Germany: LK Elbe-Elster (Brandenburg)¶

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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 12:06:08 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Elbe-Elster", weeks=5);
2023-03-07T12:06:21.558299 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 40 40 60 60 80 80 7-day incidence rate (per 100K people) 64.8 LK Elbe-Elster, 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 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-LK Elbe-Elster new deaths (rolling 7d mean) Germany-LK Elbe-Elster 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-LK Elbe-Elster cases daily growth factor Germany-LK Elbe-Elster cases daily growth factor (rolling mean) Germany-LK Elbe-Elster 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-LK Elbe-Elster deaths daily growth factor Germany-LK Elbe-Elster deaths daily growth factor (rolling mean) Germany-LK Elbe-Elster 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 Elbe-Elster doubling time cases (rolling mean) 0.00 10.03 20.06 30.10 daily change Germany-LK Elbe-Elster new cases (rolling 7d mean) Germany-LK Elbe-Elster new cases 0.000 0.252 0.504 0.756
In [4]:
overview(country="Germany", subregion="LK Elbe-Elster");
2023-03-07T12:06:40.322981 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) 64.8 LK Elbe-Elster, 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 0.0 2.5 5.0 7.5 10.0 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-LK Elbe-Elster cases daily growth factor Germany-LK Elbe-Elster cases daily growth factor (rolling mean) Germany-LK Elbe-Elster 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-LK Elbe-Elster deaths daily growth factor Germany-LK Elbe-Elster deaths daily growth factor (rolling mean) Germany-LK Elbe-Elster estimated R (using deaths) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0 5000 10000 cases doubling time [days] Germany-LK Elbe-Elster doubling time cases (rolling mean) Germany-LK Elbe-Elster doubling time deaths (rolling mean) 0.0 200.6 401.3 601.9 daily change Germany-LK Elbe-Elster new cases (rolling 7d mean) Germany-LK Elbe-Elster new cases 0.00 2.51 5.02 7.52 10.03 daily change Germany-LK Elbe-Elster new deaths (rolling 7d mean) Germany-LK Elbe-Elster new deaths 0.0 203.5 407.0 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Elbe-Elster", dates="2020-03-15:");
2023-03-07T12:08:50.070800 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 Elbe-Elster LK Elbe-Elster 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 Elbe-Elster Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Elbe-Elster")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Elbe-Elster")
print(f'Population of country="Germany", subregion="LK Elbe-Elster": {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 Elbe-Elster": 100317 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 50258 5 444 0
2023-03-03 50253 1 444 0
2023-03-02 50252 6 444 0
2023-03-01 50246 18 444 0
2023-02-28 50228 35 444 0
2023-02-27 50193 16 444 0
2023-02-24 50177 15 444 0
2023-02-23 50162 9 444 0
2023-02-22 50153 11 444 0
2023-02-21 50142 16 444 0
2023-02-20 50126 9 444 0
2023-02-17 50117 11 444 0
2023-02-16 50106 6 444 0
2023-02-15 50100 17 444 0
2023-02-14 50083 10 444 0
2023-02-13 50073 16 444 0
2023-02-10 50057 4 444 0
2023-02-09 50053 10 444 0
2023-02-08 50043 11 444 0
2023-02-07 50032 18 444 0
2023-02-06 50014 11 444 0
2023-02-03 50003 4 444 0
2023-02-02 49999 12 444 0
2023-02-01 49987 7 444 0
2023-01-31 49980 8 444 0
2023-01-30 49972 8 444 0
2023-01-27 49964 3 444 0
2023-01-26 49961 2 444 0
2023-01-25 49959 9 444 0
2023-01-24 49950 12 444 0
2023-01-23 49938 12 444 0
2023-01-20 49926 3 444 0
2023-01-19 49923 9 444 0
2023-01-18 49914 16 444 0
2023-01-17 49898 9 444 0
2023-01-16 49889 20 444 1
2023-01-13 49869 3 443 0
2023-01-12 49866 10 443 0
2023-01-11 49856 21 443 0
2023-01-10 49835 27 443 0
2023-01-09 49808 20 443 4
2023-01-06 49788 11 439 0
2023-01-05 49777 21 439 0
2023-01-04 49756 33 439 1
2023-01-03 49723 49 438 1
2023-01-02 49674 30 437 0
2022-12-30 49644 18 437 0
2022-12-29 49626 30 437 0
2022-12-28 49596 42 437 0
2022-12-27 49554 45 437 1
2022-12-23 49509 29 436 1
2022-12-22 49480 32 435 1
2022-12-21 49448 46 434 0
2022-12-20 49402 54 434 1
2022-12-19 49348 55 433 1
2022-12-16 49293 25 432 0
2022-12-15 49268 43 432 1
2022-12-14 49225 69 431 1
2022-12-13 49156 98 430 2
2022-12-12 49058 54 428 2
2022-12-09 49004 46 426 0
2022-12-08 48958 55 426 1
2022-12-07 48903 62 425 0
2022-12-06 48841 85 425 0
2022-12-05 48756 65 425 0
2022-12-02 48691 31 425 0
2022-12-01 48660 31 425 0
2022-11-30 48629 53 425 0
2022-11-29 48576 70 425 2
2022-11-28 48506 60 423 1
2022-11-25 48446 30 422 0
2022-11-24 48416 32 422 0
2022-11-23 48384 34 422 0
2022-11-22 48350 58 422 1
2022-11-21 48292 52 421 0
2022-11-18 48240 26 421 0
2022-11-17 48214 62 421 2
2022-11-16 48152 41 419 0
2022-11-15 48111 93 419 1
2022-11-14 48018 55 418 0
2022-11-11 47963 31 418 0
2022-11-10 47932 51 418 0
2022-11-09 47881 64 418 0
2022-11-08 47817 82 418 0
2022-11-07 47735 79 418 0
2022-11-04 47656 35 418 0
2022-11-03 47621 97 418 0
2022-11-02 47524 120 418 0
2022-11-01 47404 100 418 1
2022-10-28 47304 73 417 0
2022-10-27 47231 83 417 1
2022-10-26 47148 137 416 0
2022-10-25 47011 181 416 0
2022-10-24 46830 157 416 1
2022-10-21 46673 108 415 0
2022-10-20 46565 161 415 1
2022-10-19 46404 177 414 0
2022-10-18 46227 227 414 0
2022-10-17 46000 196 414 2
2022-10-14 45804 87 412 0
2022-10-13 45717 100 412 1
2022-10-12 45617 147 411 0
2022-10-11 45470 297 411 0
2022-10-10 45173 169 411 0
2022-10-07 45004 147 411 0
2022-10-06 44857 162 411 0
2022-10-05 44695 305 411 1
2022-10-04 44390 132 410 1
2022-10-01 44258 1 409 0
2022-09-30 44257 75 409 0
2022-09-29 44182 87 409 0
2022-09-28 44095 141 409 1
2022-09-27 43954 143 408 0
2022-09-26 43811 102 408 1
2022-09-23 43709 66 407 0
2022-09-22 43643 74 407 0
2022-09-21 43569 74 407 0
2022-09-20 43495 111 407 0
2022-09-19 43384 83 407 1
2022-09-16 43301 62 406 0
2022-09-15 43239 70 406 1
2022-09-14 43169 101 405 0
2022-09-13 43068 92 405 0
2022-09-12 42976 57 405 0
2022-09-09 42919 45 405 0
2022-09-08 42874 59 405 0
2022-09-07 42815 72 405 0
2022-09-06 42743 76 405 0
2022-09-05 42667 60 405 0
2022-09-02 42607 38 405 0
2022-09-01 42569 44 405 0
2022-08-31 42525 55 405 0
2022-08-30 42470 76 405 0
2022-08-29 42394 63 405 0
2022-08-26 42331 44 405 1
2022-08-25 42287 65 404 0
2022-08-24 42222 62 404 0
2022-08-23 42160 89 404 1
2022-08-22 42071 55 403 0
2022-08-19 42016 44 403 0
2022-08-18 41972 74 403 0
2022-08-17 41898 65 403 1
2022-08-16 41833 102 402 0
2022-08-15 41731 103 402 1
2022-08-13 41628 2 401 0
2022-08-12 41626 42 401 0
2022-08-11 41584 85 401 1
2022-08-10 41499 98 400 0
2022-08-09 41401 117 400 0
2022-08-08 41284 106 400 1
2022-08-05 41178 65 399 1
2022-08-04 41113 81 398 0
2022-08-03 41032 99 398 0
2022-08-02 40933 116 398 1
2022-08-01 40817 81 397 0
2022-07-29 40736 65 397 0
2022-07-28 40671 84 397 0
2022-07-27 40587 117 397 0
2022-07-26 40470 161 397 0
2022-07-25 40309 118 397 0
2022-07-22 40191 71 397 0
2022-07-21 40120 143 397 0
2022-07-20 39977 85 397 0
2022-07-19 39892 130 397 0
2022-07-18 39762 116 397 0
2022-07-15 39646 55 397 0
2022-07-14 39591 78 397 0
2022-07-13 39513 112 397 0
2022-07-12 39401 90 397 0
2022-07-11 39311 85 397 0
2022-07-08 39226 68 397 0
2022-07-07 39158 80 397 0
2022-07-06 39078 100 397 0
2022-07-05 38978 106 397 0
2022-07-04 38872 82 397 0
2022-07-01 38790 68 397 0
2022-06-30 38722 55 397 0
2022-06-29 38667 90 397 0
2022-06-28 38577 136 397 0
2022-06-27 38441 85 397 0
2022-06-24 38356 42 397 0
2022-06-23 38314 66 397 0
2022-06-22 38248 76 397 0
2022-06-21 38172 85 397 0
2022-06-20 38087 71 397 0
2022-06-17 38016 45 397 0
2022-06-16 37971 38 397 0
2022-06-15 37933 51 397 0
2022-06-14 37882 64 397 0
2022-06-13 37818 55 397 0
2022-06-11 37763 2 397 0
2022-06-10 37761 29 397 0
2022-06-09 37732 47 397 0
2022-06-08 37685 54 397 0
2022-06-07 37631 32 397 0
2022-06-03 37599 17 397 0
2022-06-02 37582 21 397 1
2022-06-01 37561 23 396 0
2022-05-31 37538 37 396 0
2022-05-30 37501 34 396 1
2022-05-25 37467 18 395 0
2022-05-24 37449 42 395 0
2022-05-23 37407 32 395 0
2022-05-20 37375 21 395 0
2022-05-19 37354 14 395 0
2022-05-18 37340 29 395 0
2022-05-17 37311 48 395 0
2022-05-16 37263 48 395 2
2022-05-13 37215 30 393 0
2022-05-12 37185 42 393 0
2022-05-11 37143 61 393 0
2022-05-10 37082 93 393 0
2022-05-09 36989 70 393 0
2022-05-06 36919 47 393 0
2022-05-05 36872 68 393 0
2022-05-04 36804 63 393 0
2022-05-03 36741 135 393 0
2022-05-02 36606 88 393 0
2022-04-29 36518 61 393 0
2022-04-28 36457 67 393 0
2022-04-27 36390 70 393 0
2022-04-26 36320 213 393 0
2022-04-25 36107 141 393 0
2022-04-22 35966 94 393 0
2022-04-21 35872 145 393 0
2022-04-20 35727 226 393 0
2022-04-19 35501 210 393 0
2022-04-14 35291 109 393 0
2022-04-13 35182 155 393 0
2022-04-12 35027 188 393 0
2022-04-11 34839 145 393 0
2022-04-08 34694 97 393 0
2022-04-07 34597 116 393 0
2022-04-06 34481 137 393 0
2022-04-05 34344 269 393 0
2022-04-04 34075 193 393 1
2022-04-01 33882 170 392 0
2022-03-31 33712 202 392 0
2022-03-30 33510 358 392 0
2022-03-29 33152 274 392 1
2022-03-28 32878 325 391 1
2022-03-27 32553 1 390 0
2022-03-25 32552 202 390 0
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2022-03-23 32010 565 389 1
2022-03-22 31445 333 388 0
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2022-03-18 30636 217 387 1
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2022-03-16 29854 542 386 2
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2022-03-14 28794 219 384 0
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2022-01-11 17335 77 360 3
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2022-01-01 16775 27 350 0
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2021-12-29 16600 110 349 2
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2021-05-22 7060 8 246 0
2021-05-21 7052 11 246 0
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2021-05-14 6980 3 246 0
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2021-05-12 6967 38 246 0
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2021-05-08 6889 26 246 1
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2021-04-20 6438 39 237 0
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2021-04-14 6308 57 234 1
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2021-04-08 6120 55 228 1
2021-04-07 6065 69 227 1
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2021-03-07 5059 5 195 0
2021-03-06 5054 12 195 0
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2021-02-13 4716 16 188 1
2021-02-12 4700 24 187 0
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2021-02-07 4602 5 183 0
2021-02-06 4597 16 183 2
2021-02-05 4581 12 181 1
2021-02-04 4569 13 180 0
2021-02-03 4556 22 180 1
2021-02-02 4534 37 179 1
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2021-01-31 4496 12 178 1
2021-01-30 4484 24 177 0
2021-01-29 4460 21 177 1
2021-01-28 4439 33 176 0
2021-01-27 4406 44 176 1
2021-01-26 4362 36 175 1
2021-01-25 4326 7 174 2
2021-01-24 4319 6 172 0
2021-01-23 4313 50 172 1
2021-01-22 4263 31 171 0
2021-01-21 4232 8 171 1
2021-01-20 4224 65 170 0
2021-01-19 4159 63 170 4
2021-01-18 4096 32 166 2
2021-01-17 4064 9 164 0
2021-01-16 4055 23 164 0
2021-01-15 4032 41 164 3
2021-01-14 3991 69 161 1
2021-01-13 3922 51 160 9
2021-01-12 3871 101 151 5
2021-01-11 3770 46 146 5
2021-01-10 3724 10 141 1
2021-01-09 3714 64 140 2
2021-01-08 3650 60 138 2
2021-01-07 3590 62 136 3
2021-01-06 3528 150 133 5
2021-01-05 3378 132 128 3
2021-01-04 3246 76 125 7
2021-01-03 3170 16 118 1
2021-01-02 3154 23 117 3
2021-01-01 3131 31 114 2
2020-12-31 3100 68 112 7
2020-12-30 3032 66 105 2
2020-12-29 2966 105 103 1
2020-12-28 2861 89 102 7
2020-12-27 2772 43 95 0
2020-12-26 2729 37 95 1
2020-12-25 2692 56 94 7
2020-12-24 2636 77 87 6
2020-12-23 2559 96 81 3
2020-12-22 2463 137 78 11
2020-12-21 2326 67 67 0
2020-12-20 2259 21 67 2
2020-12-19 2238 73 65 3
2020-12-18 2165 121 62 7
2020-12-17 2044 108 55 2
2020-12-16 1936 104 53 7
2020-12-15 1832 86 46 4
2020-12-14 1746 57 42 3
2020-12-13 1689 10 39 1
2020-12-12 1679 55 38 3
2020-12-11 1624 94 35 3
2020-12-10 1530 128 32 3
2020-12-09 1402 79 29 2
2020-12-08 1323 63 27 1
2020-12-07 1260 54 26 5
2020-12-06 1206 16 21 0
2020-12-05 1190 48 21 1
2020-12-04 1142 40 20 0
2020-12-03 1102 58 20 3
2020-12-02 1044 47 17 1
2020-12-01 997 33 16 1
2020-11-30 964 21 15 0
2020-11-29 943 10 15 1
2020-11-28 933 16 14 1
2020-11-27 917 25 13 1
2020-11-26 892 20 12 0
2020-11-25 872 34 12 1
2020-11-24 838 31 11 1
2020-11-23 807 14 10 0
2020-11-22 793 18 10 0
2020-11-21 775 10 10 0
2020-11-20 765 21 10 0
2020-11-19 744 19 10 0
2020-11-18 725 27 10 0
2020-11-17 698 20 10 0
2020-11-16 678 11 10 0
2020-11-15 667 9 10 0
2020-11-14 658 21 10 0
2020-11-13 637 16 10 2
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2020-11-11 592 21 8 0
2020-11-10 571 27 8 0
2020-11-09 544 10 8 0
2020-11-08 534 5 8 0
2020-11-07 529 21 8 1
2020-11-06 508 13 7 0
2020-11-05 495 34 7 0
2020-11-04 461 30 7 0
2020-11-03 431 20 7 0
2020-11-02 411 21 7 0
2020-11-01 390 17 7 0
2020-10-31 373 20 7 0
2020-10-30 353 28 7 0
2020-10-29 325 29 7 0
2020-10-28 296 36 7 0
2020-10-27 260 23 7 0
2020-10-26 237 4 7 0
2020-10-25 233 3 7 0
2020-10-24 230 29 7 1
2020-10-23 201 12 6 1
2020-10-22 189 17 5 0
2020-10-21 172 2 5 0
2020-10-20 170 11 5 0
2020-10-19 159 3 5 1
2020-10-18 156 3 4 0
2020-10-17 153 3 4 1
2020-10-16 150 7 3 0
2020-10-15 143 2 3 0
2020-10-14 141 7 3 0
2020-10-13 134 8 3 0
2020-10-12 126 4 3 0
2020-10-11 122 1 3 0
2020-10-10 121 2 3 0
2020-10-09 119 2 3 0
2020-10-08 117 2 3 0
2020-10-06 115 4 3 0
2020-10-02 111 2 3 0
2020-09-28 109 1 3 0
2020-09-27 108 1 3 0
2020-09-24 107 1 3 0
2020-09-23 106 2 3 0
2020-09-19 104 1 3 0
2020-09-18 103 5 3 0
2020-09-17 98 1 3 0
2020-09-16 97 1 3 0
2020-09-08 96 1 3 0
2020-09-01 95 1 3 0
2020-08-28 94 1 3 0
2020-08-26 93 2 3 0
2020-08-21 91 1 3 0
2020-08-12 90 1 3 0
2020-08-06 89 3 3 0
2020-07-23 86 1 3 0
2020-07-21 85 2 3 0
2020-07-03 83 1 3 0
2020-06-26 82 1 3 0
2020-06-15 81 1 3 0
2020-06-11 80 1 3 0
2020-06-08 79 1 3 0
2020-05-26 78 1 3 0
2020-05-13 77 1 3 0
2020-05-07 76 1 3 0
2020-05-06 75 1 3 0
2020-04-21 74 1 3 0
2020-04-16 73 3 3 0
2020-04-12 70 1 3 0
2020-04-11 69 1 3 1
2020-04-10 68 1 2 0
2020-04-09 67 1 2 0
2020-04-07 66 3 2 0
2020-04-06 63 3 2 0
2020-04-04 60 7 2 0
2020-04-03 53 9 2 0
2020-04-02 44 5 2 0
2020-04-01 39 1 2 0
2020-03-31 38 3 2 1
2020-03-30 35 1 1 0
2020-03-29 34 2 1 0
2020-03-28 32 3 1 0
2020-03-27 29 6 1 1
2020-03-26 23 5 0 0
2020-03-25 18 1 0 0
2020-03-24 17 4 0 0
2020-03-22 13 3 0 0
2020-03-20 10 1 0 0
2020-03-18 9 1 0 0
2020-03-17 8 2 0 0
2020-03-12 6 2 0 0
2020-03-10 4 2 0 0
2020-03-09 2 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: 0:02:56.270488