Germany: LK Landshut (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 11:02:50 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Landshut", weeks=5);
2023-03-07T11:03:06.371540 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 100 100 125 125 150 150 175 175 200 200 7-day incidence rate (per 100K people) 95.5 LK Landshut, Germany, last 5 weeks, last data point from 2023-03-06 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 20 40 60 daily change normalised per 100K 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.0 0.5 1.0 1.5 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 Landshut cases daily growth factor Germany-LK Landshut cases daily growth factor (rolling mean) Germany-LK Landshut estimated R (using cases) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 1 1 2 2 R & growth factor (based on deaths) Germany-LK Landshut deaths daily growth factor Germany-LK Landshut deaths daily growth factor (rolling mean) Germany-LK Landshut estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 1000 2000 3000 cases doubling time [days] Germany-LK Landshut doubling time cases (rolling mean) Germany-LK Landshut doubling time deaths (rolling mean) 0.0 32.5 64.9 97.4 daily change Germany-LK Landshut new cases (rolling 7d mean) Germany-LK Landshut new cases 0.000 0.812 1.623 2.435 daily change Germany-LK Landshut new deaths (rolling 7d mean) Germany-LK Landshut new deaths 0.0 78.4 156.9 235.3 deaths doubling time [days]
In [4]:
overview(country="Germany", subregion="LK Landshut");
2023-03-07T11:03:27.078987 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0 0 1000 1000 2000 2000 3000 3000 7-day incidence rate (per 100K people) 95.5 LK Landshut, Germany, last data point from 2023-03-06 May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0 200 400 600 daily change normalised per 100K May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0 2 4 6 daily change normalised per 100K May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0.8 0.8 1.0 1.0 1.2 1.2 R & growth factor (based on cases) Germany-LK Landshut cases daily growth factor Germany-LK Landshut cases daily growth factor (rolling mean) Germany-LK Landshut estimated R (using cases) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 1 1 2 2 R & growth factor (based on deaths) Germany-LK Landshut deaths daily growth factor Germany-LK Landshut deaths daily growth factor (rolling mean) Germany-LK Landshut estimated R (using deaths) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0 2000 4000 6000 8000 cases doubling time [days] Germany-LK Landshut doubling time cases (rolling mean) Germany-LK Landshut doubling time deaths (rolling mean) 0 325 649 974 daily change Germany-LK Landshut new cases (rolling 7d mean) Germany-LK Landshut new cases 0.00 3.25 6.49 9.74 daily change Germany-LK Landshut new deaths (rolling 7d mean) Germany-LK Landshut new deaths 0.0 67.0 133.9 200.9 267.9 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Landshut", dates="2020-03-15:");
2023-03-07T11:05:51.121602 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 Landshut LK Landshut 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 Landshut Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Landshut")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Landshut")
print(f'Population of country="Germany", subregion="LK Landshut": {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 Landshut": 162331 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 86732 31 379 0
2023-03-03 86701 18 379 0
2023-03-02 86683 22 379 0
2023-03-01 86661 35 379 1
2023-02-28 86626 49 378 0
2023-02-27 86577 72 378 1
2023-02-24 86505 41 377 0
2023-02-23 86464 49 377 1
2023-02-22 86415 107 376 0
2023-02-20 86308 87 376 0
2023-02-17 86221 35 376 0
2023-02-16 86186 56 376 0
2023-02-15 86130 62 376 3
2023-02-14 86068 87 373 0
2023-02-13 85981 85 373 1
2023-02-10 85896 41 372 0
2023-02-09 85855 58 372 1
2023-02-08 85797 63 371 1
2023-02-07 85734 77 370 0
2023-02-06 85657 89 370 0
2023-02-03 85568 29 370 0
2023-02-02 85539 65 370 1
2023-02-01 85474 37 369 0
2023-01-31 85437 67 369 1
2023-01-30 85370 73 368 1
2023-01-27 85297 22 367 0
2023-01-26 85275 40 367 0
2023-01-25 85235 25 367 0
2023-01-24 85210 33 367 0
2023-01-23 85177 57 367 0
2023-01-20 85120 13 367 0
2023-01-19 85107 19 367 0
2023-01-18 85088 36 367 1
2023-01-17 85052 31 366 0
2023-01-16 85021 42 366 1
2023-01-13 84979 17 365 0
2023-01-12 84962 21 365 0
2023-01-11 84941 32 365 0
2023-01-10 84909 25 365 0
2023-01-09 84884 61 365 0
2023-01-05 84823 40 365 0
2023-01-04 84783 30 365 0
2023-01-03 84753 34 365 1
2023-01-02 84719 26 364 0
2022-12-30 84693 36 364 0
2022-12-29 84657 35 364 0
2022-12-28 84622 36 364 0
2022-12-27 84586 57 364 0
2022-12-23 84529 22 364 0
2022-12-22 84507 26 364 0
2022-12-21 84481 36 364 0
2022-12-20 84445 36 364 0
2022-12-19 84409 58 364 0
2022-12-16 84351 14 364 0
2022-12-15 84337 18 364 0
2022-12-14 84319 25 364 0
2022-12-13 84294 23 364 0
2022-12-12 84271 32 364 0
2022-12-09 84239 20 364 0
2022-12-08 84219 26 364 0
2022-12-07 84193 16 364 0
2022-12-06 84177 31 364 0
2022-12-05 84146 52 364 1
2022-12-02 84094 10 363 0
2022-12-01 84084 22 363 0
2022-11-30 84062 16 363 0
2022-11-29 84046 36 363 0
2022-11-28 84010 30 363 0
2022-11-25 83980 18 363 0
2022-11-24 83962 40 363 0
2022-11-23 83922 11 363 0
2022-11-22 83911 24 363 0
2022-11-21 83887 46 363 0
2022-11-18 83841 13 363 0
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2022-11-16 83807 25 363 0
2022-11-15 83782 48 363 1
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2022-10-31 83169 129 358 0
2022-10-28 83040 47 358 4
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2022-10-26 82913 116 354 1
2022-10-25 82797 150 353 2
2022-10-24 82647 169 351 0
2022-10-21 82478 112 351 0
2022-10-20 82366 143 351 0
2022-10-19 82223 225 351 0
2022-10-18 81998 315 351 3
2022-10-17 81683 389 348 1
2022-10-16 81294 1 347 1
2022-10-14 81293 149 346 0
2022-10-13 81144 240 346 2
2022-10-12 80904 254 344 1
2022-10-11 80650 498 343 2
2022-10-10 80152 458 341 1
2022-10-07 79694 256 340 0
2022-10-06 79438 341 340 1
2022-10-05 79097 313 339 2
2022-10-04 78784 520 337 0
2022-09-30 78264 192 337 0
2022-09-29 78072 215 337 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: 0:03:17.473262