Germany: LK Coesfeld (Nordrhein-Westfalen)¶

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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 13:45:06 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Coesfeld", weeks=5);
2023-03-07T13:45:19.151823 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 100 100 150 150 200 200 7-day incidence rate (per 100K people) 72.3 LK Coesfeld, 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 80 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 0.4 daily change normalised per 100K 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.50 0.50 0.75 0.75 1.00 1.00 1.25 1.25 1.50 1.50 R & growth factor (based on cases) Germany-LK Coesfeld cases daily growth factor Germany-LK Coesfeld cases daily growth factor (rolling mean) Germany-LK Coesfeld estimated R (using cases) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 0 1 1 2 2 3 3 4 4 R & growth factor (based on deaths) Germany-LK Coesfeld deaths daily growth factor Germany-LK Coesfeld deaths daily growth factor (rolling mean) Germany-LK Coesfeld 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 Coesfeld doubling time cases (rolling mean) 0.0 44.3 88.5 132.8 177.1 daily change Germany-LK Coesfeld new cases (rolling 7d mean) Germany-LK Coesfeld new cases 0.000 0.221 0.443 0.664 0.885 daily change Germany-LK Coesfeld new deaths (rolling 7d mean) Germany-LK Coesfeld new deaths 0.000 0.364 0.728
In [4]:
overview(country="Germany", subregion="LK Coesfeld");
2023-03-07T13:45:35.793854 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 7-day incidence rate (per 100K people) 72.3 LK Coesfeld, 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 daily change normalised per 100K May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0.0 0.5 1.0 1.5 daily change normalised per 100K May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0.50 0.50 0.75 0.75 1.00 1.00 1.25 1.25 1.50 1.50 R & growth factor (based on cases) Germany-LK Coesfeld cases daily growth factor Germany-LK Coesfeld cases daily growth factor (rolling mean) Germany-LK Coesfeld estimated R (using cases) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0 0 1 1 2 2 3 3 4 4 R & growth factor (based on deaths) Germany-LK Coesfeld deaths daily growth factor Germany-LK Coesfeld deaths daily growth factor (rolling mean) Germany-LK Coesfeld 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 Coesfeld doubling time cases (rolling mean) Germany-LK Coesfeld doubling time deaths (rolling mean) 0 443 885 daily change Germany-LK Coesfeld new cases (rolling 7d mean) Germany-LK Coesfeld new cases 0.000 1.107 2.214 3.320 daily change Germany-LK Coesfeld new deaths (rolling 7d mean) Germany-LK Coesfeld new deaths 0.00 19.59 39.17 58.76 78.35 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Coesfeld", dates="2020-03-15:");
2023-03-07T13:48:05.472929 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 Coesfeld LK Coesfeld 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 Coesfeld Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Coesfeld")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Coesfeld")
print(f'Population of country="Germany", subregion="LK Coesfeld": {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 Coesfeld": 221352 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 110508 17 161 0
2023-03-05 110491 1 161 0
2023-03-04 110490 5 161 0
2023-03-03 110485 14 161 0
2023-03-02 110471 21 161 0
2023-03-01 110450 40 161 0
2023-02-28 110410 62 161 0
2023-02-27 110348 44 161 0
2023-02-26 110304 12 161 0
2023-02-25 110292 28 161 0
2023-02-24 110264 60 161 0
2023-02-23 110204 90 161 0
2023-02-22 110114 42 161 0
2023-02-21 110072 76 161 0
2023-02-20 109996 181 161 0
2023-02-19 109815 5 161 0
2023-02-18 109810 20 161 0
2023-02-17 109790 42 161 0
2023-02-16 109748 54 161 1
2023-02-15 109694 48 160 0
2023-02-14 109646 63 160 0
2023-02-13 109583 42 160 0
2023-02-12 109541 12 160 0
2023-02-11 109529 20 160 0
2023-02-10 109509 38 160 0
2023-02-09 109471 40 160 0
2023-02-08 109431 30 160 0
2023-02-07 109401 53 160 0
2023-02-06 109348 45 160 0
2023-02-05 109303 9 160 0
2023-02-04 109294 25 160 0
2023-02-03 109269 19 160 0
2023-02-02 109250 44 160 0
2023-02-01 109206 31 160 0
2023-01-31 109175 45 160 0
2023-01-30 109130 37 160 0
2023-01-29 109093 11 160 0
2023-01-28 109082 15 160 0
2023-01-27 109067 39 160 0
2023-01-26 109028 27 160 0
2023-01-25 109001 78 160 0
2023-01-24 108923 36 160 0
2023-01-23 108887 48 160 0
2023-01-22 108839 14 160 0
2023-01-21 108825 34 160 0
2023-01-20 108791 29 160 0
2023-01-19 108762 27 160 0
2023-01-18 108735 44 160 0
2023-01-17 108691 34 160 0
2023-01-16 108657 31 160 0
2023-01-15 108626 8 160 0
2023-01-14 108618 32 160 0
2023-01-13 108586 35 160 0
2023-01-12 108551 40 160 0
2023-01-11 108511 30 160 0
2023-01-10 108481 78 160 0
2023-01-09 108403 56 160 0
2023-01-08 108347 11 160 0
2023-01-07 108336 53 160 0
2023-01-06 108283 20 160 0
2023-01-05 108263 61 160 0
2023-01-04 108202 96 160 0
2023-01-03 108106 73 160 0
2023-01-02 108033 104 160 0
2023-01-01 107929 28 160 0
2022-12-31 107901 34 160 0
2022-12-30 107867 88 160 0
2022-12-29 107779 103 160 0
2022-12-28 107676 79 160 0
2022-12-27 107597 121 160 0
2022-12-26 107476 74 160 0
2022-12-25 107402 29 160 0
2022-12-24 107373 108 160 0
2022-12-23 107265 62 160 0
2022-12-22 107203 150 160 0
2022-12-21 107053 126 160 0
2022-12-20 106927 129 160 0
2022-12-19 106798 165 160 0
2022-12-18 106633 26 160 0
2022-12-17 106607 56 160 0
2022-12-16 106551 86 160 0
2022-12-15 106465 115 160 0
2022-12-14 106350 157 160 0
2022-12-13 106193 117 160 0
2022-12-12 106076 79 160 0
2022-12-11 105997 19 160 0
2022-12-10 105978 39 160 0
2022-12-09 105939 62 160 0
2022-12-08 105877 78 160 1
2022-12-07 105799 64 159 0
2022-12-06 105735 140 159 1
2022-12-05 105595 60 158 1
2022-12-04 105535 17 157 0
2022-12-03 105518 47 157 0
2022-12-02 105471 59 157 0
2022-12-01 105412 85 157 1
2022-11-30 105327 73 156 0
2022-11-29 105254 150 156 0
2022-11-28 105104 91 156 0
2022-11-27 105013 25 156 1
2022-11-26 104988 47 155 0
2022-11-25 104941 77 155 0
2022-11-24 104864 126 155 0
2022-11-23 104738 84 155 1
2022-11-22 104654 101 154 0
2022-11-21 104553 81 154 0
2022-11-20 104472 18 154 0
2022-11-19 104454 33 154 0
2022-11-18 104421 61 154 0
2022-11-17 104360 98 154 1
2022-11-16 104262 102 153 0
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2022-11-11 103885 98 153 0
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2022-11-06 103214 19 153 0
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2022-11-04 103080 94 153 1
2022-11-03 102986 210 152 0
2022-11-02 102776 287 152 0
2022-11-01 102489 86 152 0
2022-10-31 102403 209 152 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: 0:04:16.536594