Germany: LK Enzkreis (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 09:44:09 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Enzkreis", weeks=5);
2023-03-07T09:44:23.683950 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) 59.9 LK Enzkreis, 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 40 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 Enzkreis new deaths (rolling 7d mean) Germany-LK Enzkreis new deaths 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 Enzkreis cases daily growth factor Germany-LK Enzkreis cases daily growth factor (rolling mean) Germany-LK Enzkreis 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 Enzkreis deaths daily growth factor Germany-LK Enzkreis deaths daily growth factor (rolling mean) Germany-LK Enzkreis 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 Enzkreis doubling time cases (rolling mean) 0.0 20.0 40.0 60.1 80.1 daily change Germany-LK Enzkreis new cases (rolling 7d mean) Germany-LK Enzkreis new cases 0.000 0.312 0.624 0.937
In [4]:
overview(country="Germany", subregion="LK Enzkreis");
2023-03-07T09:44:42.979683 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) 59.9 LK Enzkreis, 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 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 Enzkreis cases daily growth factor Germany-LK Enzkreis cases daily growth factor (rolling mean) Germany-LK Enzkreis 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 Enzkreis deaths daily growth factor Germany-LK Enzkreis deaths daily growth factor (rolling mean) Germany-LK Enzkreis 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 Enzkreis doubling time cases (rolling mean) Germany-LK Enzkreis doubling time deaths (rolling mean) 0 400 801 daily change Germany-LK Enzkreis new cases (rolling 7d mean) Germany-LK Enzkreis new cases 0.00 4.00 8.01 daily change Germany-LK Enzkreis new deaths (rolling 7d mean) Germany-LK Enzkreis new deaths 0.0 91.0 181.9 272.9 363.9 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Enzkreis", dates="2020-03-15:");
2023-03-07T09:47:01.438665 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 Enzkreis LK Enzkreis 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 Enzkreis Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Enzkreis")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Enzkreis")
print(f'Population of country="Germany", subregion="LK Enzkreis": {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 Enzkreis": 200237 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 95376 17 359 0
2023-03-03 95359 8 359 0
2023-03-02 95351 13 359 0
2023-03-01 95338 41 359 0
2023-02-28 95297 41 359 0
2023-02-27 95256 93 359 0
2023-02-24 95163 41 359 0
2023-02-23 95122 37 359 0
2023-02-22 95085 42 359 0
2023-02-21 95043 47 359 0
2023-02-20 94996 51 359 0
2023-02-17 94945 26 359 0
2023-02-16 94919 33 359 0
2023-02-15 94886 36 359 0
2023-02-14 94850 47 359 0
2023-02-13 94803 45 359 0
2023-02-12 94758 1 359 0
2023-02-10 94757 36 359 0
2023-02-09 94721 21 359 0
2023-02-08 94700 43 359 0
2023-02-07 94657 36 359 0
2023-02-06 94621 30 359 0
2023-02-05 94591 10 359 0
2023-02-03 94581 20 359 0
2023-02-02 94561 24 359 0
2023-02-01 94537 32 359 0
2023-01-31 94505 25 359 0
2023-01-30 94480 47 359 0
2023-01-29 94433 1 359 0
2023-01-27 94432 12 359 0
2023-01-26 94420 15 359 0
2023-01-25 94405 12 359 0
2023-01-24 94393 29 359 0
2023-01-23 94364 12 359 0
2023-01-22 94352 19 359 0
2023-01-20 94333 12 359 0
2023-01-19 94321 17 359 0
2023-01-18 94304 21 359 0
2023-01-17 94283 21 359 0
2023-01-16 94262 37 359 0
2023-01-13 94225 33 359 0
2023-01-12 94192 15 359 0
2023-01-11 94177 38 359 0
2023-01-10 94139 34 359 0
2023-01-09 94105 64 359 0
2023-01-05 94041 33 359 0
2023-01-04 94008 43 359 0
2023-01-03 93965 72 359 0
2023-01-02 93893 38 359 0
2022-12-31 93855 28 359 0
2022-12-30 93827 31 359 1
2022-12-29 93796 33 358 0
2022-12-28 93763 102 358 0
2022-12-27 93661 105 358 1
2022-12-23 93556 90 357 0
2022-12-22 93466 64 357 0
2022-12-21 93402 75 357 0
2022-12-20 93327 99 357 0
2022-12-19 93228 113 357 1
2022-12-16 93115 58 356 0
2022-12-15 93057 49 356 0
2022-12-14 93008 79 356 0
2022-12-13 92929 101 356 0
2022-12-12 92828 125 356 0
2022-12-09 92703 72 356 0
2022-12-08 92631 60 356 0
2022-12-07 92571 81 356 0
2022-12-06 92490 85 356 0
2022-12-05 92405 104 356 0
2022-12-02 92301 54 356 0
2022-12-01 92247 62 356 0
2022-11-30 92185 74 356 0
2022-11-29 92111 77 356 0
2022-11-28 92034 74 356 0
2022-11-25 91960 33 356 0
2022-11-24 91927 56 356 0
2022-11-23 91871 72 356 0
2022-11-22 91799 66 356 0
2022-11-21 91733 78 356 0
2022-11-18 91655 55 356 0
2022-11-17 91600 62 356 0
2022-11-16 91538 77 356 0
2022-11-15 91461 90 356 0
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2022-11-11 91255 67 356 0
2022-11-10 91188 72 356 0
2022-11-09 91116 81 356 0
2022-11-08 91035 110 356 0
2022-11-07 90925 122 356 1
2022-11-04 90803 83 355 0
2022-11-03 90720 138 355 1
2022-11-02 90582 269 354 0
2022-10-31 90313 50 354 0
2022-10-28 90263 115 354 0
2022-10-27 90148 74 354 0
2022-10-26 90074 129 354 0
2022-10-25 89945 227 354 0
2022-10-24 89718 224 354 0
2022-10-23 89494 34 354 0
2022-10-22 89460 65 354 0
2022-10-21 89395 227 354 1
2022-10-20 89168 391 353 0
2022-10-19 88777 337 353 0
2022-10-18 88440 69 353 1
2022-10-17 88371 278 352 0
2022-10-16 88093 9 352 0
2022-10-15 88084 96 352 0
2022-10-14 87988 633 352 0
2022-10-13 87355 738 352 2
2022-10-12 86617 355 350 0
2022-10-11 86262 307 350 1
2022-10-10 85955 53 349 0
2022-10-09 85902 6 349 0
2022-10-08 85896 52 349 0
2022-10-07 85844 114 349 0
2022-10-06 85730 267 349 1
2022-10-05 85463 355 348 0
2022-10-04 85108 425 348 0
2022-10-03 84683 77 348 0
2022-10-02 84606 62 348 1
2022-10-01 84544 141 347 0
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2022-09-29 84119 263 347 0
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2022-09-27 83610 271 347 0
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2022-09-25 83038 36 347 0
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2022-09-23 82897 169 347 0
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2022-09-21 82597 119 347 0
2022-09-20 82478 130 347 0
2022-09-19 82348 146 347 0
2022-09-18 82202 38 347 0
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2022-09-13 81888 126 347 0
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2022-09-11 81655 11 347 0
2022-09-10 81644 17 347 1
2022-09-09 81627 49 346 0
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2022-09-07 81518 66 346 0
2022-09-06 81452 140 346 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: 0:04:35.893506