Germany: LK Landsberg a.Lech (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 10:55:10 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Landsberg a.Lech", weeks=5);
2023-03-07T10:55:22.601214 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 60 60 70 70 80 80 90 90 100 100 7-day incidence rate (per 100K people) 65.0 LK Landsberg a.Lech, 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 0.2 0.4 0.6 0.8 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-LK Landsberg a.Lech cases daily growth factor Germany-LK Landsberg a.Lech cases daily growth factor (rolling mean) Germany-LK Landsberg a.Lech estimated R (using cases) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.8 0.8 0.9 0.9 1.0 1.0 1.1 1.1 1.2 1.2 R & growth factor (based on deaths) Germany-LK Landsberg a.Lech deaths daily growth factor Germany-LK Landsberg a.Lech deaths daily growth factor (rolling mean) Germany-LK Landsberg a.Lech estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 2000 4000 cases doubling time [days] Germany-LK Landsberg a.Lech doubling time cases (rolling mean) 0.00 12.15 24.29 36.44 daily change Germany-LK Landsberg a.Lech new cases (rolling 7d mean) Germany-LK Landsberg a.Lech new cases 0.000 0.243 0.486 0.729 0.972 daily change Germany-LK Landsberg a.Lech new deaths (rolling 7d mean) Germany-LK Landsberg a.Lech new deaths 0.000 0.368 0.736
In [4]:
overview(country="Germany", subregion="LK Landsberg a.Lech");
2023-03-07T10:55:44.175177 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 3000 3000 7-day incidence rate (per 100K people) 65.0 LK Landsberg a.Lech, 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 0 250 500 750 1000 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 1 2 3 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-LK Landsberg a.Lech cases daily growth factor Germany-LK Landsberg a.Lech cases daily growth factor (rolling mean) Germany-LK Landsberg a.Lech 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 0.8 0.8 0.9 0.9 1.0 1.0 1.1 1.1 1.2 1.2 R & growth factor (based on deaths) Germany-LK Landsberg a.Lech deaths daily growth factor Germany-LK Landsberg a.Lech deaths daily growth factor (rolling mean) Germany-LK Landsberg a.Lech 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-LK Landsberg a.Lech doubling time cases (rolling mean) Germany-LK Landsberg a.Lech doubling time deaths (rolling mean) 0 304 607 911 1215 daily change Germany-LK Landsberg a.Lech new cases (rolling 7d mean) Germany-LK Landsberg a.Lech new cases 0.000 1.215 2.429 3.644 daily change Germany-LK Landsberg a.Lech new deaths (rolling 7d mean) Germany-LK Landsberg a.Lech new deaths 0.0 28.3 56.6 85.0 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Landsberg a.Lech", dates="2020-03-15:");
2023-03-07T10:58:07.720683 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: LK Landsberg a.Lech LK Landsberg a.Lech 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 LK Landsberg a.Lech Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Landsberg a.Lech")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Landsberg a.Lech")
print(f'Population of country="Germany", subregion="LK Landsberg a.Lech": {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 Landsberg a.Lech": 121466 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 58209 9 127 0
2023-03-03 58200 26 127 0
2023-03-02 58174 5 127 0
2023-03-01 58169 19 127 0
2023-02-28 58150 20 127 0
2023-02-27 58130 20 127 0
2023-02-24 58110 11 127 0
2023-02-23 58099 19 127 0
2023-02-22 58080 35 127 0
2023-02-21 58045 19 127 0
2023-02-20 58026 40 127 0
2023-02-17 57986 12 127 0
2023-02-16 57974 15 127 0
2023-02-15 57959 24 127 0
2023-02-14 57935 20 127 0
2023-02-13 57915 34 127 0
2023-02-10 57881 14 127 0
2023-02-09 57867 18 127 0
2023-02-08 57849 21 127 0
2023-02-07 57828 36 127 0
2023-02-06 57792 32 127 0
2023-02-03 57760 13 127 0
2023-02-02 57747 17 127 0
2023-02-01 57730 14 127 0
2023-01-31 57716 24 127 1
2023-01-30 57692 27 126 0
2023-01-27 57665 14 126 0
2023-01-26 57651 12 126 0
2023-01-25 57639 7 126 0
2023-01-24 57632 10 126 0
2023-01-23 57622 17 126 0
2023-01-20 57605 11 126 0
2023-01-19 57594 9 126 0
2023-01-18 57585 7 126 0
2023-01-17 57578 20 126 0
2023-01-16 57558 20 126 1
2023-01-13 57538 4 125 0
2023-01-12 57534 12 125 0
2023-01-11 57522 33 125 0
2023-01-10 57489 26 125 0
2023-01-09 57463 28 125 0
2023-01-05 57435 15 125 0
2023-01-04 57420 12 125 0
2023-01-03 57408 16 125 0
2023-01-02 57392 23 125 0
2022-12-30 57369 5 125 0
2022-12-29 57364 31 125 0
2022-12-28 57333 28 125 0
2022-12-27 57305 41 125 0
2022-12-23 57264 21 125 0
2022-12-22 57243 29 125 0
2022-12-21 57214 18 125 0
2022-12-20 57196 42 125 0
2022-12-19 57154 32 125 0
2022-12-16 57122 15 125 0
2022-12-15 57107 17 125 0
2022-12-14 57090 26 125 0
2022-12-13 57064 23 125 0
2022-12-12 57041 12 125 0
2022-12-09 57029 17 125 0
2022-12-08 57012 24 125 0
2022-12-07 56988 19 125 0
2022-12-06 56969 28 125 0
2022-12-05 56941 17 125 0
2022-12-02 56924 19 125 0
2022-12-01 56905 13 125 0
2022-11-30 56892 20 125 0
2022-11-29 56872 18 125 0
2022-11-28 56854 20 125 0
2022-11-25 56834 15 125 0
2022-11-24 56819 14 125 0
2022-11-23 56805 25 125 0
2022-11-22 56780 19 125 0
2022-11-21 56761 32 125 0
2022-11-18 56729 15 125 0
2022-11-17 56714 17 125 0
2022-11-16 56697 29 125 0
2022-11-15 56668 29 125 1
2022-11-14 56639 36 124 0
2022-11-11 56603 17 124 0
2022-11-10 56586 26 124 0
2022-11-09 56560 34 124 0
2022-11-08 56526 38 124 0
2022-11-07 56488 65 124 0
2022-11-04 56423 27 124 0
2022-11-03 56396 43 124 0
2022-11-02 56353 69 124 0
2022-10-31 56284 74 124 0
2022-10-28 56210 31 124 0
2022-10-27 56179 74 124 0
2022-10-26 56105 85 124 0
2022-10-25 56020 80 124 0
2022-10-24 55940 140 124 0
2022-10-21 55800 74 124 0
2022-10-20 55726 113 124 0
2022-10-19 55613 166 124 0
2022-10-18 55447 177 124 0
2022-10-17 55270 254 124 0
2022-10-14 55016 150 124 0
2022-10-13 54866 197 124 0
2022-10-12 54669 314 124 0
2022-10-11 54355 332 124 0
2022-10-10 54023 479 124 0
2022-10-07 53544 289 124 0
2022-10-06 53255 495 124 0
2022-10-05 52760 291 124 0
2022-10-04 52469 380 124 0
2022-09-30 52089 169 124 0
2022-09-29 51920 182 124 0
2022-09-28 51738 249 124 0
2022-09-27 51489 157 124 0
2022-09-26 51332 272 124 1
2022-09-23 51060 1 123 0
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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)

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: 0:03:13.064540