Germany: LK Rhein-Lahn-Kreis (Rheinland-Pfalz)¶

  • Homepage of project: https://oscovida.github.io
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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 14:47:34 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Rhein-Lahn-Kreis", weeks=5);
2023-03-07T14:47:49.268081 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) 121.4 LK Rhein-Lahn-Kreis, 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 0.8 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 Rhein-Lahn-Kreis cases daily growth factor Germany-LK Rhein-Lahn-Kreis cases daily growth factor (rolling mean) Germany-LK Rhein-Lahn-Kreis 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 Rhein-Lahn-Kreis deaths daily growth factor Germany-LK Rhein-Lahn-Kreis deaths daily growth factor (rolling mean) Germany-LK Rhein-Lahn-Kreis 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-LK Rhein-Lahn-Kreis doubling time cases (rolling mean) 0.00 24.54 49.09 73.63 daily change Germany-LK Rhein-Lahn-Kreis new cases (rolling 7d mean) Germany-LK Rhein-Lahn-Kreis new cases 0.000 0.245 0.491 0.736 0.982 daily change Germany-LK Rhein-Lahn-Kreis new deaths (rolling 7d mean) Germany-LK Rhein-Lahn-Kreis new deaths 0.000 0.332 0.664 0.996
In [4]:
overview(country="Germany", subregion="LK Rhein-Lahn-Kreis");
2023-03-07T14:48:09.004771 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 0 500 500 1000 1000 1500 1500 7-day incidence rate (per 100K people) 121.4 LK Rhein-Lahn-Kreis, 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 0 100 200 300 400 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 Rhein-Lahn-Kreis cases daily growth factor Germany-LK Rhein-Lahn-Kreis cases daily growth factor (rolling mean) Germany-LK Rhein-Lahn-Kreis 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 Rhein-Lahn-Kreis deaths daily growth factor Germany-LK Rhein-Lahn-Kreis deaths daily growth factor (rolling mean) Germany-LK Rhein-Lahn-Kreis 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-LK Rhein-Lahn-Kreis doubling time cases (rolling mean) Germany-LK Rhein-Lahn-Kreis doubling time deaths (rolling mean) 0.0 122.7 245.4 368.2 490.9 daily change Germany-LK Rhein-Lahn-Kreis new cases (rolling 7d mean) Germany-LK Rhein-Lahn-Kreis new cases 0.00 2.45 4.91 7.36 9.82 daily change Germany-LK Rhein-Lahn-Kreis new deaths (rolling 7d mean) Germany-LK Rhein-Lahn-Kreis new deaths 0.00 12.84 25.68 38.52 51.36 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Rhein-Lahn-Kreis", dates="2020-03-15:");
2023-03-07T14:50:35.743698 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 Rhein-Lahn-Kreis LK Rhein-Lahn-Kreis 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 Rhein-Lahn-Kreis Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Rhein-Lahn-Kreis")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Rhein-Lahn-Kreis")
print(f'Population of country="Germany", subregion="LK Rhein-Lahn-Kreis": {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 Rhein-Lahn-Kreis": 122724 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-03 49608 44 140 0
2023-03-02 49564 15 140 0
2023-03-01 49549 38 140 0
2023-02-28 49511 52 140 0
2023-02-27 49459 90 140 0
2023-02-23 49369 64 140 0
2023-02-22 49305 43 140 1
2023-02-21 49262 39 139 0
2023-02-20 49223 53 139 0
2023-02-17 49170 37 139 0
2023-02-16 49133 22 139 0
2023-02-15 49111 52 139 0
2023-02-14 49059 36 139 0
2023-02-13 49023 51 139 0
2023-02-10 48972 27 139 0
2023-02-09 48945 30 139 0
2023-02-08 48915 30 139 0
2023-02-07 48885 36 139 0
2023-02-06 48849 41 139 1
2023-02-03 48808 11 138 0
2023-02-02 48797 13 138 0
2023-02-01 48784 34 138 1
2023-01-31 48750 25 137 0
2023-01-30 48725 32 137 0
2023-01-27 48693 11 137 0
2023-01-26 48682 15 137 0
2023-01-25 48667 22 137 0
2023-01-24 48645 19 137 1
2023-01-23 48626 26 136 2
2023-01-20 48600 18 134 0
2023-01-19 48582 18 134 0
2023-01-18 48564 16 134 0
2023-01-17 48548 14 134 0
2023-01-16 48534 21 134 0
2023-01-13 48513 9 134 0
2023-01-12 48504 19 134 1
2023-01-11 48485 14 133 0
2023-01-10 48471 43 133 0
2023-01-09 48428 45 133 0
2023-01-06 48383 17 133 0
2023-01-05 48366 41 133 1
2023-01-04 48325 46 132 0
2023-01-03 48279 37 132 0
2023-01-02 48242 70 132 0
2022-12-30 48172 26 132 0
2022-12-29 48146 47 132 0
2022-12-28 48099 67 132 0
2022-12-27 48032 114 132 0
2022-12-23 47918 51 132 0
2022-12-22 47867 77 132 1
2022-12-21 47790 79 131 0
2022-12-20 47711 38 131 0
2022-12-19 47673 80 131 0
2022-12-16 47593 27 131 0
2022-12-15 47566 46 131 0
2022-12-14 47520 80 131 0
2022-12-13 47440 45 131 0
2022-12-12 47395 65 131 1
2022-12-09 47330 33 130 0
2022-12-08 47297 43 130 0
2022-12-07 47254 35 130 0
2022-12-06 47219 46 130 0
2022-12-05 47173 67 130 0
2022-12-02 47106 21 130 0
2022-12-01 47085 28 130 0
2022-11-30 47057 36 130 0
2022-11-29 47021 44 130 0
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2022-11-25 46925 23 130 0
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2021-04-06 3202 14 92 0
2021-04-05 3188 16 92 0
2021-04-03 3172 29 92 0
2021-04-01 3143 28 92 0
2021-03-31 3115 38 92 0
2021-03-30 3077 13 92 0
2021-03-29 3064 18 92 0
2021-03-28 3046 2 92 0
2021-03-27 3044 17 92 0
2021-03-26 3027 16 92 0
2021-03-25 3011 29 92 0
2021-03-24 2982 27 92 0
2021-03-23 2955 14 92 0
2021-03-22 2941 11 92 0
2021-03-20 2930 22 92 0
2021-03-19 2908 24 92 0
2021-03-18 2884 11 92 0
2021-03-17 2873 21 92 0
2021-03-16 2852 8 92 0
2021-03-15 2844 7 92 0
2021-03-13 2837 11 92 1
2021-03-12 2826 10 91 0
2021-03-11 2816 12 91 0
2021-03-10 2804 19 91 0
2021-03-09 2785 25 91 0
2021-03-08 2760 6 91 0
2021-03-06 2754 18 91 0
2021-03-05 2736 12 91 1
2021-03-04 2724 12 90 0
2021-03-03 2712 15 90 0
2021-03-02 2697 6 90 0
2021-03-01 2691 12 90 1
2021-02-27 2679 18 89 0
2021-02-26 2661 12 89 0
2021-02-25 2649 23 89 0
2021-02-24 2626 28 89 0
2021-02-23 2598 15 89 0
2021-02-22 2583 17 89 0
2021-02-20 2566 16 89 0
2021-02-19 2550 14 89 0
2021-02-18 2536 20 89 1
2021-02-17 2516 18 88 0
2021-02-16 2498 15 88 0
2021-02-15 2483 13 88 0
2021-02-13 2470 2 88 0
2021-02-12 2468 17 88 1
2021-02-11 2451 21 87 0
2021-02-10 2430 10 87 0
2021-02-09 2420 9 87 0
2021-02-08 2411 12 87 0
2021-02-07 2399 11 87 0
2021-02-06 2388 11 87 0
2021-02-05 2377 8 87 0
2021-02-04 2369 15 87 0
2021-02-03 2354 33 87 0
2021-02-02 2321 8 87 0
2021-02-01 2313 8 87 0
2021-01-30 2305 20 87 0
2021-01-29 2285 15 87 0
2021-01-28 2270 18 87 0
2021-01-27 2252 18 87 0
2021-01-26 2234 10 87 1
2021-01-25 2224 11 86 0
2021-01-23 2213 22 86 0
2021-01-22 2191 21 86 2
2021-01-21 2170 21 84 0
2021-01-20 2149 15 84 0
2021-01-19 2134 21 84 0
2021-01-18 2113 10 84 0
2021-01-16 2103 15 84 0
2021-01-15 2088 18 84 1
2021-01-14 2070 15 83 1
2021-01-13 2055 20 82 1
2021-01-12 2035 11 81 0
2021-01-11 2024 11 81 1
2021-01-10 2013 2 80 0
2021-01-09 2011 17 80 1
2021-01-08 1994 25 79 1
2021-01-07 1969 28 78 1
2021-01-06 1941 9 77 0
2021-01-05 1932 24 77 0
2021-01-04 1908 23 77 0
2021-01-03 1885 7 77 0
2021-01-02 1878 7 77 0
2021-01-01 1871 18 77 0
2020-12-31 1853 30 77 0
2020-12-30 1823 56 77 1
2020-12-29 1767 15 76 0
2020-12-28 1752 25 76 1
2020-12-27 1727 12 75 0
2020-12-26 1715 24 75 0
2020-12-25 1691 37 75 2
2020-12-24 1654 16 73 1
2020-12-23 1638 45 72 2
2020-12-22 1593 34 70 1
2020-12-21 1559 17 69 0
2020-12-20 1542 95 69 12
2020-12-19 1447 36 57 1
2020-12-18 1411 32 56 1
2020-12-17 1379 60 55 4
2020-12-16 1319 40 51 3
2020-12-15 1279 37 48 1
2020-12-14 1242 28 47 2
2020-12-13 1214 23 45 1
2020-12-12 1191 28 44 4
2020-12-11 1163 37 40 3
2020-12-10 1126 25 37 1
2020-12-09 1101 48 36 0
2020-12-08 1053 14 36 2
2020-12-07 1039 17 34 1
2020-12-06 1022 12 33 1
2020-12-05 1010 18 32 2
2020-12-04 992 30 30 3
2020-12-03 962 30 27 1
2020-12-02 932 36 26 0
2020-12-01 896 20 26 0
2020-11-30 876 12 26 0
2020-11-29 864 5 26 0
2020-11-28 859 20 26 2
2020-11-27 839 16 24 3
2020-11-26 823 16 21 3
2020-11-25 807 28 18 1
2020-11-24 779 17 17 0
2020-11-23 762 22 17 1
2020-11-22 740 3 16 0
2020-11-21 737 13 16 1
2020-11-20 724 26 15 0
2020-11-19 698 21 15 1
2020-11-18 677 24 14 1
2020-11-17 653 12 13 0
2020-11-16 641 12 13 0
2020-11-15 629 7 13 0
2020-11-14 622 11 13 1
2020-11-13 611 20 12 1
2020-11-12 591 13 11 0
2020-11-11 578 28 11 1
2020-11-10 550 9 10 0
2020-11-09 541 6 10 0
2020-11-08 535 13 10 0
2020-11-07 522 23 10 0
2020-11-06 499 9 10 0
2020-11-05 490 11 10 0
2020-11-04 479 35 10 1
2020-11-03 444 9 9 0
2020-11-02 435 6 9 1
2020-11-01 429 14 8 0
2020-10-31 415 15 8 0
2020-10-30 400 14 8 0
2020-10-29 386 14 8 0
2020-10-28 372 25 8 0
2020-10-27 347 21 8 1
2020-10-26 326 21 7 0
2020-10-24 305 9 7 0
2020-10-23 296 5 7 0
2020-10-22 291 9 7 0
2020-10-21 282 10 7 0
2020-10-20 272 4 7 0
2020-10-19 268 2 7 0
2020-10-17 266 7 7 0
2020-10-16 259 11 7 0
2020-10-15 248 4 7 0
2020-10-14 244 3 7 0
2020-10-13 241 4 7 1
2020-10-12 237 1 6 0
2020-10-11 236 8 6 0
2020-10-10 228 1 6 0
2020-10-09 227 5 6 0
2020-10-08 222 2 6 0
2020-10-07 220 1 6 0
2020-10-06 219 4 6 0
2020-10-04 215 4 6 0
2020-10-02 211 6 6 0
2020-10-01 205 1 6 0
2020-09-27 204 3 6 0
2020-09-23 201 2 6 0
2020-09-21 199 3 6 0
2020-09-06 196 2 6 0
2020-09-04 194 1 6 0
2020-09-02 193 1 6 0
2020-08-29 192 1 6 0
2020-08-28 191 3 6 0
2020-08-27 188 1 6 0
2020-08-26 187 4 6 0
2020-08-24 183 1 6 0
2020-08-23 182 1 6 0
2020-08-21 181 1 6 0
2020-08-18 180 1 6 0
2020-08-17 179 5 6 0
2020-08-14 174 1 6 0
2020-08-13 173 1 6 0
2020-08-11 172 2 6 0
2020-08-08 170 2 6 0
2020-08-07 168 1 6 0
2020-08-05 167 1 6 0
2020-08-04 166 1 6 0
2020-08-03 165 1 6 0
2020-07-30 164 2 6 0
2020-07-29 162 1 6 0
2020-07-21 161 2 6 0
2020-06-02 159 1 6 0
2020-05-11 158 1 6 0
2020-05-06 157 2 6 0
2020-05-04 155 2 6 0
2020-04-30 153 1 6 0
2020-04-26 152 1 6 0
2020-04-24 151 1 6 0
2020-04-18 150 2 6 0
2020-04-17 148 8 6 0
2020-04-16 140 3 6 0
2020-04-15 137 1 6 0
2020-04-14 136 2 6 0
2020-04-12 134 2 6 0
2020-04-11 132 2 6 0
2020-04-10 130 1 6 1
2020-04-09 129 3 5 0
2020-04-08 126 6 5 0
2020-04-07 120 3 5 0
2020-04-06 117 3 5 0
2020-04-05 114 3 5 0
2020-04-04 111 5 5 0
2020-04-02 106 11 5 1
2020-04-01 95 7 4 0
2020-03-31 88 3 4 1
2020-03-30 85 6 3 0
2020-03-29 79 1 3 0
2020-03-28 78 5 3 1
2020-03-27 73 6 2 0
2020-03-26 67 8 2 1
2020-03-25 59 9 1 0
2020-03-24 50 3 1 0
2020-03-23 47 10 1 0
2020-03-22 37 3 1 0
2020-03-21 34 2 1 0
2020-03-19 32 6 1 0
2020-03-18 26 10 1 0
2020-03-17 16 6 1 0
2020-03-16 10 4 1 1
2020-03-13 6 1 0 0
2020-03-12 5 1 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: 0:04:39.219461