Germany: LK Hameln-Pyrmont (Niedersachsen)¶

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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 13:15:05 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Hameln-Pyrmont", weeks=5);
2023-03-07T13:15:16.942831 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) 95.3 LK Hameln-Pyrmont, 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 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 Hameln-Pyrmont cases daily growth factor Germany-LK Hameln-Pyrmont cases daily growth factor (rolling mean) Germany-LK Hameln-Pyrmont 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-LK Hameln-Pyrmont deaths daily growth factor Germany-LK Hameln-Pyrmont deaths daily growth factor (rolling mean) Germany-LK Hameln-Pyrmont 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 Hameln-Pyrmont doubling time cases (rolling mean) 0.00 29.79 59.59 daily change Germany-LK Hameln-Pyrmont new cases (rolling 7d mean) Germany-LK Hameln-Pyrmont new cases 0.000 0.298 0.596 0.894 daily change Germany-LK Hameln-Pyrmont new deaths (rolling 7d mean) Germany-LK Hameln-Pyrmont new deaths 0.000 0.249 0.498 0.747 0.996
In [4]:
overview(country="Germany", subregion="LK Hameln-Pyrmont");
2023-03-07T13:15:34.804329 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) 95.3 LK Hameln-Pyrmont, 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 Hameln-Pyrmont cases daily growth factor Germany-LK Hameln-Pyrmont cases daily growth factor (rolling mean) Germany-LK Hameln-Pyrmont 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-LK Hameln-Pyrmont deaths daily growth factor Germany-LK Hameln-Pyrmont deaths daily growth factor (rolling mean) Germany-LK Hameln-Pyrmont 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 Hameln-Pyrmont doubling time cases (rolling mean) Germany-LK Hameln-Pyrmont doubling time deaths (rolling mean) 0 298 596 894 daily change Germany-LK Hameln-Pyrmont new cases (rolling 7d mean) Germany-LK Hameln-Pyrmont new cases 0.000 1.490 2.979 4.469 5.959 daily change Germany-LK Hameln-Pyrmont new deaths (rolling 7d mean) Germany-LK Hameln-Pyrmont new deaths 0.0 42.5 85.1 127.6 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Hameln-Pyrmont", dates="2020-03-15:");
2023-03-07T13:18:00.038756 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 Hameln-Pyrmont LK Hameln-Pyrmont 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 Hameln-Pyrmont Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Hameln-Pyrmont")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Hameln-Pyrmont")
print(f'Population of country="Germany", subregion="LK Hameln-Pyrmont": {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 Hameln-Pyrmont": 148963 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 69484 13 212 0
2023-03-03 69471 11 212 0
2023-03-02 69460 13 212 0
2023-03-01 69447 40 212 0
2023-02-28 69407 65 212 0
2023-02-27 69342 34 212 0
2023-02-24 69308 24 212 0
2023-02-23 69284 43 212 0
2023-02-22 69241 29 212 0
2023-02-21 69212 68 212 0
2023-02-20 69144 32 212 0
2023-02-17 69112 30 212 0
2023-02-16 69082 33 212 0
2023-02-15 69049 30 212 0
2023-02-14 69019 73 212 0
2023-02-13 68946 32 212 0
2023-02-10 68914 54 212 0
2023-02-08 68860 20 212 0
2023-02-07 68840 52 212 0
2023-02-06 68788 29 212 0
2023-02-03 68759 19 212 0
2023-02-02 68740 33 212 1
2023-02-01 68707 37 211 0
2023-01-31 68670 75 211 0
2023-01-30 68595 25 211 0
2023-01-27 68570 25 211 0
2023-01-26 68545 35 211 0
2023-01-25 68510 33 211 0
2023-01-24 68477 71 211 0
2023-01-23 68406 33 211 0
2023-01-20 68373 19 211 0
2023-01-19 68354 16 211 0
2023-01-18 68338 38 211 0
2023-01-17 68300 75 211 0
2023-01-16 68225 29 211 0
2023-01-13 68196 32 211 0
2023-01-12 68164 41 211 0
2023-01-11 68123 57 211 0
2023-01-10 68066 89 211 1
2023-01-09 67977 41 210 0
2023-01-06 67936 41 210 0
2023-01-05 67895 59 210 0
2023-01-04 67836 77 210 0
2023-01-03 67759 149 210 0
2023-01-02 67610 96 210 1
2022-12-30 67514 320 209 1
2022-12-29 67194 6 208 0
2022-12-28 67188 219 208 0
2022-12-23 66969 141 208 0
2022-12-22 66828 139 208 1
2022-12-21 66689 174 207 0
2022-12-20 66515 227 207 0
2022-12-19 66288 122 207 0
2022-12-16 66166 116 207 0
2022-12-15 66050 96 207 0
2022-12-14 65954 118 207 1
2022-12-13 65836 207 206 0
2022-12-12 65629 160 206 1
2022-12-09 65469 92 205 0
2022-12-08 65377 93 205 0
2022-12-07 65284 105 205 1
2022-12-06 65179 182 204 0
2022-12-05 64997 107 204 0
2022-12-02 64890 90 204 0
2022-12-01 64800 87 204 0
2022-11-30 64713 100 204 1
2022-11-29 64613 189 203 0
2022-11-28 64424 138 203 0
2022-11-25 64286 42 203 1
2022-11-24 64244 75 202 0
2022-11-23 64169 85 202 1
2022-11-22 64084 126 201 0
2022-11-21 63958 72 201 1
2022-11-18 63886 39 200 0
2022-11-17 63847 55 200 0
2022-11-16 63792 82 200 0
2022-11-15 63710 148 200 0
2022-11-14 63562 82 200 0
2022-11-11 63480 74 200 0
2022-11-10 63406 80 200 0
2022-11-09 63326 98 200 0
2022-11-08 63228 164 200 1
2022-11-07 63064 76 199 0
2022-11-04 62988 80 199 0
2022-11-03 62908 106 199 1
2022-11-02 62802 192 198 0
2022-11-01 62610 184 198 4
2022-10-28 62426 109 194 0
2022-10-27 62317 143 194 0
2022-10-26 62174 154 194 0
2022-10-25 62020 333 194 0
2022-10-24 61687 170 194 0
2022-10-21 61517 215 194 0
2022-10-20 61302 182 194 0
2022-10-19 61120 162 194 0
2022-10-18 60958 396 194 1
2022-10-17 60562 264 193 0
2022-10-14 60298 199 193 0
2022-10-13 60099 224 193 1
2022-10-12 59875 373 192 1
2022-10-11 59502 425 191 0
2022-10-10 59077 253 191 0
2022-10-07 58824 328 191 0
2022-10-06 58496 399 191 0
2022-10-05 58097 499 191 0
2022-10-04 57598 218 191 0
2022-09-30 57380 158 191 0
2022-09-29 57222 157 191 0
2022-09-28 57065 165 191 0
2022-09-27 56900 261 191 1
2022-09-26 56639 101 190 0
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2022-08-31 54957 73 188 0
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2022-08-26 54678 58 188 1
2022-08-25 54620 76 187 0
2022-08-24 54544 101 187 0
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2022-08-22 54254 93 187 0
2022-08-19 54161 75 187 2
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2021-01-01 1704 9 39 0
2020-12-31 1695 24 39 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: 0:03:23.490894