Germany: LK Grafschaft Bentheim (Niedersachsen)¶

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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 13:15:11 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Grafschaft Bentheim", weeks=5);
2023-03-07T13:15:22.458847 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 50 50 100 100 150 150 7-day incidence rate (per 100K people) 47.6 LK Grafschaft Bentheim, 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 daily change normalised per 100K 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 R & growth factor (based on cases) Germany-LK Grafschaft Bentheim cases daily growth factor Germany-LK Grafschaft Bentheim cases daily growth factor (rolling mean) Germany-LK Grafschaft Bentheim estimated R (using cases) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.0 0.0 0.5 0.5 1.0 1.0 R & growth factor (based on deaths) Germany-LK Grafschaft Bentheim deaths daily growth factor Germany-LK Grafschaft Bentheim deaths daily growth factor (rolling mean) Germany-LK Grafschaft Bentheim estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 2000 4000 6000 8000 cases doubling time [days] Germany-LK Grafschaft Bentheim doubling time cases (rolling mean) 0.00 13.87 27.74 41.62 daily change Germany-LK Grafschaft Bentheim new cases (rolling 7d mean) Germany-LK Grafschaft Bentheim new cases 0.000 0.277 0.555 0.832 daily change Germany-LK Grafschaft Bentheim new deaths (rolling 7d mean) Germany-LK Grafschaft Bentheim new deaths 0.000 0.217 0.435 0.652 0.870
In [4]:
overview(country="Germany", subregion="LK Grafschaft Bentheim");
2023-03-07T13:15:40.097949 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) 47.6 LK Grafschaft Bentheim, 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 1 2 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 R & growth factor (based on cases) Germany-LK Grafschaft Bentheim cases daily growth factor Germany-LK Grafschaft Bentheim cases daily growth factor (rolling mean) Germany-LK Grafschaft Bentheim estimated R (using cases) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0.0 0.0 0.5 0.5 1.0 1.0 R & growth factor (based on deaths) Germany-LK Grafschaft Bentheim deaths daily growth factor Germany-LK Grafschaft Bentheim deaths daily growth factor (rolling mean) Germany-LK Grafschaft Bentheim 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 Grafschaft Bentheim doubling time cases (rolling mean) Germany-LK Grafschaft Bentheim doubling time deaths (rolling mean) 0 277 555 832 daily change Germany-LK Grafschaft Bentheim new cases (rolling 7d mean) Germany-LK Grafschaft Bentheim new cases 0.000 1.387 2.774 daily change Germany-LK Grafschaft Bentheim new deaths (rolling 7d mean) Germany-LK Grafschaft Bentheim new deaths 0.0 35.3 70.6 106.0 141.3 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Grafschaft Bentheim", dates="2020-03-15:");
2023-03-07T13:17:57.358769 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 Grafschaft Bentheim LK Grafschaft Bentheim 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 Grafschaft Bentheim Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Grafschaft Bentheim")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Grafschaft Bentheim")
print(f'Population of country="Germany", subregion="LK Grafschaft Bentheim": {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 Grafschaft Bentheim": 138722 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 76034 11 226 0
2023-03-03 76023 4 226 0
2023-03-02 76019 11 226 0
2023-03-01 76008 19 226 0
2023-02-28 75989 21 226 0
2023-02-27 75968 33 226 0
2023-02-24 75935 24 226 0
2023-02-23 75911 29 226 0
2023-02-22 75882 35 226 0
2023-02-21 75847 41 226 0
2023-02-20 75806 38 226 0
2023-02-17 75768 32 226 0
2023-02-16 75736 24 226 0
2023-02-15 75712 18 226 0
2023-02-14 75694 25 226 0
2023-02-13 75669 30 226 0
2023-02-10 75639 14 226 0
2023-02-09 75625 22 226 0
2023-02-08 75603 15 226 0
2023-02-07 75588 24 226 0
2023-02-06 75564 30 226 1
2023-02-03 75534 13 225 1
2023-02-02 75521 23 224 0
2023-02-01 75498 47 224 0
2023-01-31 75451 46 224 0
2023-01-30 75405 3 224 0
2023-01-29 75402 23 224 0
2023-01-28 75379 26 224 0
2023-01-27 75353 42 224 0
2023-01-26 75311 45 224 0
2023-01-25 75266 47 224 0
2023-01-24 75219 63 224 0
2023-01-23 75156 3 224 0
2023-01-22 75153 25 224 0
2023-01-21 75128 34 224 0
2023-01-20 75094 25 224 0
2023-01-19 75069 25 224 1
2023-01-18 75044 52 223 0
2023-01-17 74992 39 223 1
2023-01-16 74953 5 222 0
2023-01-15 74948 13 222 0
2023-01-14 74935 23 222 0
2023-01-13 74912 19 222 0
2023-01-12 74893 40 222 0
2023-01-11 74853 42 222 0
2023-01-10 74811 57 222 0
2023-01-09 74754 25 222 1
2023-01-08 74729 33 221 0
2023-01-07 74696 44 221 0
2023-01-06 74652 78 221 0
2023-01-05 74574 86 221 0
2023-01-04 74488 83 221 0
2023-01-03 74405 96 221 1
2023-01-02 74309 35 220 0
2023-01-01 74274 14 220 0
2022-12-31 74260 75 220 1
2022-12-30 74185 66 219 0
2022-12-29 74119 87 219 0
2022-12-28 74032 82 219 0
2022-12-27 73950 55 219 0
2022-12-26 73895 24 219 0
2022-12-25 73871 65 219 0
2022-12-24 73806 92 219 0
2022-12-23 73714 91 219 1
2022-12-22 73623 112 218 1
2022-12-21 73511 129 217 0
2022-12-20 73382 117 217 0
2022-12-19 73265 22 217 1
2022-12-18 73243 66 216 0
2022-12-17 73177 89 216 0
2022-12-16 73088 120 216 2
2022-12-15 72968 117 214 0
2022-12-14 72851 108 214 0
2022-12-13 72743 107 214 0
2022-12-12 72636 107 214 0
2022-12-10 72529 96 214 0
2022-12-09 72433 105 214 1
2022-12-08 72328 96 213 1
2022-12-07 72232 127 212 0
2022-12-06 72105 124 212 0
2022-12-05 71981 41 212 0
2022-12-04 71940 44 212 1
2022-12-03 71896 91 211 0
2022-12-02 71805 94 211 0
2022-12-01 71711 85 211 0
2022-11-30 71626 102 211 0
2022-11-29 71524 115 211 0
2022-11-28 71409 36 211 0
2022-11-27 71373 56 211 1
2022-11-26 71317 79 210 0
2022-11-25 71238 92 210 0
2022-11-24 71146 57 210 0
2022-11-23 71089 106 210 0
2022-11-22 70983 126 210 0
2022-11-21 70857 10 210 0
2022-11-20 70847 20 210 0
2022-11-19 70827 61 210 0
2022-11-18 70766 68 210 0
2022-11-17 70698 66 210 0
2022-11-16 70632 89 210 1
2022-11-15 70543 77 209 0
2022-11-14 70466 22 209 0
2022-11-13 70444 38 209 0
2022-11-12 70406 38 209 0
2022-11-11 70368 76 209 0
2022-11-10 70292 96 209 0
2022-11-09 70196 78 209 1
2022-11-08 70118 81 208 0
2022-11-07 70037 23 208 0
2022-11-06 70014 42 208 0
2022-11-05 69972 48 208 0
2022-11-04 69924 59 208 0
2022-11-03 69865 133 208 0
2022-11-02 69732 175 208 3
2022-11-01 69557 37 205 1
2022-10-31 69520 25 204 1
2022-10-30 69495 42 203 0
2022-10-29 69453 55 203 0
2022-10-28 69398 106 203 0
2022-10-27 69292 95 203 0
2022-10-26 69197 134 203 0
2022-10-25 69063 123 203 0
2022-10-24 68940 43 203 0
2022-10-23 68897 74 203 0
2022-10-22 68823 131 203 0
2022-10-21 68692 107 203 1
2022-10-20 68585 105 202 1
2022-10-19 68480 207 201 0
2022-10-18 68273 160 201 2
2022-10-17 68113 38 199 2
2022-10-16 68075 86 197 0
2022-10-15 67989 126 197 0
2022-10-14 67863 152 197 1
2022-10-13 67711 182 196 0
2022-10-12 67529 220 196 0
2022-10-11 67309 138 196 0
2022-10-10 67171 30 196 0
2022-10-09 67141 141 196 0
2022-10-08 67000 193 196 0
2022-10-07 66807 231 196 1
2022-10-06 66576 240 195 0
2022-10-05 66336 104 195 0
2022-10-04 66232 35 195 0
2022-10-03 66197 10 195 0
2022-10-02 66187 65 195 0
2022-10-01 66122 82 195 0
2022-09-30 66040 95 195 0
2022-09-29 65945 118 195 0
2022-09-28 65827 114 195 0
2022-09-27 65713 103 195 0
2022-09-26 65610 15 195 0
2022-09-25 65595 49 195 0
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2022-09-23 65474 76 195 0
2022-09-22 65398 65 195 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: 0:03:18.234377