Germany: LK Mühldorf a.Inn (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 11:03:21 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Mühldorf a.Inn", weeks=5);
2023-03-07T11:03:31.033612 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 75 75 100 100 125 125 150 150 7-day incidence rate (per 100K people) 74.0 LK Mühldorf a.Inn, 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 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 0.6 0.6 0.8 0.8 1.0 1.0 1.2 1.2 R & growth factor (based on cases) Germany-LK Mühldorf a.Inn cases daily growth factor Germany-LK Mühldorf a.Inn cases daily growth factor (rolling mean) Germany-LK Mühldorf a.Inn estimated R (using cases) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 2 2 4 4 6 6 8 8 R & growth factor (based on deaths) Germany-LK Mühldorf a.Inn deaths daily growth factor Germany-LK Mühldorf a.Inn deaths daily growth factor (rolling mean) Germany-LK Mühldorf a.Inn 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 Mühldorf a.Inn doubling time cases (rolling mean) 0.00 23.52 47.04 70.56 daily change Germany-LK Mühldorf a.Inn new cases (rolling 7d mean) Germany-LK Mühldorf a.Inn new cases 0.000 0.588 1.176 1.764 daily change Germany-LK Mühldorf a.Inn new deaths (rolling 7d mean) Germany-LK Mühldorf a.Inn new deaths 0.000 0.319 0.639 0.958
In [4]:
overview(country="Germany", subregion="LK Mühldorf a.Inn");
2023-03-07T11:03:46.414448 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) 74.0 LK Mühldorf a.Inn, 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 6 daily change normalised per 100K May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0.6 0.6 0.8 0.8 1.0 1.0 1.2 1.2 R & growth factor (based on cases) Germany-LK Mühldorf a.Inn cases daily growth factor Germany-LK Mühldorf a.Inn cases daily growth factor (rolling mean) Germany-LK Mühldorf a.Inn estimated R (using cases) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 2 2 4 4 6 6 8 8 R & growth factor (based on deaths) Germany-LK Mühldorf a.Inn deaths daily growth factor Germany-LK Mühldorf a.Inn deaths daily growth factor (rolling mean) Germany-LK Mühldorf a.Inn 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 Mühldorf a.Inn doubling time cases (rolling mean) Germany-LK Mühldorf a.Inn doubling time deaths (rolling mean) 0.0 235.2 470.4 705.6 daily change Germany-LK Mühldorf a.Inn new cases (rolling 7d mean) Germany-LK Mühldorf a.Inn new cases 0.000 2.352 4.704 7.056 daily change Germany-LK Mühldorf a.Inn new deaths (rolling 7d mean) Germany-LK Mühldorf a.Inn new deaths 0.0 74.4 148.8 223.3 297.7 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Mühldorf a.Inn", dates="2020-03-15:");
2023-03-07T11:05:54.372132 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 Mühldorf a.Inn LK Mühldorf a.Inn 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 Mühldorf a.Inn Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Mühldorf a.Inn")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Mühldorf a.Inn")
print(f'Population of country="Germany", subregion="LK Mühldorf a.Inn": {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 Mühldorf a.Inn": 117606 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 68158 22 369 0
2023-03-03 68136 5 369 0
2023-03-02 68131 14 369 0
2023-03-01 68117 26 369 0
2023-02-28 68091 20 369 1
2023-02-27 68071 47 368 0
2023-02-24 68024 27 368 1
2023-02-23 67997 33 367 0
2023-02-22 67964 71 367 0
2023-02-20 67893 47 367 2
2023-02-17 67846 30 365 0
2023-02-16 67816 20 365 0
2023-02-15 67796 32 365 0
2023-02-14 67764 30 365 0
2023-02-13 67734 37 365 0
2023-02-10 67697 22 365 1
2023-02-09 67675 24 364 0
2023-02-08 67651 24 364 0
2023-02-07 67627 29 364 1
2023-02-06 67598 51 363 0
2023-02-03 67547 16 363 0
2023-02-02 67531 17 363 0
2023-02-01 67514 25 363 0
2023-01-31 67489 36 363 0
2023-01-30 67453 37 363 0
2023-01-27 67416 14 363 0
2023-01-26 67402 19 363 0
2023-01-25 67383 21 363 1
2023-01-24 67362 26 362 0
2023-01-23 67336 42 362 1
2023-01-20 67294 8 361 0
2023-01-19 67286 14 361 0
2023-01-18 67272 20 361 1
2023-01-17 67252 15 360 0
2023-01-16 67237 27 360 0
2023-01-13 67210 18 360 0
2023-01-12 67192 17 360 0
2023-01-11 67175 22 360 0
2023-01-10 67153 42 360 3
2023-01-09 67111 20 357 0
2023-01-07 67091 18 357 1
2023-01-05 67073 15 356 0
2023-01-04 67058 21 356 0
2023-01-03 67037 13 356 0
2023-01-02 67024 23 356 1
2022-12-31 67001 12 355 1
2022-12-30 66989 10 354 1
2022-12-29 66979 23 353 0
2022-12-28 66956 23 353 0
2022-12-27 66933 35 353 3
2022-12-24 66898 11 350 0
2022-12-23 66887 9 350 0
2022-12-22 66878 21 350 0
2022-12-21 66857 28 350 0
2022-12-20 66829 18 350 0
2022-12-19 66811 28 350 0
2022-12-17 66783 13 350 0
2022-12-16 66770 16 350 1
2022-12-15 66754 22 349 0
2022-12-14 66732 22 349 0
2022-12-13 66710 34 349 0
2022-12-12 66676 23 349 0
2022-12-10 66653 16 349 0
2022-12-09 66637 16 349 0
2022-12-08 66621 19 349 1
2022-12-07 66602 20 348 0
2022-12-06 66582 38 348 1
2022-12-05 66544 18 347 0
2022-12-03 66526 15 347 0
2022-12-02 66511 17 347 0
2022-12-01 66494 22 347 0
2022-11-30 66472 12 347 0
2022-11-29 66460 24 347 0
2022-11-28 66436 17 347 0
2022-11-26 66419 16 347 0
2022-11-25 66403 12 347 0
2022-11-24 66391 19 347 0
2022-11-23 66372 21 347 0
2022-11-22 66351 29 347 0
2022-11-21 66322 23 347 0
2022-11-19 66299 14 347 0
2022-11-18 66285 15 347 0
2022-11-17 66270 16 347 0
2022-11-16 66254 21 347 0
2022-11-15 66233 21 347 0
2022-11-14 66212 28 347 0
2022-11-12 66184 18 347 0
2022-11-11 66166 23 347 0
2022-11-10 66143 23 347 0
2022-11-09 66120 37 347 1
2022-11-08 66083 39 346 0
2022-11-07 66044 40 346 1
2022-11-05 66004 21 345 0
2022-11-04 65983 25 345 0
2022-11-03 65958 38 345 0
2022-11-02 65920 58 345 0
2022-10-31 65862 36 345 0
2022-10-29 65826 35 345 0
2022-10-28 65791 26 345 0
2022-10-27 65765 42 345 0
2022-10-26 65723 50 345 0
2022-10-25 65673 79 345 0
2022-10-24 65594 84 345 0
2022-10-22 65510 40 345 0
2022-10-21 65470 89 345 1
2022-10-20 65381 91 344 1
2022-10-19 65290 106 343 0
2022-10-18 65184 151 343 1
2022-10-17 65033 142 342 0
2022-10-15 64891 129 342 0
2022-10-14 64762 128 342 2
2022-10-13 64634 160 340 1
2022-10-12 64474 232 339 1
2022-10-11 64242 214 338 0
2022-10-10 64028 266 338 2
2022-10-08 63762 117 336 2
2022-10-07 63645 266 334 0
2022-10-06 63379 297 334 0
2022-10-05 63082 215 334 0
2022-10-04 62867 346 334 3
2022-10-01 62521 159 331 0
2022-09-30 62362 185 331 0
2022-09-29 62177 189 331 1
2022-09-28 61988 240 330 1
2022-09-27 61748 175 329 1
2022-09-26 61573 197 328 0
2022-09-24 61376 94 328 0
2022-09-23 61282 77 328 2
2022-09-22 61205 105 326 0
2022-09-21 61100 125 326 1
2022-09-20 60975 102 325 0
2022-09-19 60873 108 325 0
2022-09-17 60765 50 325 0
2022-09-16 60715 35 325 0
2022-09-15 60680 80 325 0
2022-09-14 60600 73 325 0
2022-09-13 60527 80 325 0
2022-09-12 60447 61 325 0
2022-09-10 60386 35 325 0
2022-09-09 60351 39 325 0
2022-09-08 60312 60 325 0
2022-09-07 60252 68 325 0
2022-09-06 60184 39 325 1
2022-09-05 60145 28 324 0
2022-09-03 60117 29 324 0
2022-09-02 60088 34 324 0
2022-09-01 60054 27 324 0
2022-08-31 60027 48 324 0
2022-08-30 59979 45 324 0
2022-08-29 59934 29 324 0
2022-08-27 59905 23 324 0
2022-08-26 59882 23 324 0
2022-08-25 59859 28 324 0
2022-08-24 59831 46 324 0
2022-08-23 59785 42 324 0
2022-08-22 59743 32 324 0
2022-08-20 59711 25 324 0
2022-08-19 59686 32 324 0
2022-08-18 59654 29 324 0
2022-08-17 59625 67 324 0
2022-08-16 59558 59 324 0
2022-08-13 59499 14 324 0
2022-08-12 59485 29 324 0
2022-08-11 59456 39 324 0
2022-08-10 59417 54 324 0
2022-08-09 59363 85 324 0
2022-08-08 59278 61 324 1
2022-08-06 59217 62 323 0
2022-08-05 59155 70 323 0
2022-08-04 59085 94 323 0
2022-08-03 58991 71 323 0
2022-08-02 58920 163 323 0
2022-08-01 58757 70 323 0
2022-07-30 58687 70 323 1
2022-07-29 58617 109 322 1
2022-07-28 58508 197 321 0
2022-07-27 58311 183 321 2
2022-07-26 58128 258 319 0
2022-07-25 57870 152 319 0
2022-07-23 57718 136 319 1
2022-07-22 57582 160 318 0
2022-07-21 57422 199 318 2
2022-07-20 57223 245 316 2
2022-07-19 56978 346 314 1
2022-07-18 56632 242 313 1
2022-07-16 56390 189 312 0
2022-07-15 56201 84 312 0
2022-07-14 56117 192 312 0
2022-07-13 55925 196 312 0
2022-07-12 55729 182 312 0
2022-07-11 55547 173 312 0
2022-07-09 55374 91 312 0
2022-07-08 55283 127 312 0
2022-07-07 55156 132 312 0
2022-07-06 55024 146 312 0
2022-07-05 54878 223 312 0
2022-07-04 54655 110 312 0
2022-07-02 54545 109 312 0
2022-07-01 54436 90 312 0
2022-06-30 54346 149 312 0
2022-06-29 54197 195 312 0
2022-06-28 54002 181 312 2
2022-06-27 53821 129 310 0
2022-06-25 53692 95 310 1
2022-06-24 53597 114 309 0
2022-06-23 53483 131 309 0
2022-06-22 53352 131 309 0
2022-06-21 53221 167 309 0
2022-06-20 53054 63 309 1
2022-06-18 52991 77 308 0
2022-06-17 52914 119 308 0
2022-06-15 52795 88 308 0
2022-06-14 52707 107 308 0
2022-06-13 52600 99 308 0
2022-06-11 52501 47 308 0
2022-06-10 52454 74 308 0
2022-06-09 52380 62 308 0
2022-06-08 52318 77 308 0
2022-06-07 52241 73 308 1
2022-06-05 52168 29 307 0
2022-06-04 52139 24 307 0
2022-06-03 52115 37 307 0
2022-06-02 52078 50 307 0
2022-06-01 52028 52 307 0
2022-05-31 51976 69 307 0
2022-05-30 51907 45 307 0
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2022-05-28 51824 18 307 0
2022-05-27 51806 28 307 0
2022-05-26 51778 41 307 0
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2022-05-24 51685 79 307 0
2022-05-23 51606 29 307 0
2022-05-22 51577 57 307 0
2022-05-21 51520 90 307 0
2022-05-20 51430 54 307 0
2022-05-19 51376 68 307 0
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2022-05-16 51105 24 307 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: 0:02:44.857619