Germany: SK Weiden i.d.OPf. (Bayern)¶

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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 11:51:35 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="SK Weiden i.d.OPf.", weeks=5);
2023-03-07T11:51:48.875099 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 150 150 200 200 250 250 300 300 7-day incidence rate (per 100K people) 124.8 SK Weiden i.d.OPf., 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 0.5 1.0 1.5 2.0 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-SK Weiden i.d.OPf. cases daily growth factor Germany-SK Weiden i.d.OPf. cases daily growth factor (rolling mean) Germany-SK Weiden i.d.OPf. 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-SK Weiden i.d.OPf. deaths daily growth factor Germany-SK Weiden i.d.OPf. deaths daily growth factor (rolling mean) Germany-SK Weiden i.d.OPf. estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 1000 2000 3000 cases doubling time [days] Germany-SK Weiden i.d.OPf. doubling time cases (rolling mean) 0.00 8.49 16.99 25.48 33.98 daily change Germany-SK Weiden i.d.OPf. new cases (rolling 7d mean) Germany-SK Weiden i.d.OPf. new cases 0.000 0.212 0.425 0.637 0.849 daily change Germany-SK Weiden i.d.OPf. new deaths (rolling 7d mean) Germany-SK Weiden i.d.OPf. new deaths 0.000 0.291 0.581 0.872
In [4]:
overview(country="Germany", subregion="SK Weiden i.d.OPf.");
2023-03-07T11:52:06.718403 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) 124.8 SK Weiden i.d.OPf., 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 0.0 2.5 5.0 7.5 10.0 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-SK Weiden i.d.OPf. cases daily growth factor Germany-SK Weiden i.d.OPf. cases daily growth factor (rolling mean) Germany-SK Weiden i.d.OPf. 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-SK Weiden i.d.OPf. deaths daily growth factor Germany-SK Weiden i.d.OPf. deaths daily growth factor (rolling mean) Germany-SK Weiden i.d.OPf. estimated R (using deaths) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0 1000 2000 3000 4000 cases doubling time [days] Germany-SK Weiden i.d.OPf. doubling time cases (rolling mean) Germany-SK Weiden i.d.OPf. doubling time deaths (rolling mean) 0.0 84.9 169.9 254.8 daily change Germany-SK Weiden i.d.OPf. new cases (rolling 7d mean) Germany-SK Weiden i.d.OPf. new cases 0.000 1.062 2.124 3.185 4.247 daily change Germany-SK Weiden i.d.OPf. new deaths (rolling 7d mean) Germany-SK Weiden i.d.OPf. new deaths 0.00 19.78 39.57 59.35 79.14 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="SK Weiden i.d.OPf.", dates="2020-03-15:");
2023-03-07T11:54:07.694300 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: SK Weiden i.d.OPf. SK Weiden i.d.OPf. 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 SK Weiden i.d.OPf. Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="SK Weiden i.d.OPf.")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="SK Weiden i.d.OPf.")
print(f'Population of country="Germany", subregion="SK Weiden i.d.OPf.": {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="SK Weiden i.d.OPf.": 42472 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 23043 1 136 0
2023-03-05 23042 1 136 0
2023-03-04 23041 3 136 0
2023-03-03 23038 9 136 0
2023-03-02 23029 6 136 0
2023-03-01 23023 4 136 0
2023-02-28 23019 29 136 0
2023-02-27 22990 18 136 0
2023-02-26 22972 2 136 0
2023-02-25 22970 3 136 0
2023-02-24 22967 18 136 0
2023-02-23 22949 13 136 0
2023-02-22 22936 16 136 0
2023-02-21 22920 37 136 0
2023-02-20 22883 12 136 0
2023-02-19 22871 6 136 0
2023-02-18 22865 1 136 0
2023-02-17 22864 26 136 0
2023-02-16 22838 27 136 0
2023-02-15 22811 22 136 0
2023-02-14 22789 28 136 0
2023-02-13 22761 17 136 0
2023-02-12 22744 10 136 0
2023-02-11 22734 9 136 0
2023-02-10 22725 14 136 0
2023-02-09 22711 26 136 0
2023-02-08 22685 18 136 0
2023-02-07 22667 20 136 0
2023-02-06 22647 35 136 0
2023-02-05 22612 6 136 0
2023-02-04 22606 7 136 0
2023-02-03 22599 18 136 1
2023-02-02 22581 23 135 0
2023-02-01 22558 28 135 0
2023-01-31 22530 26 135 0
2023-01-30 22504 22 135 0
2023-01-29 22482 11 135 0
2023-01-28 22471 4 135 0
2023-01-27 22467 19 135 0
2023-01-26 22448 12 135 0
2023-01-25 22436 15 135 0
2023-01-24 22421 19 135 0
2023-01-23 22402 20 135 0
2023-01-22 22382 1 135 0
2023-01-21 22381 4 135 0
2023-01-20 22377 11 135 0
2023-01-19 22366 17 135 0
2023-01-18 22349 11 135 0
2023-01-17 22338 14 135 0
2023-01-16 22324 21 135 0
2023-01-15 22303 2 135 0
2023-01-14 22301 5 135 0
2023-01-13 22296 5 135 0
2023-01-12 22291 12 135 0
2023-01-11 22279 4 135 0
2023-01-10 22275 12 135 0
2023-01-09 22263 11 135 0
2023-01-08 22252 4 135 0
2023-01-07 22248 5 135 0
2023-01-06 22243 1 135 0
2023-01-05 22242 23 135 0
2023-01-04 22219 3 135 0
2023-01-03 22216 12 135 0
2023-01-02 22204 5 135 0
2023-01-01 22199 2 135 0
2022-12-31 22197 2 135 0
2022-12-30 22195 14 135 0
2022-12-29 22181 16 135 0
2022-12-28 22165 17 135 0
2022-12-27 22148 17 135 0
2022-12-26 22131 6 135 0
2022-12-25 22125 2 135 0
2022-12-24 22123 9 135 0
2022-12-23 22114 7 135 0
2022-12-22 22107 18 135 0
2022-12-21 22089 13 135 0
2022-12-20 22076 11 135 0
2022-12-19 22065 15 135 0
2022-12-18 22050 1 135 0
2022-12-17 22049 4 135 0
2022-12-16 22045 8 135 0
2022-12-15 22037 14 135 0
2022-12-14 22023 6 135 0
2022-12-13 22017 10 135 0
2022-12-12 22007 18 135 0
2022-12-11 21989 1 135 0
2022-12-10 21988 8 135 0
2022-12-09 21980 11 135 0
2022-12-08 21969 7 135 0
2022-12-07 21962 13 135 0
2022-12-06 21949 17 135 0
2022-12-05 21932 14 135 0
2022-12-04 21918 4 135 0
2022-12-03 21914 2 135 0
2022-12-02 21912 7 135 0
2022-12-01 21905 4 135 0
2022-11-30 21901 9 135 0
2022-11-29 21892 9 135 0
2022-11-28 21883 8 135 0
2022-11-26 21875 5 135 0
2022-11-25 21870 11 135 0
2022-11-24 21859 9 135 0
2022-11-23 21850 8 135 0
2022-11-22 21842 14 135 0
2022-11-21 21828 9 135 0
2022-11-20 21819 2 135 0
2022-11-18 21817 12 135 0
2022-11-17 21805 4 135 0
2022-11-16 21801 10 135 0
2022-11-15 21791 11 135 0
2022-11-14 21780 17 135 0
2022-11-12 21763 10 135 0
2022-11-11 21753 3 135 0
2022-11-10 21750 9 135 0
2022-11-09 21741 25 135 0
2022-11-08 21716 19 135 0
2022-11-07 21697 30 135 0
2022-11-06 21667 5 135 0
2022-11-05 21662 10 135 0
2022-11-04 21652 22 135 0
2022-11-03 21630 23 135 0
2022-11-02 21607 37 135 0
2022-11-01 21570 12 135 0
2022-10-31 21558 28 135 0
2022-10-30 21530 9 135 0
2022-10-29 21521 11 135 0
2022-10-28 21510 40 135 0
2022-10-27 21470 37 135 0
2022-10-26 21433 30 135 0
2022-10-25 21403 38 135 0
2022-10-24 21365 64 135 0
2022-10-23 21301 10 135 0
2022-10-22 21291 41 135 1
2022-10-21 21250 41 134 0
2022-10-20 21209 46 134 0
2022-10-19 21163 91 134 0
2022-10-18 21072 81 134 0
2022-10-17 20991 70 134 0
2022-10-16 20921 30 134 1
2022-10-15 20891 18 133 0
2022-10-14 20873 95 133 0
2022-10-13 20778 57 133 0
2022-10-12 20721 91 133 0
2022-10-11 20630 97 133 0
2022-10-10 20533 143 133 0
2022-10-09 20390 12 133 0
2022-10-08 20378 33 133 0
2022-10-07 20345 69 133 0
2022-10-06 20276 94 133 0
2022-10-05 20182 105 133 0
2022-10-04 20077 99 133 0
2022-10-03 19978 20 133 0
2022-10-02 19958 31 133 0
2022-10-01 19927 18 133 0
2022-09-30 19909 80 133 0
2022-09-29 19829 44 133 0
2022-09-28 19785 76 133 0
2022-09-27 19709 63 133 0
2022-09-26 19646 74 133 0
2022-09-25 19572 14 133 1
2022-09-24 19558 11 132 0
2022-09-23 19547 47 132 0
2022-09-22 19500 26 132 0
2022-09-21 19474 31 132 0
2022-09-20 19443 43 132 0
2022-09-19 19400 48 132 0
2022-09-18 19352 9 132 0
2022-09-17 19343 12 132 0
2022-09-16 19331 25 132 0
2022-09-15 19306 31 132 0
2022-09-14 19275 30 132 0
2022-09-13 19245 37 132 0
2022-09-12 19208 17 132 0
2022-09-11 19191 6 132 0
2022-09-10 19185 8 132 0
2022-09-09 19177 19 132 0
2022-09-08 19158 24 132 0
2022-09-07 19134 47 132 0
2022-09-06 19087 15 132 0
2022-09-05 19072 35 132 0
2022-09-04 19037 8 132 0
2022-09-03 19029 10 132 0
2022-09-02 19019 23 132 0
2022-09-01 18996 21 132 0
2022-08-31 18975 21 132 0
2022-08-30 18954 19 132 0
2022-08-29 18935 28 132 0
2022-08-28 18907 4 132 0
2022-08-27 18903 12 132 0
2022-08-26 18891 5 132 0
2022-08-25 18886 19 132 0
2022-08-24 18867 19 132 0
2022-08-23 18848 19 132 0
2022-08-22 18829 24 132 0
2022-08-21 18805 4 132 0
2022-08-20 18801 4 132 0
2022-08-19 18797 12 132 0
2022-08-18 18785 18 132 0
2022-08-17 18767 31 132 0
2022-08-16 18736 50 132 0
2022-08-15 18686 8 132 0
2022-08-14 18678 4 132 0
2022-08-13 18674 10 132 1
2022-08-12 18664 24 131 0
2022-08-11 18640 28 131 0
2022-08-10 18612 25 131 0
2022-08-09 18587 30 131 0
2022-08-08 18557 55 131 0
2022-08-07 18502 9 131 0
2022-08-06 18493 11 131 0
2022-08-05 18482 24 131 0
2022-08-04 18458 38 131 0
2022-08-03 18420 36 131 0
2022-08-02 18384 48 131 0
2022-08-01 18336 55 131 0
2022-07-31 18281 10 131 0
2022-07-30 18271 13 131 0
2022-07-29 18258 47 131 0
2022-07-28 18211 58 131 0
2022-07-27 18153 46 131 0
2022-07-26 18107 90 131 0
2022-07-25 18017 106 131 1
2022-07-24 17911 13 130 0
2022-07-23 17898 26 130 0
2022-07-22 17872 73 130 0
2022-07-21 17799 85 130 0
2022-07-20 17714 67 130 0
2022-07-19 17647 101 130 0
2022-07-18 17546 107 130 0
2022-07-17 17439 24 130 0
2022-07-16 17415 18 130 0
2022-07-15 17397 58 130 1
2022-07-14 17339 73 129 0
2022-07-13 17266 65 129 0
2022-07-12 17201 64 129 0
2022-07-11 17137 102 129 0
2022-07-10 17035 10 129 0
2022-07-09 17025 16 129 0
2022-07-08 17009 46 129 0
2022-07-07 16963 64 129 1
2022-07-06 16899 56 128 0
2022-07-05 16843 67 128 0
2022-07-04 16776 93 128 0
2022-07-03 16683 15 128 0
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2022-07-01 16649 55 128 0
2022-06-30 16594 52 128 0
2022-06-29 16542 50 128 0
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2022-06-26 16376 9 128 0
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2020-12-03 994 9 32 2
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2020-04-01 92 12 6 2
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2020-03-29 66 5 3 0
2020-03-28 61 6 3 1
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2020-03-25 40 9 2 1
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2020-03-21 20 9 1 1
2020-03-20 11 2 0 0
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2020-03-11 3 2 0 0

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: 0:02:43.410359