Germany: LK Rottweil (Baden-Württemberg)¶

  • Homepage of project: https://oscovida.github.io
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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 10:11:11 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Rottweil", weeks=5);
2023-03-07T10:11:28.097224 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) 61.2 LK Rottweil, 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 0.50 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 Rottweil cases daily growth factor Germany-LK Rottweil cases daily growth factor (rolling mean) Germany-LK Rottweil 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 Rottweil deaths daily growth factor Germany-LK Rottweil deaths daily growth factor (rolling mean) Germany-LK Rottweil estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 2000 4000 6000 cases doubling time [days] Germany-LK Rottweil doubling time cases (rolling mean) 0.00 14.04 28.09 42.13 daily change Germany-LK Rottweil new cases (rolling 7d mean) Germany-LK Rottweil new cases 0.000 0.281 0.562 0.843 daily change Germany-LK Rottweil new deaths (rolling 7d mean) Germany-LK Rottweil new deaths 0.000 0.329 0.658 0.986
In [4]:
overview(country="Germany", subregion="LK Rottweil");
2023-03-07T10:11:49.789011 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) 61.2 LK Rottweil, 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 3 4 daily change normalised per 100K May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0.50 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 Rottweil cases daily growth factor Germany-LK Rottweil cases daily growth factor (rolling mean) Germany-LK Rottweil 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 Rottweil deaths daily growth factor Germany-LK Rottweil deaths daily growth factor (rolling mean) Germany-LK Rottweil estimated R (using deaths) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0 2500 5000 7500 10000 cases doubling time [days] Germany-LK Rottweil doubling time cases (rolling mean) Germany-LK Rottweil doubling time deaths (rolling mean) 0 281 562 843 daily change Germany-LK Rottweil new cases (rolling 7d mean) Germany-LK Rottweil new cases 0.000 1.404 2.809 4.213 5.618 daily change Germany-LK Rottweil new deaths (rolling 7d mean) Germany-LK Rottweil new deaths 0.0 68.4 136.8 205.2 273.6 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Rottweil", dates="2020-03-15:");
2023-03-07T10:14:17.701385 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 Rottweil LK Rottweil 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 Rottweil Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Rottweil")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Rottweil")
print(f'Population of country="Germany", subregion="LK Rottweil": {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 Rottweil": 140446 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 67855 3 311 0
2023-03-05 67852 1 311 0
2023-03-04 67851 2 311 0
2023-03-03 67849 14 311 0
2023-03-02 67835 15 311 0
2023-03-01 67820 13 311 1
2023-02-28 67807 38 310 0
2023-02-27 67769 52 310 0
2023-02-26 67717 16 310 0
2023-02-25 67701 27 310 0
2023-02-24 67674 48 310 0
2023-02-23 67626 53 310 0
2023-02-22 67573 47 310 0
2023-02-21 67526 25 310 0
2023-02-20 67501 19 310 1
2023-02-19 67482 14 309 1
2023-02-18 67468 12 308 0
2023-02-17 67456 20 308 0
2023-02-16 67436 19 308 0
2023-02-15 67417 32 308 0
2023-02-14 67385 27 308 0
2023-02-13 67358 22 308 0
2023-02-12 67336 9 308 0
2023-02-11 67327 9 308 0
2023-02-10 67318 19 308 0
2023-02-09 67299 22 308 0
2023-02-08 67277 19 308 1
2023-02-07 67258 27 307 0
2023-02-06 67231 25 307 0
2023-02-05 67206 15 307 0
2023-02-04 67191 10 307 0
2023-02-03 67181 19 307 0
2023-02-02 67162 14 307 0
2023-02-01 67148 26 307 0
2023-01-31 67122 12 307 0
2023-01-30 67110 26 307 0
2023-01-29 67084 5 307 0
2023-01-28 67079 8 307 0
2023-01-27 67071 17 307 0
2023-01-26 67054 13 307 0
2023-01-25 67041 16 307 0
2023-01-24 67025 15 307 0
2023-01-23 67010 9 307 0
2023-01-22 67001 1 307 0
2023-01-21 67000 5 307 0
2023-01-20 66995 2 307 0
2023-01-19 66993 8 307 0
2023-01-18 66985 15 307 0
2023-01-17 66970 7 307 0
2023-01-16 66963 16 307 0
2023-01-15 66947 3 307 0
2023-01-14 66944 2 307 0
2023-01-13 66942 10 307 0
2023-01-12 66932 7 307 0
2023-01-11 66925 14 307 0
2023-01-10 66911 13 307 1
2023-01-09 66898 10 306 0
2023-01-08 66888 7 306 0
2023-01-07 66881 5 306 0
2023-01-06 66876 7 306 1
2023-01-05 66869 20 305 1
2023-01-04 66849 15 304 0
2023-01-03 66834 25 304 0
2023-01-02 66809 19 304 0
2023-01-01 66790 5 304 1
2022-12-31 66785 3 303 0
2022-12-30 66782 11 303 0
2022-12-29 66771 7 303 0
2022-12-28 66764 27 303 0
2022-12-27 66737 24 303 0
2022-12-26 66713 9 303 0
2022-12-25 66704 9 303 0
2022-12-24 66695 6 303 0
2022-12-23 66689 12 303 0
2022-12-22 66677 30 303 0
2022-12-21 66647 33 303 0
2022-12-20 66614 40 303 0
2022-12-19 66574 26 303 0
2022-12-18 66548 16 303 0
2022-12-17 66532 16 303 0
2022-12-16 66516 30 303 0
2022-12-15 66486 12 303 0
2022-12-14 66474 32 303 1
2022-12-13 66442 22 302 0
2022-12-12 66420 36 302 0
2022-12-11 66384 6 302 0
2022-12-10 66378 11 302 2
2022-12-09 66367 18 300 0
2022-12-08 66349 24 300 0
2022-12-07 66325 46 300 0
2022-12-06 66279 56 300 0
2022-12-05 66223 16 300 0
2022-12-04 66207 15 300 0
2022-12-03 66192 11 300 0
2022-12-02 66181 26 300 0
2022-12-01 66155 29 300 0
2022-11-30 66126 15 300 0
2022-11-29 66111 23 300 0
2022-11-28 66088 45 300 0
2022-11-27 66043 6 300 0
2022-11-26 66037 10 300 0
2022-11-25 66027 23 300 0
2022-11-24 66004 35 300 0
2022-11-23 65969 26 300 0
2022-11-22 65943 20 300 0
2022-11-21 65923 39 300 0
2022-11-20 65884 4 300 0
2022-11-19 65880 13 300 0
2022-11-18 65867 32 300 0
2022-11-17 65835 22 300 0
2022-11-16 65813 22 300 0
2022-11-15 65791 35 300 0
2022-11-14 65756 23 300 0
2022-11-13 65733 3 300 0
2022-11-12 65730 12 300 0
2022-11-11 65718 25 300 0
2022-11-10 65693 29 300 0
2022-11-09 65664 32 300 0
2022-11-08 65632 45 300 0
2022-11-07 65587 15 300 0
2022-11-06 65572 5 300 0
2022-11-05 65567 20 300 0
2022-11-04 65547 29 300 0
2022-11-03 65518 67 300 0
2022-11-02 65451 44 300 0
2022-11-01 65407 27 300 0
2022-10-31 65380 38 300 0
2022-10-30 65342 17 300 0
2022-10-29 65325 25 300 0
2022-10-28 65300 55 300 0
2022-10-27 65245 82 300 0
2022-10-26 65163 74 300 0
2022-10-25 65089 123 300 0
2022-10-24 64966 92 300 1
2022-10-23 64874 21 299 0
2022-10-22 64853 65 299 0
2022-10-21 64788 103 299 1
2022-10-20 64685 136 298 0
2022-10-19 64549 131 298 1
2022-10-18 64418 270 297 1
2022-10-17 64148 126 296 0
2022-10-16 64022 66 296 0
2022-10-15 63956 91 296 0
2022-10-14 63865 179 296 2
2022-10-13 63686 209 294 1
2022-10-12 63477 181 293 2
2022-10-11 63296 294 291 0
2022-10-10 63002 135 291 0
2022-10-09 62867 87 291 0
2022-10-08 62780 155 291 0
2022-10-07 62625 221 291 0
2022-10-06 62404 320 291 2
2022-10-05 62084 263 289 0
2022-10-04 61821 160 289 0
2022-10-03 61661 65 289 0
2022-10-02 61596 37 289 0
2022-10-01 61559 85 289 0
2022-09-30 61474 130 289 0
2022-09-29 61344 154 289 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: 0:03:23.492853