Germany: LK Neumarkt 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 11:02:50 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Neumarkt i.d.OPf.", weeks=5);
2023-03-07T11:03:03.314162 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) 127.1 LK Neumarkt 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 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 0.8 0.8 1.0 1.0 1.2 1.2 R & growth factor (based on cases) Germany-LK Neumarkt i.d.OPf. cases daily growth factor Germany-LK Neumarkt i.d.OPf. cases daily growth factor (rolling mean) Germany-LK Neumarkt i.d.OPf. estimated R (using cases) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 1 1 2 2 3 3 4 4 5 5 R & growth factor (based on deaths) Germany-LK Neumarkt i.d.OPf. deaths daily growth factor Germany-LK Neumarkt i.d.OPf. deaths daily growth factor (rolling mean) Germany-LK Neumarkt i.d.OPf. estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 1000 2000 3000 4000 cases doubling time [days] Germany-LK Neumarkt i.d.OPf. doubling time cases (rolling mean) 0.0 27.2 54.4 81.6 daily change Germany-LK Neumarkt i.d.OPf. new cases (rolling 7d mean) Germany-LK Neumarkt i.d.OPf. new cases 0.000 0.272 0.544 0.816 daily change Germany-LK Neumarkt i.d.OPf. new deaths (rolling 7d mean) Germany-LK Neumarkt i.d.OPf. new deaths 0.000 0.246 0.492 0.737 0.983
In [4]:
overview(country="Germany", subregion="LK Neumarkt i.d.OPf.");
2023-03-07T11:03:20.841381 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) 127.1 LK Neumarkt 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 2 4 6 8 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 Neumarkt i.d.OPf. cases daily growth factor Germany-LK Neumarkt i.d.OPf. cases daily growth factor (rolling mean) Germany-LK Neumarkt 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 1 1 2 2 3 3 4 4 5 5 R & growth factor (based on deaths) Germany-LK Neumarkt i.d.OPf. deaths daily growth factor Germany-LK Neumarkt i.d.OPf. deaths daily growth factor (rolling mean) Germany-LK Neumarkt 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 2500 5000 7500 10000 cases doubling time [days] Germany-LK Neumarkt i.d.OPf. doubling time cases (rolling mean) Germany-LK Neumarkt i.d.OPf. doubling time deaths (rolling mean) 0 272 544 816 daily change Germany-LK Neumarkt i.d.OPf. new cases (rolling 7d mean) Germany-LK Neumarkt i.d.OPf. new cases 0.00 2.72 5.44 8.16 10.88 daily change Germany-LK Neumarkt i.d.OPf. new deaths (rolling 7d mean) Germany-LK Neumarkt i.d.OPf. new deaths 0.0 54.1 108.2 162.3 216.5 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Neumarkt i.d.OPf.", dates="2020-03-15:");
2023-03-07T11:06:02.842663 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 Neumarkt i.d.OPf. LK Neumarkt 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 LK Neumarkt i.d.OPf. Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Neumarkt i.d.OPf.")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Neumarkt i.d.OPf.")
print(f'Population of country="Germany", subregion="LK Neumarkt 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="LK Neumarkt i.d.OPf.": 136062 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 68531 39 291 0
2023-03-03 68492 18 291 0
2023-03-02 68474 52 291 0
2023-03-01 68422 11 291 0
2023-02-28 68411 53 291 1
2023-02-27 68358 94 290 1
2023-02-24 68264 39 289 0
2023-02-23 68225 59 289 0
2023-02-22 68166 77 289 0
2023-02-21 68089 36 289 1
2023-02-20 68053 74 288 0
2023-02-17 67979 26 288 1
2023-02-16 67953 38 287 0
2023-02-15 67915 58 287 0
2023-02-14 67857 40 287 0
2023-02-13 67817 55 287 1
2023-02-10 67762 38 286 0
2023-02-09 67724 29 286 0
2023-02-08 67695 39 286 0
2023-02-07 67656 24 286 0
2023-02-06 67632 43 286 0
2023-02-03 67589 19 286 0
2023-02-02 67570 16 286 0
2023-02-01 67554 31 286 0
2023-01-31 67523 34 286 0
2023-01-30 67489 32 286 0
2023-01-27 67457 17 286 0
2023-01-26 67440 18 286 0
2023-01-25 67422 21 286 0
2023-01-24 67401 17 286 0
2023-01-23 67384 34 286 0
2023-01-20 67350 20 286 0
2023-01-19 67330 23 286 0
2023-01-18 67307 26 286 1
2023-01-17 67281 15 285 0
2023-01-16 67266 21 285 0
2023-01-13 67245 15 285 0
2023-01-12 67230 22 285 0
2023-01-11 67208 26 285 0
2023-01-10 67182 22 285 0
2023-01-09 67160 49 285 1
2023-01-05 67111 14 284 0
2023-01-04 67097 22 284 0
2023-01-03 67075 25 284 0
2023-01-02 67050 41 284 2
2022-12-30 67009 16 282 0
2022-12-29 66993 24 282 0
2022-12-28 66969 31 282 0
2022-12-27 66938 46 282 0
2022-12-23 66892 21 282 0
2022-12-22 66871 33 282 0
2022-12-21 66838 19 282 0
2022-12-20 66819 51 282 0
2022-12-19 66768 46 282 0
2022-12-16 66722 14 282 0
2022-12-15 66708 30 282 0
2022-12-14 66678 29 282 0
2022-12-13 66649 46 282 0
2022-12-12 66603 40 282 0
2022-12-09 66563 18 282 0
2022-12-08 66545 29 282 0
2022-12-07 66516 37 282 0
2022-12-06 66479 54 282 0
2022-12-05 66425 55 282 1
2022-12-02 66370 20 281 0
2022-12-01 66350 31 281 0
2022-11-30 66319 27 281 0
2022-11-29 66292 56 281 1
2022-11-28 66236 31 280 0
2022-11-25 66205 30 280 0
2022-11-24 66175 35 280 0
2022-11-23 66140 29 280 0
2022-11-22 66111 45 280 0
2022-11-21 66066 53 280 1
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2022-11-14 65846 83 279 2
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2022-11-07 65547 59 276 0
2022-11-04 65488 41 276 0
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2022-10-31 65314 107 276 1
2022-10-29 65207 1 275 0
2022-10-28 65206 51 275 0
2022-10-27 65155 78 275 0
2022-10-26 65077 131 275 0
2022-10-25 64946 147 275 0
2022-10-24 64799 194 275 1
2022-10-23 64605 1 274 0
2022-10-21 64604 54 274 0
2022-10-20 64550 156 274 0
2022-10-19 64394 237 274 0
2022-10-18 64157 145 274 0
2022-10-17 64012 301 274 1
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2022-10-11 63227 231 273 0
2022-10-10 62996 222 273 0
2022-10-08 62774 1 273 0
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2022-10-06 62439 64 273 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: 0:03:55.556919