Germany: SK Kaufbeuren (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 11:43:47 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="SK Kaufbeuren", weeks=5);
2023-03-07T11:44:02.876758 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 80 80 100 100 120 120 140 140 160 160 7-day incidence rate (per 100K people) 75.4 SK Kaufbeuren, 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 40 daily change normalised per 100K 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar −0.050 −0.050 −0.025 −0.025 0.000 0.000 0.025 0.025 0.050 0.050 daily change Germany-SK Kaufbeuren new deaths (rolling 7d mean) Germany-SK Kaufbeuren new deaths 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.8 0.8 1.0 1.0 1.2 1.2 1.4 1.4 R & growth factor (based on cases) Germany-SK Kaufbeuren cases daily growth factor Germany-SK Kaufbeuren cases daily growth factor (rolling mean) Germany-SK Kaufbeuren estimated R (using cases) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.8 0.8 0.9 0.9 1.0 1.0 1.1 1.1 1.2 1.2 R & growth factor (based on deaths) Germany-SK Kaufbeuren deaths daily growth factor Germany-SK Kaufbeuren deaths daily growth factor (rolling mean) Germany-SK Kaufbeuren estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 2000 4000 cases doubling time [days] Germany-SK Kaufbeuren doubling time cases (rolling mean) 0.00 4.51 9.02 13.54 18.05 daily change Germany-SK Kaufbeuren new cases (rolling 7d mean) Germany-SK Kaufbeuren new cases 0.000 0.398 0.796
In [4]:
overview(country="Germany", subregion="SK Kaufbeuren");
2023-03-07T11:44:17.915569 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) 75.4 SK Kaufbeuren, 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 daily change normalised per 100K May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0 5 10 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 1.4 1.4 R & growth factor (based on cases) Germany-SK Kaufbeuren cases daily growth factor Germany-SK Kaufbeuren cases daily growth factor (rolling mean) Germany-SK Kaufbeuren estimated R (using cases) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0.8 0.8 0.9 0.9 1.0 1.0 1.1 1.1 1.2 1.2 R & growth factor (based on deaths) Germany-SK Kaufbeuren deaths daily growth factor Germany-SK Kaufbeuren deaths daily growth factor (rolling mean) Germany-SK Kaufbeuren 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-SK Kaufbeuren doubling time cases (rolling mean) Germany-SK Kaufbeuren doubling time deaths (rolling mean) 0.0 90.2 180.5 daily change Germany-SK Kaufbeuren new cases (rolling 7d mean) Germany-SK Kaufbeuren new cases 0.000 2.256 4.512 daily change Germany-SK Kaufbeuren new deaths (rolling 7d mean) Germany-SK Kaufbeuren new deaths 0.00 17.84 35.67 53.51 71.34 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="SK Kaufbeuren", dates="2020-03-15:");
2023-03-07T11:46:23.203917 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: SK Kaufbeuren SK Kaufbeuren 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 SK Kaufbeuren Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="SK Kaufbeuren")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="SK Kaufbeuren")
print(f'Population of country="Germany", subregion="SK Kaufbeuren": {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="SK Kaufbeuren": 45118 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 21497 11 138 0
2023-03-03 21486 4 138 0
2023-03-02 21482 7 138 0
2023-03-01 21475 4 138 0
2023-02-28 21471 8 138 0
2023-02-27 21463 22 138 0
2023-02-24 21441 6 138 0
2023-02-23 21435 9 138 0
2023-02-22 21426 18 138 0
2023-02-21 21408 16 138 0
2023-02-20 21392 14 138 0
2023-02-17 21378 5 138 0
2023-02-16 21373 4 138 0
2023-02-15 21369 8 138 0
2023-02-14 21361 11 138 0
2023-02-13 21350 12 138 0
2023-02-10 21338 2 138 0
2023-02-09 21336 5 138 0
2023-02-08 21331 13 138 0
2023-02-07 21318 10 138 0
2023-02-06 21308 12 138 0
2023-02-03 21296 5 138 0
2023-02-02 21291 7 138 0
2023-02-01 21284 12 138 0
2023-01-31 21272 17 138 0
2023-01-30 21255 25 138 0
2023-01-27 21230 8 138 0
2023-01-26 21222 6 138 0
2023-01-25 21216 5 138 0
2023-01-24 21211 18 138 0
2023-01-23 21193 10 138 0
2023-01-20 21183 3 138 0
2023-01-19 21180 5 138 0
2023-01-18 21175 4 138 0
2023-01-17 21171 9 138 0
2023-01-16 21162 5 138 0
2023-01-13 21157 4 138 0
2023-01-12 21153 6 138 0
2023-01-11 21147 5 138 0
2023-01-10 21142 6 138 0
2023-01-09 21136 17 138 0
2023-01-05 21119 6 138 0
2023-01-04 21113 2 138 0
2023-01-03 21111 1 138 0
2023-01-02 21110 9 138 0
2022-12-30 21101 1 138 0
2022-12-29 21100 4 138 0
2022-12-28 21096 16 138 0
2022-12-27 21080 10 138 0
2022-12-23 21070 6 138 0
2022-12-22 21064 10 138 0
2022-12-21 21054 5 138 0
2022-12-20 21049 8 138 0
2022-12-19 21041 20 138 0
2022-12-16 21021 1 138 0
2022-12-15 21020 13 138 0
2022-12-14 21007 3 138 0
2022-12-13 21004 9 138 0
2022-12-12 20995 15 138 0
2022-12-09 20980 6 138 0
2022-12-08 20974 8 138 0
2022-12-07 20966 9 138 0
2022-12-06 20957 8 138 0
2022-12-05 20949 4 138 0
2022-12-02 20945 6 138 0
2022-12-01 20939 7 138 0
2022-11-30 20932 8 138 0
2022-11-29 20924 8 138 0
2022-11-28 20916 9 138 0
2022-11-25 20907 12 138 0
2022-11-24 20895 3 138 0
2022-11-23 20892 3 138 0
2022-11-22 20889 9 138 0
2022-11-21 20880 1 138 0
2022-11-18 20879 3 138 0
2022-11-17 20876 10 138 0
2022-11-16 20866 2 138 0
2022-11-15 20864 15 138 0
2022-11-14 20849 17 138 1
2022-11-11 20832 9 137 0
2022-11-10 20823 9 137 0
2022-11-09 20814 23 137 0
2022-11-08 20791 20 137 1
2022-11-07 20771 21 136 0
2022-11-04 20750 19 136 0
2022-11-03 20731 13 136 0
2022-11-02 20718 28 136 0
2022-10-31 20690 24 136 0
2022-10-28 20666 25 136 0
2022-10-27 20641 32 136 0
2022-10-26 20609 23 136 0
2022-10-25 20586 54 136 0
2022-10-24 20532 42 136 1
2022-10-21 20490 25 135 0
2022-10-20 20465 52 135 0
2022-10-19 20413 32 135 0
2022-10-18 20381 66 135 0
2022-10-17 20315 97 135 0
2022-10-14 20218 27 135 0
2022-10-13 20191 64 135 0
2022-10-12 20127 119 135 1
2022-10-11 20008 62 134 0
2022-10-10 19946 87 134 0
2022-10-07 19859 58 134 0
2022-10-06 19801 123 134 0
2022-10-05 19678 43 134 1
2022-10-04 19635 62 133 0
2022-10-03 19573 1 133 0
2022-09-30 19572 28 133 0
2022-09-29 19544 40 133 1
2022-09-28 19504 43 132 0
2022-09-27 19461 69 132 1
2022-09-26 19392 27 131 1
2022-09-23 19365 19 130 0
2022-09-22 19346 12 130 0
2022-09-21 19334 27 130 0
2022-09-20 19307 17 130 0
2022-09-19 19290 40 130 0
2022-09-16 19250 10 130 0
2022-09-15 19240 10 130 0
2022-09-14 19230 6 130 0
2022-09-13 19224 19 130 0
2022-09-12 19205 13 130 0
2022-09-09 19192 5 130 0
2022-09-08 19187 11 130 0
2022-09-07 19176 17 130 0
2022-09-06 19159 23 130 0
2022-09-05 19136 9 130 0
2022-09-02 19127 12 130 0
2022-09-01 19115 7 130 0
2022-08-31 19108 11 130 1
2022-08-30 19097 14 129 0
2022-08-29 19083 15 129 0
2022-08-26 19068 1 129 0
2022-08-25 19067 23 129 0
2022-08-24 19044 17 129 0
2022-08-23 19027 15 129 1
2022-08-22 19012 10 128 0
2022-08-19 19002 1 128 0
2022-08-18 19001 13 128 0
2022-08-17 18988 25 128 0
2022-08-16 18963 23 128 0
2022-08-12 18940 17 128 1
2022-08-11 18923 18 127 0
2022-08-10 18905 55 127 1
2022-08-09 18850 47 126 1
2022-08-08 18803 50 125 0
2022-08-06 18753 1 125 0
2022-08-05 18752 40 125 0
2022-08-04 18712 48 125 0
2022-08-03 18664 74 125 0
2022-08-02 18590 140 125 1
2022-08-01 18450 146 124 1
2022-07-29 18304 79 123 0
2022-07-28 18225 134 123 0
2022-07-27 18091 192 123 0
2022-07-26 17899 113 123 0
2022-07-25 17786 72 123 0
2022-07-22 17714 43 123 0
2022-07-21 17671 52 123 1
2022-07-20 17619 84 122 0
2022-07-19 17535 66 122 0
2022-07-18 17469 79 122 0
2022-07-15 17390 25 122 0
2022-07-14 17365 35 122 0
2022-07-13 17330 101 122 0
2022-07-12 17229 61 122 1
2022-07-11 17168 81 121 0
2022-07-08 17087 38 121 1
2022-07-07 17049 71 120 0
2022-07-06 16978 82 120 0
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2022-07-04 16843 62 120 0
2022-07-01 16781 19 120 0
2022-06-30 16762 72 120 0
2022-06-29 16690 56 120 0
2022-06-28 16634 35 120 0
2022-06-27 16599 70 120 0
2022-06-24 16529 19 120 0
2022-06-23 16510 35 120 0
2022-06-22 16475 34 120 0
2022-06-21 16441 57 120 0
2022-06-20 16384 50 120 0
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2022-06-15 16288 36 120 0
2022-06-14 16252 24 120 0
2022-06-13 16228 66 120 0
2022-06-10 16162 27 120 0
2022-06-09 16135 126 120 1
2022-06-07 16009 1 119 0
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2022-06-02 16007 34 119 0
2022-06-01 15973 24 119 0
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2022-05-27 15888 16 119 0
2022-05-25 15872 24 119 0
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2022-05-23 15831 40 119 0
2022-05-20 15791 6 119 0
2022-05-19 15785 22 119 0
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2022-05-10 15530 70 119 0
2022-05-09 15460 53 119 0
2022-05-06 15407 28 119 0
2022-05-05 15379 38 119 0
2022-05-04 15341 61 119 0
2022-05-03 15280 34 119 0
2022-05-02 15246 72 119 0
2022-04-29 15174 15 119 0
2022-04-28 15159 38 119 0
2022-04-27 15121 48 119 0
2022-04-26 15073 63 119 0
2022-04-25 15010 47 119 2
2022-04-23 14963 14 117 0
2022-04-22 14949 34 117 0
2022-04-21 14915 66 117 0
2022-04-20 14849 80 117 0
2022-04-19 14769 107 117 2
2022-04-18 14662 1 115 0
2022-04-16 14661 28 115 0
2022-04-14 14633 91 115 3
2022-04-13 14542 73 112 0
2022-04-12 14469 94 112 0
2022-04-11 14375 70 112 1
2022-04-09 14305 57 111 0
2022-04-08 14248 56 111 0
2022-04-07 14192 116 111 0
2022-04-06 14076 68 111 0
2022-04-05 14008 152 111 1
2022-04-04 13856 72 110 0
2022-04-03 13784 1 110 0
2022-04-02 13783 81 110 0
2022-04-01 13702 84 110 0
2022-03-31 13618 145 110 0
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2022-03-22 12408 226 109 0
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2021-05-13 2318 3 58 0
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2021-04-25 2136 2 56 0
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2021-04-22 2117 8 56 0
2021-04-21 2109 19 56 0
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2021-04-16 2039 12 54 0
2021-04-15 2027 23 54 0
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2021-04-12 1942 8 54 0
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2021-04-10 1919 4 54 0
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2021-04-02 1814 33 52 0
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2021-03-31 1758 19 51 0
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2021-03-27 1702 13 51 0
2021-03-26 1689 12 51 0
2021-03-25 1677 14 51 0
2021-03-24 1663 19 51 0
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2021-02-02 1435 8 45 0
2021-02-01 1427 4 45 0
2021-01-31 1423 1 45 0
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2021-01-29 1419 3 45 1
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2021-01-27 1412 3 44 0
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2021-01-25 1402 3 44 0
2021-01-24 1399 3 44 0
2021-01-23 1396 3 44 0
2021-01-22 1393 6 44 0
2021-01-21 1387 12 44 0
2021-01-20 1375 2 44 0
2021-01-19 1373 5 44 0
2021-01-17 1368 5 44 0
2021-01-16 1363 6 44 0
2021-01-15 1357 8 44 0
2021-01-14 1349 9 44 0
2021-01-13 1340 7 44 0
2021-01-12 1333 18 44 0
2021-01-11 1315 2 44 0
2021-01-10 1313 9 44 0
2021-01-09 1304 7 44 0
2021-01-08 1297 28 44 0
2021-01-07 1269 19 44 0
2021-01-06 1250 7 44 0
2021-01-05 1243 17 44 0
2021-01-04 1226 3 44 0
2021-01-03 1223 5 44 0
2021-01-01 1218 1 44 0
2020-12-31 1217 9 44 0
2020-12-30 1208 19 44 0
2020-12-29 1189 7 44 0
2020-12-28 1182 3 44 0
2020-12-27 1179 1 44 0
2020-12-26 1178 1 44 0
2020-12-25 1177 2 44 1
2020-12-24 1175 9 43 0
2020-12-23 1166 16 43 0
2020-12-22 1150 23 43 0
2020-12-21 1127 6 43 0
2020-12-20 1121 5 43 0
2020-12-19 1116 6 43 1
2020-12-18 1110 19 42 0
2020-12-17 1091 11 42 1
2020-12-16 1080 16 41 0
2020-12-15 1064 17 41 0
2020-12-14 1047 4 41 0
2020-12-13 1043 7 41 0
2020-12-12 1036 14 41 0
2020-12-11 1022 19 41 0
2020-12-10 1003 20 41 0
2020-12-09 983 16 41 0
2020-12-08 967 30 41 2
2020-12-07 937 11 39 1
2020-12-05 926 13 38 0
2020-12-04 913 20 38 0
2020-12-03 893 22 38 0
2020-12-02 871 14 38 0
2020-12-01 857 27 38 1
2020-11-30 830 6 37 0
2020-11-29 824 1 37 0
2020-11-28 823 3 37 0
2020-11-27 820 17 37 1
2020-11-26 803 7 36 1
2020-11-25 796 25 35 0
2020-11-24 771 8 35 0
2020-11-23 763 44 35 3
2020-11-22 719 11 32 1
2020-11-21 708 7 31 0
2020-11-20 701 25 31 1
2020-11-19 676 12 30 1
2020-11-18 664 27 29 3
2020-11-17 637 21 26 2
2020-11-16 616 45 24 6
2020-11-15 571 3 18 0
2020-11-14 568 19 18 0
2020-11-13 549 17 18 1
2020-11-12 532 15 17 0
2020-11-11 517 20 17 1
2020-11-10 497 10 16 0
2020-11-09 487 8 16 2
2020-11-08 479 14 14 0
2020-11-07 465 9 14 0
2020-11-06 456 15 14 0
2020-11-05 441 7 14 0
2020-11-04 434 19 14 1
2020-11-03 415 9 13 0
2020-11-02 406 4 13 0
2020-11-01 402 11 13 0
2020-10-31 391 11 13 0
2020-10-30 380 18 13 0
2020-10-29 362 15 13 0
2020-10-28 347 39 13 0
2020-10-27 308 12 13 0
2020-10-26 296 1 13 0
2020-10-25 295 8 13 0
2020-10-24 287 7 13 1
2020-10-23 280 5 12 0
2020-10-22 275 20 12 0
2020-10-21 255 11 12 0
2020-10-20 244 7 12 0
2020-10-17 237 4 12 1
2020-10-16 233 6 11 0
2020-10-14 227 3 11 0
2020-10-13 224 4 11 0
2020-10-10 220 1 11 0
2020-10-09 219 4 11 0
2020-10-08 215 1 11 0
2020-10-07 214 1 11 0
2020-10-06 213 4 11 0
2020-10-05 209 2 11 0
2020-10-02 207 2 11 0
2020-10-01 205 2 11 0
2020-09-30 203 2 11 0
2020-09-29 201 1 11 0
2020-09-28 200 2 11 0
2020-09-26 198 1 11 0
2020-09-24 197 1 11 0
2020-09-23 196 2 11 0
2020-09-22 194 1 11 0
2020-09-21 193 2 11 0
2020-09-20 191 1 11 1
2020-09-18 190 1 10 0
2020-09-17 189 4 10 1
2020-09-16 185 4 9 0
2020-09-15 181 3 9 0
2020-09-14 178 1 9 0
2020-09-11 177 17 9 1
2020-09-10 160 2 8 0
2020-09-09 158 1 8 0
2020-09-08 157 12 8 3
2020-09-07 145 2 5 0
2020-09-05 143 3 5 0
2020-09-03 140 1 5 0
2020-09-02 139 3 5 0
2020-09-01 136 1 5 0
2020-08-30 135 1 5 0
2020-08-29 134 1 5 0
2020-08-27 133 1 5 0
2020-08-26 132 1 5 0
2020-08-25 131 1 5 0
2020-08-24 130 3 5 0
2020-08-23 127 1 5 0
2020-08-22 126 4 5 0
2020-08-21 122 2 5 0
2020-08-20 120 1 5 0
2020-08-18 119 1 5 0
2020-08-16 118 2 5 0
2020-08-14 116 1 5 0
2020-08-12 115 1 5 0
2020-08-07 114 1 5 0
2020-07-24 113 1 5 0
2020-07-21 112 1 5 0
2020-05-23 111 2 5 0
2020-05-19 109 1 5 0
2020-05-14 108 2 5 0
2020-04-30 106 1 5 0
2020-04-29 105 1 5 0
2020-04-28 104 3 5 0
2020-04-24 101 2 5 0
2020-04-23 99 1 5 0
2020-04-22 98 3 5 0
2020-04-17 95 5 5 0
2020-04-16 90 3 5 0
2020-04-15 87 6 5 0
2020-04-14 81 2 5 0
2020-04-13 79 1 5 0
2020-04-12 78 1 5 0
2020-04-11 77 6 5 2
2020-04-09 71 4 3 1
2020-04-08 67 3 2 0
2020-04-07 64 3 2 0
2020-04-06 61 2 2 0
2020-04-05 59 4 2 0
2020-04-04 55 4 2 0
2020-04-03 51 7 2 1
2020-04-02 44 4 1 0
2020-04-01 40 3 1 0
2020-03-31 37 5 1 0
2020-03-30 32 2 1 0
2020-03-29 30 1 1 0
2020-03-28 29 7 1 0
2020-03-27 22 7 1 1
2020-03-26 15 3 0 0
2020-03-25 12 2 0 0
2020-03-24 10 2 0 0
2020-03-23 8 2 0 0
2020-03-21 6 1 0 0
2020-03-19 5 1 0 0
2020-03-16 4 3 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: 0:03:00.077926