Germany: LK Rhein-Pfalz-Kreis (Rheinland-Pfalz)¶

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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 14:56:19 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Rhein-Pfalz-Kreis", weeks=5);
2023-03-07T14:56:36.449089 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 60 60 80 80 100 100 120 120 7-day incidence rate (per 100K people) 64.5 LK Rhein-Pfalz-Kreis, Germany, last 5 weeks, last data point from 2023-03-06 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 5 10 15 20 daily change normalised per 100K 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar −0.050 −0.050 −0.025 −0.025 0.000 0.000 0.025 0.025 0.050 0.050 daily change Germany-LK Rhein-Pfalz-Kreis new deaths (rolling 7d mean) Germany-LK Rhein-Pfalz-Kreis new deaths 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.6 0.6 0.8 0.8 1.0 1.0 1.2 1.2 1.4 1.4 R & growth factor (based on cases) Germany-LK Rhein-Pfalz-Kreis cases daily growth factor Germany-LK Rhein-Pfalz-Kreis cases daily growth factor (rolling mean) Germany-LK Rhein-Pfalz-Kreis 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 Rhein-Pfalz-Kreis deaths daily growth factor Germany-LK Rhein-Pfalz-Kreis deaths daily growth factor (rolling mean) Germany-LK Rhein-Pfalz-Kreis 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 Rhein-Pfalz-Kreis doubling time cases (rolling mean) 0.00 7.75 15.50 23.26 31.01 daily change Germany-LK Rhein-Pfalz-Kreis new cases (rolling 7d mean) Germany-LK Rhein-Pfalz-Kreis new cases 0.000 0.372 0.743
In [4]:
overview(country="Germany", subregion="LK Rhein-Pfalz-Kreis");
2023-03-07T14:56:55.122943 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 0 500 500 1000 1000 1500 1500 7-day incidence rate (per 100K people) 64.5 LK Rhein-Pfalz-Kreis, 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 0 100 200 300 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 8 daily change normalised per 100K May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0.6 0.6 0.8 0.8 1.0 1.0 1.2 1.2 1.4 1.4 R & growth factor (based on cases) Germany-LK Rhein-Pfalz-Kreis cases daily growth factor Germany-LK Rhein-Pfalz-Kreis cases daily growth factor (rolling mean) Germany-LK Rhein-Pfalz-Kreis estimated R (using cases) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 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 Rhein-Pfalz-Kreis deaths daily growth factor Germany-LK Rhein-Pfalz-Kreis deaths daily growth factor (rolling mean) Germany-LK Rhein-Pfalz-Kreis 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 cases doubling time [days] Germany-LK Rhein-Pfalz-Kreis doubling time cases (rolling mean) Germany-LK Rhein-Pfalz-Kreis doubling time deaths (rolling mean) 0.0 155.1 310.1 465.2 daily change Germany-LK Rhein-Pfalz-Kreis new cases (rolling 7d mean) Germany-LK Rhein-Pfalz-Kreis new cases 0.0 3.1 6.2 9.3 12.4 daily change Germany-LK Rhein-Pfalz-Kreis new deaths (rolling 7d mean) Germany-LK Rhein-Pfalz-Kreis new deaths 0.0 52.2 104.4 156.6 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Rhein-Pfalz-Kreis", dates="2020-03-15:");
2023-03-07T14:59:27.720155 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 Rhein-Pfalz-Kreis LK Rhein-Pfalz-Kreis 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 Rhein-Pfalz-Kreis Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Rhein-Pfalz-Kreis")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Rhein-Pfalz-Kreis")
print(f'Population of country="Germany", subregion="LK Rhein-Pfalz-Kreis": {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 Rhein-Pfalz-Kreis": 155050 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 60835 11 309 0
2023-03-05 60824 2 309 0
2023-03-03 60822 27 309 0
2023-03-02 60795 15 309 0
2023-03-01 60780 19 309 0
2023-02-28 60761 26 309 0
2023-02-27 60735 28 309 0
2023-02-26 60707 5 309 0
2023-02-25 60702 25 309 0
2023-02-24 60677 37 309 0
2023-02-23 60640 29 309 0
2023-02-22 60611 28 309 0
2023-02-21 60583 36 309 0
2023-02-20 60547 37 309 0
2023-02-19 60510 5 309 0
2023-02-18 60505 10 309 0
2023-02-17 60495 21 309 0
2023-02-16 60474 22 309 0
2023-02-15 60452 16 309 0
2023-02-14 60436 21 309 0
2023-02-13 60415 24 309 0
2023-02-12 60391 1 309 0
2023-02-11 60390 6 309 0
2023-02-10 60384 13 309 0
2023-02-09 60371 9 309 0
2023-02-08 60362 9 309 0
2023-02-07 60353 29 309 0
2023-02-06 60324 16 309 0
2023-02-05 60308 7 309 0
2023-02-04 60301 9 309 0
2023-02-03 60292 23 309 0
2023-02-02 60269 10 309 0
2023-02-01 60259 16 309 0
2023-01-31 60243 25 309 0
2023-01-30 60218 13 309 0
2023-01-29 60205 5 309 0
2023-01-28 60200 2 309 0
2023-01-27 60198 10 309 0
2023-01-26 60188 10 309 0
2023-01-25 60178 19 309 0
2023-01-24 60159 26 309 1
2023-01-23 60133 6 308 0
2023-01-22 60127 1 308 0
2023-01-21 60126 10 308 0
2023-01-20 60116 12 308 1
2023-01-19 60104 8 307 0
2023-01-18 60096 18 307 1
2023-01-17 60078 24 306 0
2023-01-16 60054 23 306 0
2023-01-15 60031 7 306 0
2023-01-14 60024 8 306 0
2023-01-13 60016 22 306 1
2023-01-12 59994 16 305 0
2023-01-11 59978 42 305 0
2023-01-10 59936 29 305 0
2023-01-09 59907 33 305 0
2023-01-08 59874 3 305 0
2023-01-07 59871 13 305 0
2023-01-06 59858 31 305 0
2023-01-05 59827 33 305 1
2023-01-04 59794 30 304 0
2023-01-03 59764 37 304 1
2023-01-02 59727 58 303 0
2023-01-01 59669 8 303 0
2022-12-31 59661 33 303 0
2022-12-30 59628 23 303 0
2022-12-29 59605 44 303 0
2022-12-28 59561 60 303 0
2022-12-27 59501 23 303 0
2022-12-26 59478 19 303 0
2022-12-25 59459 13 303 0
2022-12-24 59446 34 303 0
2022-12-23 59412 64 303 0
2022-12-22 59348 31 303 0
2022-12-21 59317 36 303 0
2022-12-20 59281 51 303 0
2022-12-19 59230 44 303 0
2022-12-18 59186 8 303 0
2022-12-17 59178 21 303 0
2022-12-16 59157 29 303 0
2022-12-15 59128 28 303 0
2022-12-14 59100 39 303 0
2022-12-13 59061 43 303 0
2022-12-12 59018 26 303 0
2022-12-11 58992 18 303 0
2022-12-10 58974 19 303 0
2022-12-09 58955 22 303 0
2022-12-08 58933 36 303 0
2022-12-07 58897 39 303 0
2022-12-06 58858 50 303 0
2022-12-05 58808 18 303 0
2022-12-04 58790 13 303 0
2022-12-03 58777 34 303 0
2022-12-02 58743 26 303 0
2022-12-01 58717 33 303 0
2022-11-30 58684 25 303 0
2022-11-29 58659 25 303 0
2022-11-28 58634 42 303 0
2022-11-27 58592 10 303 0
2022-11-26 58582 19 303 0
2022-11-25 58563 28 303 0
2022-11-24 58535 49 303 0
2022-11-23 58486 28 303 0
2022-11-22 58458 43 303 0
2022-11-21 58415 42 303 0
2022-11-20 58373 13 303 0
2022-11-19 58360 25 303 0
2022-11-18 58335 39 303 0
2022-11-17 58296 43 303 0
2022-11-16 58253 32 303 0
2022-11-15 58221 52 303 0
2022-11-14 58169 38 303 0
2022-11-13 58131 6 303 0
2022-11-12 58125 22 303 0
2022-11-11 58103 36 303 0
2022-11-10 58067 40 303 0
2022-11-09 58027 42 303 0
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2022-11-07 57929 50 303 0
2022-11-06 57879 7 303 0
2022-11-05 57872 32 303 0
2022-11-04 57840 26 303 0
2022-11-03 57814 74 303 0
2022-11-02 57740 51 303 0
2022-11-01 57689 57 303 0
2022-10-31 57632 4 303 0
2022-10-29 57628 1 303 0
2022-10-27 57627 73 303 0
2022-10-26 57554 124 303 0
2022-10-25 57430 92 303 0
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2022-10-23 57246 45 303 0
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2022-10-21 57155 105 303 0
2022-10-20 57050 118 303 0
2022-10-19 56932 190 303 0
2022-10-18 56742 180 303 0
2022-10-17 56562 171 303 0
2022-10-16 56391 44 303 0
2022-10-15 56347 85 303 0
2022-10-14 56262 138 303 0
2022-10-13 56124 154 303 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: 0:03:20.527095