Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="SK München")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="SK München")
print(f'Population of country="Germany", subregion="SK München": {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 München": 1487708 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 718111 204 2545 0
2023-03-03 717907 120 2545 0
2023-03-02 717787 168 2545 1
2023-03-01 717619 302 2544 0
2023-02-28 717317 303 2544 0
... ... ... ... ...
2020-03-03 9 1 0 0
2020-03-02 8 3 0 0
2020-02-28 5 2 0 0
2020-02-11 3 1 0 0
2020-02-03 2 1 0 0

1064 rows × 4 columns

Explore the data in your web browser¶

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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)

In [7]:
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.
In [8]:
# to force a fresh download of data, run "clear_cache()"
In [9]:
print(f"Notebook execution took: {datetime.datetime.now()-start}")
Notebook execution took: 0:03:11.380857