Germany: LK Main-Tauber-Kreis (Baden-Württemberg)¶

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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 10:01:02 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Main-Tauber-Kreis", weeks=5);
2023-03-07T10:01:19.527822 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 80 80 100 100 120 120 7-day incidence rate (per 100K people) 73.7 LK Main-Tauber-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 10 20 30 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 Main-Tauber-Kreis cases daily growth factor Germany-LK Main-Tauber-Kreis cases daily growth factor (rolling mean) Germany-LK Main-Tauber-Kreis estimated R (using cases) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 0 2 2 4 4 6 6 8 8 R & growth factor (based on deaths) Germany-LK Main-Tauber-Kreis deaths daily growth factor Germany-LK Main-Tauber-Kreis deaths daily growth factor (rolling mean) Germany-LK Main-Tauber-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 Main-Tauber-Kreis doubling time cases (rolling mean) 0.00 13.30 26.59 39.89 daily change Germany-LK Main-Tauber-Kreis new cases (rolling 7d mean) Germany-LK Main-Tauber-Kreis new cases 0.000 0.665 1.330 1.994 daily change Germany-LK Main-Tauber-Kreis new deaths (rolling 7d mean) Germany-LK Main-Tauber-Kreis new deaths 0.000 0.389 0.778
In [4]:
overview(country="Germany", subregion="LK Main-Tauber-Kreis");
2023-03-07T10:01:36.104755 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) 73.7 LK Main-Tauber-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 0 200 400 600 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 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 Main-Tauber-Kreis cases daily growth factor Germany-LK Main-Tauber-Kreis cases daily growth factor (rolling mean) Germany-LK Main-Tauber-Kreis estimated R (using cases) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0 0 2 2 4 4 6 6 8 8 R & growth factor (based on deaths) Germany-LK Main-Tauber-Kreis deaths daily growth factor Germany-LK Main-Tauber-Kreis deaths daily growth factor (rolling mean) Germany-LK Main-Tauber-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 8000 cases doubling time [days] Germany-LK Main-Tauber-Kreis doubling time cases (rolling mean) Germany-LK Main-Tauber-Kreis doubling time deaths (rolling mean) 0 266 532 798 daily change Germany-LK Main-Tauber-Kreis new cases (rolling 7d mean) Germany-LK Main-Tauber-Kreis new cases 0.000 2.659 5.318 daily change Germany-LK Main-Tauber-Kreis new deaths (rolling 7d mean) Germany-LK Main-Tauber-Kreis new deaths 0.0 50.1 100.2 150.3 200.4 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Main-Tauber-Kreis", dates="2020-03-15:");
2023-03-07T10:02:49.107865 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 Main-Tauber-Kreis LK Main-Tauber-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 Main-Tauber-Kreis Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Main-Tauber-Kreis")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Main-Tauber-Kreis")
print(f'Population of country="Germany", subregion="LK Main-Tauber-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 Main-Tauber-Kreis": 132956 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 61785 16 256 0
2023-03-03 61769 6 256 0
2023-03-02 61763 13 256 0
2023-03-01 61750 26 256 0
2023-02-28 61724 37 256 0
2023-02-27 61687 50 256 1
2023-02-24 61637 17 255 0
2023-02-23 61620 39 255 0
2023-02-22 61581 12 255 0
2023-02-21 61569 34 255 1
2023-02-20 61535 40 254 1
2023-02-18 61495 1 253 0
2023-02-17 61494 19 253 0
2023-02-16 61475 34 253 0
2023-02-15 61441 23 253 0
2023-02-14 61418 43 253 0
2023-02-13 61375 36 253 0
2023-02-10 61339 19 253 0
2023-02-09 61320 24 253 1
2023-02-08 61296 28 252 0
2023-02-07 61268 34 252 0
2023-02-06 61234 41 252 0
2023-02-03 61193 27 252 0
2023-02-02 61166 15 252 2
2023-02-01 61151 26 250 0
2023-01-31 61125 25 250 0
2023-01-30 61100 32 250 0
2023-01-27 61068 9 250 0
2023-01-26 61059 23 250 0
2023-01-25 61036 20 250 1
2023-01-24 61016 13 249 1
2023-01-23 61003 19 248 1
2023-01-20 60984 5 247 0
2023-01-19 60979 11 247 0
2023-01-18 60968 11 247 0
2023-01-17 60957 15 247 1
2023-01-16 60942 10 246 0
2023-01-13 60932 9 246 0
2023-01-12 60923 7 246 0
2023-01-11 60916 13 246 1
2023-01-10 60903 35 245 1
2023-01-09 60868 16 244 0
2023-01-06 60852 2 244 1
2023-01-05 60850 13 243 0
2023-01-04 60837 14 243 0
2023-01-03 60823 23 243 0
2023-01-02 60800 31 243 0
2022-12-30 60769 20 243 0
2022-12-29 60749 26 243 0
2022-12-28 60723 35 243 0
2022-12-27 60688 35 243 0
2022-12-26 60653 2 243 0
2022-12-25 60651 1 243 1
2022-12-23 60650 36 242 0
2022-12-22 60614 37 242 0
2022-12-21 60577 31 242 0
2022-12-20 60546 39 242 2
2022-12-19 60507 46 240 1
2022-12-16 60461 34 239 1
2022-12-15 60427 27 238 1
2022-12-14 60400 21 237 0
2022-12-13 60379 27 237 1
2022-12-12 60352 24 236 0
2022-12-09 60328 21 236 0
2022-12-08 60307 26 236 0
2022-12-07 60281 35 236 2
2022-12-06 60246 19 234 0
2022-12-05 60227 43 234 2
2022-12-02 60184 20 232 0
2022-12-01 60164 21 232 0
2022-11-30 60143 45 232 0
2022-11-29 60098 19 232 0
2022-11-28 60079 34 232 1
2022-11-27 60045 1 231 0
2022-11-25 60044 12 231 0
2022-11-24 60032 17 231 0
2022-11-23 60015 30 231 1
2022-11-22 59985 54 230 0
2022-11-21 59931 7 230 0
2022-11-18 59924 16 230 0
2022-11-17 59908 21 230 0
2022-11-16 59887 40 230 0
2022-11-15 59847 43 230 1
2022-11-14 59804 43 229 0
2022-11-11 59761 16 229 0
2022-11-10 59745 51 229 0
2022-11-09 59694 43 229 0
2022-11-08 59651 68 229 1
2022-11-07 59583 59 228 0
2022-11-04 59524 44 228 1
2022-11-03 59480 35 227 1
2022-11-02 59445 105 226 0
2022-10-31 59340 81 226 1
2022-10-28 59259 42 225 0
2022-10-27 59217 108 225 1
2022-10-26 59109 100 224 0
2022-10-25 59009 123 224 0
2022-10-24 58886 210 224 0
2022-10-23 58676 5 224 0
2022-10-22 58671 7 224 0
2022-10-21 58664 98 224 1
2022-10-20 58566 92 223 0
2022-10-19 58474 150 223 1
2022-10-18 58324 182 222 0
2022-10-17 58142 272 222 1
2022-10-16 57870 6 221 0
2022-10-15 57864 8 221 0
2022-10-14 57856 98 221 1
2022-10-13 57758 221 220 1
2022-10-12 57537 337 219 1
2022-10-11 57200 136 218 0
2022-10-10 57064 365 218 0
2022-10-09 56699 18 218 0
2022-10-08 56681 19 218 0
2022-10-07 56662 174 218 0
2022-10-06 56488 365 218 0
2022-10-05 56123 185 218 1
2022-10-04 55938 419 217 2
2022-10-03 55519 8 215 0
2022-10-02 55511 12 215 0
2022-10-01 55499 37 215 0
2022-09-30 55462 159 215 0
2022-09-29 55303 169 215 0
2022-09-28 55134 280 215 0
2022-09-27 54854 191 215 0
2022-09-26 54663 112 215 1
2022-09-23 54551 66 214 0
2022-09-22 54485 113 214 0
2022-09-21 54372 103 214 0
2022-09-20 54269 93 214 0
2022-09-19 54176 102 214 0
2022-09-16 54074 59 214 0
2022-09-15 54015 80 214 0
2022-09-14 53935 79 214 1
2022-09-13 53856 109 213 0
2022-09-12 53747 116 213 0
2022-09-10 53631 1 213 0
2022-09-09 53630 41 213 0
2022-09-08 53589 47 213 0
2022-09-07 53542 54 213 0
2022-09-06 53488 60 213 0
2022-09-05 53428 88 213 0
2022-09-04 53340 5 213 0
2022-09-03 53335 2 213 0
2022-09-02 53333 20 213 0
2022-09-01 53313 47 213 0
2022-08-31 53266 55 213 0
2022-08-30 53211 55 213 0
2022-08-29 53156 76 213 0
2022-08-26 53080 29 213 0
2022-08-25 53051 48 213 0
2022-08-24 53003 73 213 0
2022-08-23 52930 72 213 0
2022-08-22 52858 103 213 0
2022-08-19 52755 22 213 0
2022-08-18 52733 62 213 0
2022-08-17 52671 79 213 0
2022-08-16 52592 62 213 0
2022-08-15 52530 85 213 1
2022-08-12 52445 39 212 0
2022-08-11 52406 66 212 0
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2022-08-08 52174 146 212 1
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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: 0:02:03.058333