Germany: LK Weilheim-Schongau (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 11:29:13 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Weilheim-Schongau", weeks=5);
2023-03-07T11:29:25.559385 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 80 80 100 100 120 120 140 140 7-day incidence rate (per 100K people) 76.1 LK Weilheim-Schongau, 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 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 Weilheim-Schongau cases daily growth factor Germany-LK Weilheim-Schongau cases daily growth factor (rolling mean) Germany-LK Weilheim-Schongau estimated R (using cases) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 0 1 1 2 2 3 3 4 4 R & growth factor (based on deaths) Germany-LK Weilheim-Schongau deaths daily growth factor Germany-LK Weilheim-Schongau deaths daily growth factor (rolling mean) Germany-LK Weilheim-Schongau 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 Weilheim-Schongau doubling time cases (rolling mean) 0.00 13.66 27.33 40.99 daily change Germany-LK Weilheim-Schongau new cases (rolling 7d mean) Germany-LK Weilheim-Schongau new cases 0.000 0.683 1.366 daily change Germany-LK Weilheim-Schongau new deaths (rolling 7d mean) Germany-LK Weilheim-Schongau new deaths 0.000 0.365 0.730
In [4]:
overview(country="Germany", subregion="LK Weilheim-Schongau");
2023-03-07T11:29:42.692086 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) 76.1 LK Weilheim-Schongau, 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 0.8 0.8 1.0 1.0 1.2 1.2 R & growth factor (based on cases) Germany-LK Weilheim-Schongau cases daily growth factor Germany-LK Weilheim-Schongau cases daily growth factor (rolling mean) Germany-LK Weilheim-Schongau estimated R (using cases) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0 0 1 1 2 2 3 3 4 4 R & growth factor (based on deaths) Germany-LK Weilheim-Schongau deaths daily growth factor Germany-LK Weilheim-Schongau deaths daily growth factor (rolling mean) Germany-LK Weilheim-Schongau 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 cases doubling time [days] Germany-LK Weilheim-Schongau doubling time cases (rolling mean) Germany-LK Weilheim-Schongau doubling time deaths (rolling mean) 0 273 547 820 daily change Germany-LK Weilheim-Schongau new cases (rolling 7d mean) Germany-LK Weilheim-Schongau new cases 0.000 1.366 2.733 4.099 5.466 daily change Germany-LK Weilheim-Schongau new deaths (rolling 7d mean) Germany-LK Weilheim-Schongau new deaths 0.0 61.4 122.8 184.2 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Weilheim-Schongau", dates="2020-03-15:");
2023-03-07T11:32:25.813114 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 Weilheim-Schongau LK Weilheim-Schongau 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 Weilheim-Schongau Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Weilheim-Schongau")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Weilheim-Schongau")
print(f'Population of country="Germany", subregion="LK Weilheim-Schongau": {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 Weilheim-Schongau": 136642 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 70157 22 241 0
2023-03-03 70135 8 241 0
2023-03-02 70127 13 241 0
2023-03-01 70114 27 241 0
2023-02-28 70087 34 241 0
2023-02-27 70053 45 241 0
2023-02-24 70008 35 241 0
2023-02-23 69973 31 241 0
2023-02-22 69942 25 241 0
2023-02-21 69917 40 241 0
2023-02-20 69877 44 241 1
2023-02-19 69833 1 240 0
2023-02-17 69832 28 240 0
2023-02-16 69804 35 240 2
2023-02-15 69769 27 238 0
2023-02-14 69742 46 238 0
2023-02-13 69696 39 238 0
2023-02-10 69657 21 238 0
2023-02-09 69636 36 238 0
2023-02-08 69600 52 238 0
2023-02-07 69548 40 238 0
2023-02-06 69508 34 238 0
2023-02-03 69474 26 238 0
2023-02-02 69448 37 238 0
2023-02-01 69411 27 238 0
2023-01-31 69384 43 238 0
2023-01-30 69341 28 238 0
2023-01-27 69313 16 238 0
2023-01-26 69297 16 238 0
2023-01-25 69281 12 238 0
2023-01-24 69269 18 238 0
2023-01-23 69251 27 238 0
2023-01-20 69224 12 238 0
2023-01-19 69212 11 238 0
2023-01-18 69201 13 238 0
2023-01-17 69188 24 238 0
2023-01-16 69164 22 238 1
2023-01-13 69142 4 237 0
2023-01-12 69138 11 237 0
2023-01-11 69127 36 237 0
2023-01-10 69091 39 237 0
2023-01-09 69052 40 237 0
2023-01-05 69012 37 237 0
2023-01-04 68975 18 237 0
2023-01-03 68957 43 237 1
2023-01-02 68914 42 236 1
2022-12-30 68872 12 235 0
2022-12-29 68860 25 235 0
2022-12-28 68835 55 235 0
2022-12-27 68780 50 235 1
2022-12-23 68730 25 234 0
2022-12-22 68705 17 234 0
2022-12-21 68688 31 234 0
2022-12-20 68657 46 234 1
2022-12-19 68611 49 233 1
2022-12-16 68562 26 232 0
2022-12-15 68536 43 232 3
2022-12-14 68493 32 229 0
2022-12-13 68461 31 229 0
2022-12-12 68430 52 229 0
2022-12-11 68378 1 229 0
2022-12-09 68377 15 229 0
2022-12-08 68362 27 229 1
2022-12-07 68335 30 228 1
2022-12-06 68305 38 227 0
2022-12-05 68267 30 227 0
2022-12-02 68237 31 227 0
2022-12-01 68206 23 227 0
2022-11-30 68183 32 227 0
2022-11-29 68151 50 227 0
2022-11-28 68101 33 227 2
2022-11-26 68068 1 225 0
2022-11-25 68067 36 225 0
2022-11-24 68031 25 225 0
2022-11-23 68006 24 225 0
2022-11-22 67982 50 225 0
2022-11-21 67932 32 225 0
2022-11-18 67900 20 225 1
2022-11-17 67880 27 224 1
2022-11-16 67853 31 223 0
2022-11-15 67822 50 223 0
2022-11-14 67772 22 223 0
2022-11-11 67750 20 223 0
2022-11-10 67730 24 223 0
2022-11-09 67706 23 223 0
2022-11-08 67683 48 223 0
2022-11-07 67635 60 223 0
2022-11-04 67575 41 223 1
2022-11-03 67534 58 222 0
2022-11-02 67476 77 222 0
2022-10-31 67399 69 222 0
2022-10-29 67330 1 222 0
2022-10-28 67329 48 222 0
2022-10-27 67281 84 222 0
2022-10-26 67197 118 222 0
2022-10-25 67079 154 222 0
2022-10-24 66925 191 222 0
2022-10-23 66734 2 222 1
2022-10-21 66732 132 221 1
2022-10-20 66600 182 220 0
2022-10-19 66418 248 220 0
2022-10-18 66170 298 220 0
2022-10-17 65872 308 220 2
2022-10-14 65564 242 218 1
2022-10-13 65322 541 217 0
2022-10-12 64781 483 217 2
2022-10-11 64298 413 215 1
2022-10-10 63885 351 214 2
2022-10-08 63534 12 212 0
2022-10-07 63522 391 212 0
2022-10-06 63131 388 212 2
2022-10-05 62743 310 210 0
2022-10-04 62433 330 210 2
2022-10-03 62103 1 208 0
2022-10-02 62102 27 208 0
2022-10-01 62075 1 208 0
2022-09-30 62074 222 208 0
2022-09-29 61852 211 208 0
2022-09-28 61641 252 208 1
2022-09-27 61389 339 207 0
2022-09-26 61050 224 207 0
2022-09-25 60826 1 207 0
2022-09-24 60825 1 207 0
2022-09-23 60824 122 207 0
2022-09-22 60702 103 207 0
2022-09-21 60599 99 207 0
2022-09-20 60500 138 207 0
2022-09-19 60362 78 207 0
2022-09-17 60284 1 207 0
2022-09-16 60283 52 207 0
2022-09-15 60231 50 207 0
2022-09-14 60181 62 207 0
2022-09-13 60119 68 207 0
2022-09-12 60051 66 207 0
2022-09-10 59985 1 207 0
2022-09-09 59984 46 207 0
2022-09-08 59938 57 207 0
2022-09-07 59881 60 207 0
2022-09-06 59821 72 207 0
2022-09-05 59749 85 207 0
2022-09-03 59664 2 207 0
2022-09-02 59662 48 207 0
2022-09-01 59614 37 207 0
2022-08-31 59577 45 207 0
2022-08-30 59532 90 207 0
2022-08-29 59442 85 207 1
2022-08-27 59357 1 206 0
2022-08-26 59356 43 206 0
2022-08-25 59313 40 206 1
2022-08-24 59273 78 205 0
2022-08-23 59195 48 205 0
2022-08-22 59147 49 205 0
2022-08-20 59098 19 205 0
2022-08-19 59079 42 205 0
2022-08-18 59037 53 205 0
2022-08-17 58984 121 205 0
2022-08-16 58863 95 205 0
2022-08-12 58768 79 205 0
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2022-08-06 58295 2 205 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: 0:03:29.030933