import os
import pandas as pd
entries = os.listdir('test_csvs/')
li = []
for entry in entries:
if '.csv' in entry:
num = entry.split('sitrep-')[1].split('-')[0]
date = entry.split('-')[0]
f = pd.read_csv('csvs/'+entry, index_col=None, header=0)
if f.shape[1] == 7:
if 'Total' in f.columns[0]:
f.columns = ['country', 'total_confirmed', 'total_new', 'total_deaths', 'total_new_deaths', 'transmission_class', 'days_since_report']
f['date'] = date
li.append(f)
frame = pd.concat(li, axis=0, ignore_index=True)
frame
country | total_confirmed | total_new | total_deaths | total_new_deaths | transmission_class | days_since_report | date | |
---|---|---|---|---|---|---|---|---|
0 | Western Pacific Region | NaN | NaN | NaN | NaN | NaN | NaN | 20200322 |
1 | China | 81498.0 | 82.0 | 3267.0 | 6.0 | Local transmission | 0.0 | 20200322 |
2 | Republic of Korea | 8897.0 | 98.0 | 104.0 | 2.0 | Local transmission | 0.0 | 20200322 |
3 | Malaysia | 1183.0 | 153.0 | 3.0 | 0.0 | Local transmission | 0.0 | 20200322 |
4 | Australia | 1081.0 | 208.0 | 7.0 | 0.0 | Local transmission | 0.0 | 20200322 |
5 | Japan | 1046.0 | 50.0 | 36.0 | 1.0 | Local transmission | 0.0 | 20200322 |
6 | Singapore | 432.0 | 47.0 | 2.0 | 2.0 | Local transmission | 0.0 | 20200322 |
7 | Philippines | 307.0 | 77.0 | 19.0 | 1.0 | Local transmission | 0.0 | 20200322 |
8 | Viet Nam | 94.0 | 3.0 | 0.0 | 0.0 | Local transmission | 0.0 | 20200322 |
9 | Brunei Darussalam | 83.0 | 5.0 | 0.0 | 0.0 | Local transmission | 0.0 | 20200322 |
10 | New Zealand | 66.0 | 13.0 | 0.0 | 0.0 | Local transmission | 0.0 | 20200322 |
11 | Cambodia | 53.0 | 2.0 | 0.0 | 0.0 | Local transmission | 0.0 | 20200322 |
12 | Mongolia | 10.0 | 4.0 | 0.0 | 0.0 | Imported cases only | 0.0 | 20200322 |
13 | Fiji | 2.0 | 1.0 | 0.0 | 0.0 | Local transmission | 0.0 | 20200322 |
14 | Papua New Guinea | 1.0 | 0.0 | 0.0 | 0.0 | Imported cases only | 1.0 | 20200322 |
15 | Territories** | NaN | NaN | NaN | NaN | NaN | NaN | 20200322 |
16 | French Polynesia \n15 \n4 \n0 \n0 \nImported c... | NaN | NaN | NaN | NaN | NaN | NaN | 20200322 |
17 | Guam \n15 \n1 \n0 \n0 \nLocal transmission \n0 | NaN | NaN | NaN | NaN | NaN | NaN | 20200322 |
18 | New Caledonia \n4 \n2 \n0 \n0 \nImported cases... | NaN | NaN | NaN | NaN | NaN | NaN | 20200322 |
19 | European Region ^ | NaN | NaN | NaN | NaN | NaN | NaN | 20200322 |
20 | Italy | 53578.0 | 6557.0 | 4827.0 | 795.0 | Local transmission | 0.0 | 20200322 |
21 | Spain | 24926.0 | 4946.0 | 1326.0 | 324.0 | Local transmission | 0.0 | 20200322 |
22 | Germany | 21463.0 | 3140.0 | 67.0 | 22.0 | Local transmission | 0.0 | 20200322 |
23 | France | 14296.0 | 1821.0 | 562.0 | 112.0 | Local transmission | 0.0 | 20200322 |
24 | Switzerland | 6077.0 | 1237.0 | 56.0 | 13.0 | Local transmission | 0.0 | 20200322 |
25 | The United Kingdom | 5018.0 | 1035.0 | 233.0 | 56.0 | Local transmission | 0.0 | 20200322 |
26 | Netherlands | 3631.0 | 637.0 | 136.0 | 30.0 | Local transmission | 0.0 | 20200322 |
27 | Austria | 3024.0 | 375.0 | 8.0 | 2.0 | Local transmission | 0.0 | 20200322 |
28 | Belgium | 2815.0 | 558.0 | 67.0 | 30.0 | Local transmission | 0.0 | 20200322 |
29 | Norway | 1926.0 | 184.0 | 7.0 | 0.0 | Local transmission | 0.0 | 20200322 |
... | ... | ... | ... | ... | ... | ... | ... | ... |
110 | Guam \n14 \n2 \n0 \n0 \nLocal transmission \n0 | NaN | NaN | NaN | NaN | NaN | NaN | 20200321 |
111 | French Polynesia \n11 \n0 \n0 \n0 \nImported c... | NaN | NaN | NaN | NaN | NaN | NaN | 20200321 |
112 | New Caledonia \n2 \n0 \n0 \n0 \nImported cases... | NaN | NaN | NaN | NaN | NaN | NaN | 20200321 |
113 | European Region ^ | NaN | NaN | NaN | NaN | NaN | NaN | 20200321 |
114 | Italy | 47021.0 | 5986.0 | 4032.0 | 625.0 | Local transmission | 0.0 | 20200321 |
115 | Spain | 19980.0 | 2833.0 | 1002.0 | 235.0 | Local transmission | 0.0 | 20200321 |
116 | Germany | 18323.0 | 7324.0 | 45.0 | 25.0 | Local transmission | 0.0 | 20200321 |
117 | France | 12475.0 | 1598.0 | 450.0 | 78.0 | Local transmission | 0.0 | 20200321 |
118 | Switzerland | 4840.0 | 977.0 | 43.0 | 10.0 | Local transmission | 0.0 | 20200321 |
119 | The United Kingdom | 3983.0 | 706.0 | 177.0 | 33.0 | Local transmission | 0.0 | 20200321 |
120 | Netherlands | 2994.0 | 534.0 | 106.0 | 30.0 | Local transmission | 0.0 | 20200321 |
121 | Austria | 2649.0 | 806.0 | 6.0 | 1.0 | Local transmission | 0.0 | 20200321 |
122 | Belgium | 2257.0 | 462.0 | 37.0 | 23.0 | Local transmission | 0.0 | 20200321 |
123 | Norway | 1742.0 | 190.0 | 7.0 | 1.0 | Local transmission | 0.0 | 20200321 |
124 | Sweden | 1623.0 | 200.0 | 16.0 | 13.0 | Local transmission | 0.0 | 20200321 |
125 | Denmark | 1255.0 | 123.0 | 9.0 | 3.0 | Local transmission | 0.0 | 20200321 |
126 | Portugal | 1020.0 | 235.0 | 6.0 | 3.0 | Local transmission | 0.0 | 20200321 |
127 | Czechia | 904.0 | 210.0 | 0.0 | 0.0 | Local transmission | 0.0 | 20200321 |
128 | Israel | 712.0 | 183.0 | 1.0 | 1.0 | Local transmission | 0.0 | 20200321 |
129 | Ireland | 683.0 | 126.0 | 3.0 | 0.0 | Local transmission | 0.0 | 20200321 |
130 | Turkey | 670.0 | 479.0 | 9.0 | 7.0 | Local transmission | 0.0 | 20200321 |
131 | Greece | 495.0 | 77.0 | 8.0 | 3.0 | Local transmission | 0.0 | 20200321 |
132 | Luxembourg | 484.0 | 139.0 | 5.0 | 1.0 | Local transmission | 0.0 | 20200321 |
133 | Finland | 450.0 | 81.0 | 0.0 | 0.0 | Local transmission | 0.0 | 20200321 |
134 | Poland | 425.0 | 100.0 | 5.0 | 0.0 | Local transmission | 0.0 | 20200321 |
135 | Iceland | 409.0 | 79.0 | 1.0 | 1.0 | Local transmission | 0.0 | 20200321 |
136 | Slovenia | 341.0 | 22.0 | 1.0 | 0.0 | Local transmission | 0.0 | 20200321 |
137 | Romania | 308.0 | 48.0 | 0.0 | 0.0 | Local transmission | 0.0 | 20200321 |
138 | Estonia | 283.0 | 16.0 | 0.0 | 0.0 | Local transmission | 0.0 | 20200321 |
139 | Russian Federation | 253.0 | 54.0 | 0.0 | 0.0 | Imported cases only | 0.0 | 20200321 |
140 rows × 8 columns
grouped = pd.DataFrame(frame.groupby(['country', 'date','total_deaths']).sum())
grouped
total_confirmed | total_new | total_new_deaths | days_since_report | |||
---|---|---|---|---|---|---|
country | date | total_deaths | ||||
Australia | 20200320 | 6.0 | 709.0 | 199.0 | 0.0 | 0.0 |
20200321 | 7.0 | 873.0 | 164.0 | 1.0 | 0.0 | |
20200322 | 7.0 | 1081.0 | 208.0 | 0.0 | 0.0 | |
Austria | 20200320 | 5.0 | 1843.0 | 197.0 | 1.0 | 0.0 |
20200321 | 6.0 | 2649.0 | 806.0 | 1.0 | 0.0 | |
20200322 | 8.0 | 3024.0 | 375.0 | 2.0 | 0.0 | |
Belgium | 20200320 | 14.0 | 1795.0 | 309.0 | 0.0 | 0.0 |
20200321 | 37.0 | 2257.0 | 462.0 | 23.0 | 0.0 | |
20200322 | 67.0 | 2815.0 | 558.0 | 30.0 | 0.0 | |
Brunei Darussalam | 20200320 | 0.0 | 73.0 | 17.0 | 0.0 | 0.0 |
20200321 | 0.0 | 78.0 | 5.0 | 0.0 | 0.0 | |
20200322 | 0.0 | 83.0 | 5.0 | 0.0 | 0.0 | |
Cambodia | 20200320 | 0.0 | 47.0 | 12.0 | 0.0 | 0.0 |
20200321 | 0.0 | 51.0 | 4.0 | 0.0 | 0.0 | |
20200322 | 0.0 | 53.0 | 2.0 | 0.0 | 0.0 | |
China | 20200320 | 3253.0 | 81300.0 | 126.0 | 11.0 | 0.0 |
20200321 | 3261.0 | 81416.0 | 116.0 | 8.0 | 0.0 | |
20200322 | 3267.0 | 81498.0 | 82.0 | 6.0 | 0.0 | |
Croatia | 20200322 | 1.0 | 206.0 | 80.0 | 0.0 | 0.0 |
Czechia | 20200320 | 0.0 | 694.0 | 172.0 | 0.0 | 0.0 |
20200321 | 0.0 | 904.0 | 210.0 | 0.0 | 0.0 | |
20200322 | 0.0 | 995.0 | 91.0 | 0.0 | 0.0 | |
Denmark | 20200320 | 6.0 | 1132.0 | 88.0 | 2.0 | 0.0 |
20200321 | 9.0 | 1255.0 | 123.0 | 3.0 | 0.0 | |
20200322 | 13.0 | 1326.0 | 71.0 | 4.0 | 0.0 | |
Estonia | 20200320 | 0.0 | 267.0 | 9.0 | 0.0 | 0.0 |
20200321 | 0.0 | 283.0 | 16.0 | 0.0 | 0.0 | |
20200322 | 0.0 | 306.0 | 23.0 | 0.0 | 0.0 | |
Fiji | 20200320 | 0.0 | 1.0 | 1.0 | 0.0 | 0.0 |
20200321 | 0.0 | 1.0 | 0.0 | 0.0 | 1.0 | |
... | ... | ... | ... | ... | ... | ... |
Romania | 20200322 | 0.0 | 367.0 | 59.0 | 0.0 | 0.0 |
Russian Federation | 20200320 | 0.0 | 199.0 | 52.0 | 0.0 | 0.0 |
20200321 | 0.0 | 253.0 | 54.0 | 0.0 | 0.0 | |
20200322 | 0.0 | 306.0 | 53.0 | 0.0 | 0.0 | |
San Marino | 20200320 | 14.0 | 126.0 | 17.0 | 0.0 | 0.0 |
Serbia†† | 20200320 | 0.0 | 123.0 | 41.0 | 0.0 | 0.0 |
Singapore | 20200320 | 0.0 | 345.0 | 32.0 | 0.0 | 0.0 |
20200321 | 0.0 | 385.0 | 40.0 | 0.0 | 0.0 | |
20200322 | 2.0 | 432.0 | 47.0 | 2.0 | 0.0 | |
Slovenia | 20200320 | 1.0 | 319.0 | 33.0 | 0.0 | 0.0 |
20200321 | 1.0 | 341.0 | 22.0 | 0.0 | 0.0 | |
20200322 | 1.0 | 383.0 | 42.0 | 0.0 | 0.0 | |
Spain | 20200320 | 767.0 | 17147.0 | 3431.0 | 169.0 | 0.0 |
20200321 | 1002.0 | 19980.0 | 2833.0 | 235.0 | 0.0 | |
20200322 | 1326.0 | 24926.0 | 4946.0 | 324.0 | 0.0 | |
Sweden | 20200320 | 3.0 | 1423.0 | 144.0 | 0.0 | 0.0 |
20200321 | 16.0 | 1623.0 | 200.0 | 13.0 | 0.0 | |
20200322 | 20.0 | 1746.0 | 123.0 | 4.0 | 0.0 | |
Switzerland | 20200320 | 33.0 | 3863.0 | 853.0 | 12.0 | 0.0 |
20200321 | 43.0 | 4840.0 | 977.0 | 10.0 | 0.0 | |
20200322 | 56.0 | 6077.0 | 1237.0 | 13.0 | 0.0 | |
The United Kingdom | 20200320 | 144.0 | 3277.0 | 647.0 | 41.0 | 0.0 |
20200321 | 177.0 | 3983.0 | 706.0 | 33.0 | 0.0 | |
20200322 | 233.0 | 5018.0 | 1035.0 | 56.0 | 0.0 | |
Turkey | 20200320 | 2.0 | 191.0 | 0.0 | 0.0 | 1.0 |
20200321 | 9.0 | 670.0 | 479.0 | 7.0 | 0.0 | |
20200322 | 21.0 | 947.0 | 277.0 | 12.0 | 0.0 | |
Viet Nam | 20200320 | 0.0 | 85.0 | 19.0 | 0.0 | 0.0 |
20200321 | 0.0 | 91.0 | 6.0 | 0.0 | 0.0 | |
20200322 | 0.0 | 94.0 | 3.0 | 0.0 | 0.0 |
122 rows × 4 columns
import os
import pandas as pd
entries = os.listdir('csvs/')
li = []
for entry in entries:
if '.csv' in entry:
num = entry.split('sitrep-')[1].split('-')[0]
date = entry.split('-')[0]
f = pd.read_csv('csvs/'+entry, index_col=None, header=0)
if f.shape[1] == 7:
if 'Total' in f.columns[0]:
f.columns = ['country', 'total_confirmed', 'total_new', 'total_deaths', 'total_new_deaths', 'transmission_class', 'days_since_report']
f['date'] = date
li.append(f)
frame = pd.concat(li, axis=0, ignore_index=True)
grouped = pd.DataFrame(frame.groupby(['country', 'date','total_deaths']).sum())
total_confirmed | total_new | total_new_deaths | transmission_class | days_since_report | |||
---|---|---|---|---|---|---|---|
country | date | total_deaths | |||||
Albania | 20200309 | 0.0 | 2.0 | 2.0 | 0.0 | Imported cases only | 0 |
20200311 | 0.0 | 10.0 | 8.0 | 0.0 | Local transmission | 0 | |
20200312 | 0.0 | 10.0 | 0.0 | 0.0 | Local transmission | 1 | |
20200313 | 0.0 | 23.0 | 13.0 | 0.0 | Imported cases only | 0 | |
20200314 | 1.0 | 33.0 | 10.0 | 1.0 | Local transmission | 0 | |
20200315 | 1.0 | 38.0 | 5.0 | 0.0 | Local transmission | 0 | |
20200316 | 1.0 | 42.0 | 4.0 | 0.0 | Local transmission | 0 | |
Andorra | 20200303 | 0.0 | 1.0 | 1.0 | 0.0 | Imported cases only | 0 |
20200304 | 0.0 | 1.0 | 0.0 | 0.0 | Imported cases only | 1 | |
20200305 | 0.0 | 1.0 | 0.0 | 0.0 | Imported cases only | 2 | |
20200306 | 0.0 | 1.0 | 0.0 | 0.0 | Imported cases only | 3 | |
20200307 | 0.0 | 1.0 | 0.0 | 0.0 | Imported cases only | 4 | |
Armenia | 20200302 | 0.0 | 1.0 | 1.0 | 0.0 | Imported cases only | 0 |
20200303 | 0.0 | 1.0 | 0.0 | 0.0 | Imported cases only | 1 | |
20200304 | 0.0 | 1.0 | 0.0 | 0.0 | Imported cases only | 2 | |
20200305 | 0.0 | 1.0 | 0.0 | 0.0 | Imported cases only | 3 | |
20200306 | 0.0 | 1.0 | 0.0 | 0.0 | Imported cases only | 4 | |
20200307 | 0.0 | 1.0 | 0.0 | 0.0 | Imported cases only | 5 | |
Australia | 20200302 | 1.0 | 27.0 | 2.0 | 1.0 | Local transmission | 0 |
20200303 | 1.0 | 33.0 | 6.0 | 0.0 | Local transmission | 0 | |
20200304 | 1.0 | 43.0 | 10.0 | 0.0 | Local transmission | 0 | |
20200305 | 2.0 | 57.0 | 14.0 | 2.0 | Local transmission | 0 | |
20200306 | 2.0 | 57.0 | 0.0 | 0.0 | Local transmission | 1 | |
20200307 | 2.0 | 62.0 | 5.0 | 0.0 | Local transmission | 0 | |
20200308 | 3.0 | 74.0 | 12.0 | 1.0 | Local transmission | 0 | |
20200309 | 3.0 | 77.0 | 3.0 | 0.0 | Local transmission | 0 | |
20200310 | 3.0 | 92.0 | 15.0 | 0.0 | Local transmission | 0 | |
20200311 | 3.0 | 112.0 | 20.0 | 0.0 | Local transmission | 0 | |
20200312 | 3.0 | 122.0 | 10.0 | 0.0 | Local transmission | 0 | |
20200313 | 3.0 | 140.0 | 18.0 | 0.0 | Local transmission | 0 | |
... | ... | ... | ... | ... | ... | ... | ... |
Turkey | 20200319 | 2.0 | 191.0 | 51.0 | 1.0 | Local transmission | 0 |
20200320 | 2.0 | 191.0 | 0.0 | 0.0 | Local transmission | 1 | |
20200321 | 9.0 | 670.0 | 479.0 | 7.0 | Local transmission | 0 | |
20200322 | 21.0 | 947.0 | 277.0 | 12.0 | Local transmission | 0 | |
Ukraine | 20200304 | 0.0 | 1.0 | 1.0 | 0.0 | Imported cases only | 0 |
Viet Nam | 20200302 | 0.0 | 16.0 | 0.0 | 0.0 | Local transmission | 18 |
20200303 | 0.0 | 16.0 | 0.0 | 0.0 | Local transmission | 19 | |
20200304 | 0.0 | 16.0 | 0.0 | 0.0 | Local transmission | 20 | |
20200305 | 0.0 | 16.0 | 0.0 | 0.0 | Local transmission | 21 | |
20200306 | 0.0 | 16.0 | 0.0 | 0.0 | Local transmission | 22 | |
20200307 | 0.0 | 17.0 | 1.0 | 0.0 | Local transmission | 0 | |
20200308 | 0.0 | 21.0 | 4.0 | 0.0 | Local transmission | 0 | |
20200309 | 0.0 | 30.0 | 9.0 | 0.0 | Local transmission | 0 | |
20200310 | 0.0 | 31.0 | 1.0 | 0.0 | Local transmission | 0 | |
20200311 | 0.0 | 35.0 | 4.0 | 0.0 | Local transmission | 0 | |
20200312 | 0.0 | 39.0 | 4.0 | 0.0 | Local transmission | 0 | |
20200313 | 0.0 | 39.0 | 0.0 | 0.0 | Local transmission | 1 | |
20200314 | 0.0 | 48.0 | 9.0 | 0.0 | Local transmission | 0 | |
20200315 | 0.0 | 53.0 | 5.0 | 0.0 | Local transmission | 0 | |
20200316 | 0.0 | 57.0 | 4.0 | 0.0 | Local transmission | 0 | |
20200317 | 0.0 | 61.0 | 4.0 | 0.0 | Local transmission | 0 | |
20200318 | 0.0 | 61.0 | 4.0 | 0.0 | Local transmission | 0 | |
20200319 | 0.0 | 66.0 | 5.0 | 0.0 | Local transmission | 0 | |
20200320 | 0.0 | 85.0 | 19.0 | 0.0 | Local transmission | 0 | |
20200321 | 0.0 | 91.0 | 6.0 | 0.0 | Local transmission | 0 | |
20200322 | 0.0 | 94.0 | 3.0 | 0.0 | Local transmission | 0 | |
the United \nKingdom^^ | 20200305 | 0.0 | 89.0 | 38.0 | 0.0 | Local transmission | 0 |
the United \nKingdom¶ | 20200304 | 0.0 | 51.0 | 12.0 | 0.0 | Local transmission | 0 |
the United Kingdom | 20200302 | 0.0 | 36.0 | 13.0 | 0.0 | Local transmission | 0 |
20200303 | 0.0 | 39.0 | 3.0 | 0.0 | Local transmission | 0 |
919 rows × 5 columns
grouped.reset_index(inplace=True)
italy = grouped[grouped['country'] == 'Italy']
italy
country | date | total_deaths | total_confirmed | total_new | total_new_deaths | transmission_class | days_since_report | |
---|---|---|---|---|---|---|---|---|
418 | Italy | 20200302 | 35 | 1689.0 | 561.0 | 6.0 | Local transmission | 0 |
419 | Italy | 20200303 | 52 | 2036.0 | 347.0 | 17.0 | Local transmission | 0 |
420 | Italy | 20200304 | 80 | 2502.0 | 466.0 | 28.0 | Local transmission | 0 |
421 | Italy | 20200305 | 107 | 3089.0 | 587.0 | 27.0 | Local transmission | 0 |
422 | Italy | 20200306 | 148 | 3858.0 | 769.0 | 41.0 | Local transmission | 0 |
423 | Italy | 20200307 | 197 | 4636.0 | 778.0 | 49.0 | Local transmission | 0 |
424 | Italy | 20200308 | 234 | 5883.0 | 1247.0 | 37.0 | Local transmission | 0 |
425 | Italy | 20200309 | 366 | 7375.0 | 1492.0 | 132.0 | Local transmission | 0 |
426 | Italy | 20200310 | 463 | 9172.0 | 1797.0 | 97.0 | Local transmission | 0 |
427 | Italy | 20200311 | 631 | 10149.0 | 977.0 | 168.0 | Local transmission | 0 |
428 | Italy | 20200312 | 827 | 12462.0 | 2313.0 | 196.0 | Local transmission | 0 |
429 | Italy | 20200313 | 1016 | 15113.0 | 2651.0 | 189.0 | Local transmission | 0 |
430 | Italy | 20200314 | 1268 | 17660.0 | 2547.0 | 252.0 | Local transmission | 0 |
431 | Italy | 20200315 | 1441 | 21157.0 | 3497.0 | 173.0 | Local transmission | 0 |
432 | Italy | 20200316 | 1809 | 24747.0 | 3590.0 | 368.0 | Local transmission | 0 |
433 | Italy | 20200317 | 2503 | 27980.0 | 3233.0 | 349.0 | Local transmission | 0 |
434 | Italy | 20200318 | 2503 | 31506.0 | 3526.0 | 345.0 | Local transmission | 0 |
435 | Italy | 20200319 | 2978 | 35713.0 | 4207.0 | 473.0 | Local transmission | 0 |
436 | Italy | 20200320 | 3407 | 41035.0 | 5322.0 | 429.0 | Local transmission | 0 |
437 | Italy | 20200321 | 4032 | 47021.0 | 5986.0 | 625.0 | Local transmission | 0 |
438 | Italy | 20200322 | 4827 | 53578.0 | 6557.0 | 795.0 | Local transmission | 0 |
df = italy.copy()
df['corrections_death'] = df['total_deaths'] - df['total_deaths'].shift(+1)
df['corrections_new'] = df['total_confirmed'].shift(+1) + df['total_new']
df['conf_death'] = df['corrections_death'] == df['total_new_deaths']
df['conf_new'] = df['corrections_new'] == df['total_confirmed']
df
country | date | total_deaths | total_confirmed | total_new | total_new_deaths | transmission_class | days_since_report | corrections_death | corrections_new | conf_death | conf_new | |
---|---|---|---|---|---|---|---|---|---|---|---|---|
418 | Italy | 20200302 | 35 | 1689.0 | 561.0 | 6.0 | Local transmission | 0 | NaN | NaN | False | False |
419 | Italy | 20200303 | 52 | 2036.0 | 347.0 | 17.0 | Local transmission | 0 | 17 | 2036.0 | True | True |
420 | Italy | 20200304 | 80 | 2502.0 | 466.0 | 28.0 | Local transmission | 0 | 28 | 2502.0 | True | True |
421 | Italy | 20200305 | 107 | 3089.0 | 587.0 | 27.0 | Local transmission | 0 | 27 | 3089.0 | True | True |
422 | Italy | 20200306 | 148 | 3858.0 | 769.0 | 41.0 | Local transmission | 0 | 41 | 3858.0 | True | True |
423 | Italy | 20200307 | 197 | 4636.0 | 778.0 | 49.0 | Local transmission | 0 | 49 | 4636.0 | True | True |
424 | Italy | 20200308 | 234 | 5883.0 | 1247.0 | 37.0 | Local transmission | 0 | 37 | 5883.0 | True | True |
425 | Italy | 20200309 | 366 | 7375.0 | 1492.0 | 132.0 | Local transmission | 0 | 132 | 7375.0 | True | True |
426 | Italy | 20200310 | 463 | 9172.0 | 1797.0 | 97.0 | Local transmission | 0 | 97 | 9172.0 | True | True |
427 | Italy | 20200311 | 631 | 10149.0 | 977.0 | 168.0 | Local transmission | 0 | 168 | 10149.0 | True | True |
428 | Italy | 20200312 | 827 | 12462.0 | 2313.0 | 196.0 | Local transmission | 0 | 196 | 12462.0 | True | True |
429 | Italy | 20200313 | 1016 | 15113.0 | 2651.0 | 189.0 | Local transmission | 0 | 189 | 15113.0 | True | True |
430 | Italy | 20200314 | 1268 | 17660.0 | 2547.0 | 252.0 | Local transmission | 0 | 252 | 17660.0 | True | True |
431 | Italy | 20200315 | 1441 | 21157.0 | 3497.0 | 173.0 | Local transmission | 0 | 173 | 21157.0 | True | True |
432 | Italy | 20200316 | 1809 | 24747.0 | 3590.0 | 368.0 | Local transmission | 0 | 368 | 24747.0 | True | True |
433 | Italy | 20200317 | 2503 | 27980.0 | 3233.0 | 349.0 | Local transmission | 0 | 694 | 27980.0 | False | True |
434 | Italy | 20200318 | 2503 | 31506.0 | 3526.0 | 345.0 | Local transmission | 0 | 0 | 31506.0 | False | True |
435 | Italy | 20200319 | 2978 | 35713.0 | 4207.0 | 473.0 | Local transmission | 0 | 475 | 35713.0 | False | True |
436 | Italy | 20200320 | 3407 | 41035.0 | 5322.0 | 429.0 | Local transmission | 0 | 429 | 41035.0 | True | True |
437 | Italy | 20200321 | 4032 | 47021.0 | 5986.0 | 625.0 | Local transmission | 0 | 625 | 47021.0 | True | True |
438 | Italy | 20200322 | 4827 | 53578.0 | 6557.0 | 795.0 | Local transmission | 0 | 795 | 53578.0 | True | True |
countries = set(grouped['country'])
len(countries)
73
corrected_dfs = []
for country in countries:
df = grouped[grouped['country'] == country]
df['corrections_death'] = df['total_deaths'] - df['total_deaths'].shift(+1)
df['corrections_new'] = df['total_confirmed'].shift(+1) + df['total_new']
df['conf_death'] = df['corrections_death'] == df['total_new_deaths']
df['conf_new'] = df['corrections_new'] == df['total_confirmed']
corrected_dfs.append(df)
/Users/danielcaraway/.local/lib/python3.7/site-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy after removing the cwd from sys.path. /Users/danielcaraway/.local/lib/python3.7/site-packages/ipykernel_launcher.py:5: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy """ /Users/danielcaraway/.local/lib/python3.7/site-packages/ipykernel_launcher.py:6: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy /Users/danielcaraway/.local/lib/python3.7/site-packages/ipykernel_launcher.py:7: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy import sys
# frame = pd.concat(li, axis=0, ignore_index=True)
updated_df = pd.concat(corrected_dfs, axis=0, ignore_index=True)
# grouped = pd.DataFrame(frame.groupby(['country', 'date','total_deaths']).sum())
updated_df
country | date | total_deaths | total_confirmed | total_new | total_new_deaths | transmission_class | days_since_report | corrections_death | corrections_new | conf_death | conf_new | |
---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | Slovakia | 20200308 | 0 | 3.0 | 2.0 | 0.0 | Local transmission | 0 | NaN | NaN | False | False |
1 | Slovakia | 20200309 | 0 | 5.0 | 2.0 | 0.0 | Local transmission | 0 | 0 | 5.0 | True | True |
2 | Slovakia | 20200310 | 0 | 7.0 | 2.0 | 0.0 | Local transmission | 0 | 0 | 7.0 | True | True |
3 | Slovakia | 20200313 | 0 | 21.0 | 11.0 | 0.0 | Local transmission | 0 | 0 | 18.0 | True | False |
4 | Slovakia | 20200314 | 0 | 30.0 | 9.0 | 0.0 | Local transmission | 0 | 0 | 30.0 | True | True |
5 | Slovakia | 20200315 | 0 | 44.0 | 14.0 | 0.0 | Local transmission | 0 | 0 | 44.0 | True | True |
6 | Slovakia | 20200316 | 0 | 61.0 | 17.0 | 0.0 | Local transmission | 0 | 0 | 61.0 | True | True |
7 | Slovakia | 20200317 | 0 | 72.0 | 11.0 | 0.0 | Local transmission | 0 | 0 | 72.0 | True | True |
8 | Slovakia | 20200318 | 0 | 97.0 | 25.0 | 0.0 | Local transmission | 0 | 0 | 97.0 | True | True |
9 | Slovakia | 20200319 | 0 | 105.0 | 8.0 | 0.0 | Local transmission | 0 | 0 | 105.0 | True | True |
10 | Lithuania | 20200302 | 0 | 1.0 | 0.0 | 0.0 | Imported cases only | 3 | NaN | NaN | False | False |
11 | Lithuania | 20200303 | 0 | 1.0 | 0.0 | 0.0 | Imported cases only | 4 | 0 | 1.0 | True | True |
12 | Lithuania | 20200304 | 0 | 1.0 | 0.0 | 0.0 | Imported cases only | 5 | 0 | 1.0 | True | True |
13 | Lithuania | 20200305 | 0 | 1.0 | 0.0 | 0.0 | Imported cases only | 6 | 0 | 1.0 | True | True |
14 | Lithuania | 20200306 | 0 | 1.0 | 0.0 | 0.0 | Imported cases only | 7 | 0 | 1.0 | True | True |
15 | Croatia | 20200302 | 0 | 7.0 | 0.0 | 0.0 | Local transmission | 1 | NaN | NaN | False | False |
16 | Croatia | 20200303 | 0 | 8.0 | 2.0 | 0.0 | Local transmission | 0 | 0 | 9.0 | True | False |
17 | Croatia | 20200304 | 0 | 9.0 | 1.0 | 0.0 | Local transmission | 0 | 0 | 9.0 | True | True |
18 | Croatia | 20200305 | 0 | 9.0 | 0.0 | 0.0 | Local transmission | 1 | 0 | 9.0 | True | True |
19 | Croatia | 20200306 | 0 | 10.0 | 1.0 | 0.0 | Local transmission | 0 | 0 | 10.0 | True | True |
20 | Croatia | 20200307 | 0 | 11.0 | 1.0 | 0.0 | Local transmission | 0 | 0 | 11.0 | True | True |
21 | Croatia | 20200308 | 0 | 11.0 | 0.0 | 0.0 | Local transmission | 1 | 0 | 11.0 | True | True |
22 | Croatia | 20200309 | 0 | 11.0 | 0.0 | 0.0 | Local transmission | 2 | 0 | 11.0 | True | True |
23 | Croatia | 20200310 | 0 | 12.0 | 1.0 | 0.0 | Local transmission | 0 | 0 | 12.0 | True | True |
24 | Croatia | 20200311 | 0 | 16.0 | 4.0 | 0.0 | Local transmission | 0 | 0 | 16.0 | True | True |
25 | Croatia | 20200312 | 0 | 16.0 | 0.0 | 0.0 | Local transmission | 1 | 0 | 16.0 | True | True |
26 | Croatia | 20200313 | 0 | 16.0 | 0.0 | 0.0 | Local transmission | 2 | 0 | 16.0 | True | True |
27 | Croatia | 20200314 | 0 | 27.0 | 11.0 | 0.0 | Local transmission | 0 | 0 | 27.0 | True | True |
28 | Croatia | 20200315 | 0 | 37.0 | 10.0 | 0.0 | Local transmission | 0 | 0 | 37.0 | True | True |
29 | Croatia | 20200316 | 0 | 49.0 | 12.0 | 0.0 | Local transmission | 0 | 0 | 49.0 | True | True |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
889 | Austria | 20200314 | 1 | 504.0 | 143.0 | 0.0 | Local transmission | 0 | 0 | 504.0 | True | True |
890 | Austria | 20200315 | 1 | 800.0 | 296.0 | 0.0 | Local transmission | 0 | 0 | 800.0 | True | True |
891 | Austria | 20200316 | 1 | 959.0 | 159.0 | 0.0 | Local transmission | 0 | 0 | 959.0 | True | True |
892 | Austria | 20200317 | 3 | 1132.0 | 173.0 | 2.0 | Local transmission | 0 | 2 | 1132.0 | True | True |
893 | Austria | 20200318 | 3 | 1332.0 | 373.0 | 2.0 | Local transmission | 0 | 0 | 1505.0 | False | False |
894 | Austria | 20200319 | 4 | 1646.0 | 314.0 | 1.0 | Local transmission | 0 | 1 | 1646.0 | True | True |
895 | Austria | 20200320 | 5 | 1843.0 | 197.0 | 1.0 | Local transmission | 0 | 1 | 1843.0 | True | True |
896 | Austria | 20200321 | 6 | 2649.0 | 806.0 | 1.0 | Local transmission | 0 | 1 | 2649.0 | True | True |
897 | Austria | 20200322 | 8 | 3024.0 | 375.0 | 2.0 | Local transmission | 0 | 2 | 3024.0 | True | True |
898 | Republic of Korea | 20200302 | 22 | 4212.0 | 476.0 | 4.0 | Local transmission | 0 | NaN | NaN | False | False |
899 | Republic of Korea | 20200303 | 28 | 4812.0 | 600.0 | 6.0 | Local transmission | 0 | 6 | 4812.0 | True | True |
900 | Republic of Korea | 20200304 | 32 | 5328.0 | 516.0 | 4.0 | Local transmission | 0 | 4 | 5328.0 | True | True |
901 | Republic of Korea | 20200305 | 35 | 5766.0 | 438.0 | 3.0 | Local transmission | 0 | 3 | 5766.0 | True | True |
902 | Republic of Korea | 20200306 | 42 | 6284.0 | 518.0 | 7.0 | Local transmission | 0 | 7 | 6284.0 | True | True |
903 | Republic of Korea | 20200307 | 44 | 6767.0 | 483.0 | 2.0 | Local transmission | 0 | 2 | 6767.0 | True | True |
904 | Republic of Korea | 20200308 | 50 | 7134.0 | 367.0 | 6.0 | Local transmission | 0 | 6 | 7134.0 | True | True |
905 | Republic of Korea | 20200309 | 51 | 7382.0 | 248.0 | 1.0 | Local transmission | 0 | 1 | 7382.0 | True | True |
906 | Republic of Korea | 20200310 | 54 | 7513.0 | 131.0 | 3.0 | Local transmission | 0 | 3 | 7513.0 | True | True |
907 | Republic of Korea | 20200311 | 60 | 7755.0 | 242.0 | 6.0 | Local transmission | 0 | 6 | 7755.0 | True | True |
908 | Republic of Korea | 20200312 | 66 | 7869.0 | 114.0 | 6.0 | Local transmission | 0 | 6 | 7869.0 | True | True |
909 | Republic of Korea | 20200313 | 66 | 7979.0 | 110.0 | 0.0 | Local transmission | 0 | 0 | 7979.0 | True | True |
910 | Republic of Korea | 20200314 | 72 | 8086.0 | 107.0 | 6.0 | Local transmission | 0 | 6 | 8086.0 | True | True |
911 | Republic of Korea | 20200315 | 75 | 8162.0 | 76.0 | 3.0 | Local transmission | 0 | 3 | 8162.0 | True | True |
912 | Republic of Korea | 20200316 | 75 | 8236.0 | 74.0 | 0.0 | Local transmission | 0 | 0 | 8236.0 | True | True |
913 | Republic of Korea | 20200317 | 81 | 8320.0 | 84.0 | 6.0 | Local transmission | 0 | 6 | 8320.0 | True | True |
914 | Republic of Korea | 20200318 | 81 | 8320.0 | 84.0 | 6.0 | Local transmission | 0 | 0 | 8404.0 | False | False |
915 | Republic of Korea | 20200319 | 84 | 8413.0 | 93.0 | 3.0 | Local transmission | 0 | 3 | 8413.0 | True | True |
916 | Republic of Korea | 20200320 | 94 | 8652.0 | 239.0 | 10.0 | Local transmission | 0 | 10 | 8652.0 | True | True |
917 | Republic of Korea | 20200321 | 102 | 8799.0 | 147.0 | 8.0 | Local transmission | 0 | 8 | 8799.0 | True | True |
918 | Republic of Korea | 20200322 | 104 | 8897.0 | 98.0 | 2.0 | Local transmission | 0 | 2 | 8897.0 | True | True |
919 rows × 12 columns
updated_df.to_csv('covid19_whositreps_merged_and_checked.csv', index=False)
import numpy as np
false_death = updated_df[(updated_df['conf_death'] == False) & (np.isnan(updated_df['corrections_new']) == False )]
false_death
country | date | total_deaths | total_confirmed | total_new | total_new_deaths | transmission_class | days_since_report | corrections_death | corrections_new | conf_death | conf_new | |
---|---|---|---|---|---|---|---|---|---|---|---|---|
32 | Croatia | 20200322 | 1 | 206.0 | 80.0 | 0.0 | Local transmission | 0 | 1 | 145.0 | False | False |
105 | Netherlands | 20200319 | 58 | 2051.0 | 0.0 | 0.0 | Local transmission | 1 | 15 | 1705.0 | False | False |
173 | Italy | 20200317 | 2503 | 27980.0 | 3233.0 | 349.0 | Local transmission | 0 | 694 | 27980.0 | False | True |
174 | Italy | 20200318 | 2503 | 31506.0 | 3526.0 | 345.0 | Local transmission | 0 | 0 | 31506.0 | False | True |
175 | Italy | 20200319 | 2978 | 35713.0 | 4207.0 | 473.0 | Local transmission | 0 | 475 | 35713.0 | False | True |
191 | The United Kingdom | 20200318 | 55 | 1954.0 | 407.0 | 5.0 | Local transmission | 0 | 0 | 1954.0 | False | True |
192 | The United Kingdom | 20200319 | 103 | 2630.0 | 672.0 | 0.0 | Local transmission | 0 | 48 | 2626.0 | False | False |
221 | Australia | 20200305 | 2 | 57.0 | 14.0 | 2.0 | Local transmission | 0 | 1 | 57.0 | False | True |
232 | Australia | 20200316 | 5 | 298.0 | 0.0 | 0.0 | Local transmission | 1 | 2 | 249.0 | False | False |
347 | Philippines | 20200316 | 12 | 140.0 | 0.0 | 0.0 | Local transmission | 1 | 6 | 111.0 | False | False |
438 | France | 20200319 | 244 | 9043.0 | 0.0 | 0.0 | Local transmission | 1 | 69 | 7652.0 | False | False |
478 | Denmark | 20200318 | 4 | 977.0 | 79.0 | 3.0 | Local transmission | 0 | 0 | 1039.0 | False | False |
501 | China | 20200318 | 3231 | 81116.0 | 39.0 | 13.0 | Local transmission | 0 | 0 | 81155.0 | False | False |
549 | Germany | 20200320 | 20 | 10999.0 | 2801.0 | 8.0 | Local transmission | 0 | 7 | 10999.0 | False | True |
592 | Japan | 20200318 | 28 | 829.0 | 15.0 | 4.0 | Local transmission | 0 | 0 | 844.0 | False | False |
601 | Bulgaria | 20200315 | 2 | 43.0 | 36.0 | 1.0 | Local transmission | 0 | 2 | 42.0 | False | False |
664 | Switzerland | 20200318 | 14 | 2650.0 | 450.0 | 5.0 | Local transmission | 0 | 0 | 2650.0 | False | True |
665 | Switzerland | 20200319 | 21 | 3010.0 | 353.0 | 2.0 | Local transmission | 0 | 7 | 3003.0 | False | False |
787 | Greece | 20200319 | 5 | 418.0 | 0.0 | 0.0 | Local transmission | 1 | 1 | 387.0 | False | False |
893 | Austria | 20200318 | 3 | 1332.0 | 373.0 | 2.0 | Local transmission | 0 | 0 | 1505.0 | False | False |
914 | Republic of Korea | 20200318 | 81 | 8320.0 | 84.0 | 6.0 | Local transmission | 0 | 0 | 8404.0 | False | False |
false_new = updated_df[(updated_df['conf_new'] == False) & (np.isnan(updated_df['corrections_new']) == False )]
false_new['diff'] = false_new['corrections_new'] - false_new['total_confirmed']
/Users/danielcaraway/.local/lib/python3.7/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy """Entry point for launching an IPython kernel.
false_new
country | date | total_deaths | total_confirmed | total_new | total_new_deaths | transmission_class | days_since_report | corrections_death | corrections_new | conf_death | conf_new | diff | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
3 | Slovakia | 20200313 | 0 | 21.0 | 11.0 | 0.0 | Local transmission | 0 | 0 | 18.0 | True | False | -3.0 |
16 | Croatia | 20200303 | 0 | 8.0 | 2.0 | 0.0 | Local transmission | 0 | 0 | 9.0 | True | False | 1.0 |
32 | Croatia | 20200322 | 1 | 206.0 | 80.0 | 0.0 | Local transmission | 0 | 1 | 145.0 | False | False | -61.0 |
49 | Czechia | 20200318 | 0 | 434.0 | 136.0 | 0.0 | Local transmission | 0 | 0 | 519.0 | True | False | 85.0 |
50 | Czechia | 20200319 | 0 | 522.0 | 30.0 | 0.0 | Local transmission | 0 | 0 | 464.0 | True | False | -58.0 |
105 | Netherlands | 20200319 | 58 | 2051.0 | 0.0 | 0.0 | Local transmission | 1 | 15 | 1705.0 | False | False | -346.0 |
111 | Norway | 20200304 | 0 | 33.0 | 7.0 | 0.0 | Local transmission | 0 | 0 | 32.0 | True | False | -1.0 |
154 | Serbia†† | 20200318 | 0 | 85.0 | 23.0 | 0.0 | Local transmission | 0 | 0 | 93.0 | True | False | 8.0 |
156 | Serbia†† | 20200320 | 0 | 123.0 | 41.0 | 0.0 | Local transmission | 0 | 0 | 137.0 | True | False | 14.0 |
192 | The United Kingdom | 20200319 | 103 | 2630.0 | 672.0 | 0.0 | Local transmission | 0 | 48 | 2626.0 | False | False | -4.0 |
232 | Australia | 20200316 | 5 | 298.0 | 0.0 | 0.0 | Local transmission | 1 | 2 | 249.0 | False | False | -49.0 |
234 | Australia | 20200318 | 5 | 414.0 | 78.0 | 0.0 | Local transmission | 0 | 0 | 453.0 | True | False | 39.0 |
253 | Luxembourg | 20200319 | 2 | 210.0 | 63.0 | 1.0 | Local transmission | 0 | 1 | 203.0 | True | False | -7.0 |
284 | Israel | 20200318 | 0 | 304.0 | 0.0 | 0.0 | Local transmission | 1 | 0 | 250.0 | True | False | -54.0 |
285 | Israel | 20200319 | 0 | 427.0 | 0.0 | 0.0 | Local transmission | 2 | 0 | 304.0 | True | False | -123.0 |
316 | Slovenia | 20200319 | 1 | 286.0 | 0.0 | 0.0 | Local transmission | 1 | 0 | 275.0 | True | False | -11.0 |
347 | Philippines | 20200316 | 12 | 140.0 | 0.0 | 0.0 | Local transmission | 1 | 6 | 111.0 | False | False | -29.0 |
349 | Philippines | 20200318 | 12 | 187.0 | 45.0 | 0.0 | Local transmission | 0 | 0 | 232.0 | True | False | 45.0 |
363 | Iceland | 20200311 | 0 | 61.0 | 0.0 | 0.0 | Local transmission | 1 | 0 | 55.0 | True | False | -6.0 |
369 | Iceland | 20200317 | 0 | 199.0 | 19.0 | 0.0 | Local transmission | 0 | 0 | 157.0 | True | False | -42.0 |
370 | Iceland | 20200318 | 0 | 225.0 | 45.0 | 0.0 | Local transmission | 0 | 0 | 244.0 | True | False | 19.0 |
413 | Hungary | 20200308 | 0 | 7.0 | 3.0 | 0.0 | Local transmission | 0 | 0 | 8.0 | True | False | 1.0 |
425 | France | 20200306 | 6 | 420.0 | 138.0 | 2.0 | Local transmission | 0 | 2 | 350.0 | True | False | -70.0 |
438 | France | 20200319 | 244 | 9043.0 | 0.0 | 0.0 | Local transmission | 1 | 69 | 7652.0 | False | False | -1391.0 |
445 | Portugal | 20200306 | 0 | 9.0 | 0.0 | 0.0 | Local transmission | 1 | 0 | 7.0 | True | False | -2.0 |
478 | Denmark | 20200318 | 4 | 977.0 | 79.0 | 3.0 | Local transmission | 0 | 0 | 1039.0 | False | False | 62.0 |
501 | China | 20200318 | 3231 | 81116.0 | 39.0 | 13.0 | Local transmission | 0 | 0 | 81155.0 | False | False | 39.0 |
571 | New Zealand | 20200318 | 0 | 11.0 | 5.0 | 0.0 | Local transmission | 0 | 0 | 16.0 | True | False | 5.0 |
582 | Japan | 20200308 | 6 | 455.0 | 48.0 | 0.0 | Local transmission | 0 | 0 | 456.0 | True | False | 1.0 |
592 | Japan | 20200318 | 28 | 829.0 | 15.0 | 4.0 | Local transmission | 0 | 0 | 844.0 | False | False | 15.0 |
601 | Bulgaria | 20200315 | 2 | 43.0 | 36.0 | 1.0 | Local transmission | 0 | 2 | 42.0 | False | False | -1.0 |
643 | Viet Nam | 20200318 | 0 | 61.0 | 4.0 | 0.0 | Local transmission | 0 | 0 | 65.0 | True | False | 4.0 |
652 | Switzerland | 20200306 | 1 | 86.0 | 30.0 | 1.0 | Local transmission | 0 | 1 | 67.0 | True | False | -19.0 |
665 | Switzerland | 20200319 | 21 | 3010.0 | 353.0 | 2.0 | Local transmission | 0 | 7 | 3003.0 | False | False | -7.0 |
705 | Cambodia | 20200316 | 0 | 12.0 | 0.0 | 0.0 | Local transmission | 1 | 0 | 7.0 | True | False | -5.0 |
707 | Cambodia | 20200318 | 0 | 24.0 | 12.0 | 0.0 | Local transmission | 0 | 0 | 36.0 | True | False | 12.0 |
745 | Poland | 20200319 | 5 | 287.0 | 0.0 | 0.0 | Local transmission | 1 | 0 | 246.0 | True | False | -41.0 |
787 | Greece | 20200319 | 5 | 418.0 | 0.0 | 0.0 | Local transmission | 1 | 1 | 387.0 | False | False | -31.0 |
884 | Austria | 20200309 | 0 | 112.0 | 10.0 | 0.0 | Local transmission | 0 | 0 | 114.0 | True | False | 2.0 |
893 | Austria | 20200318 | 3 | 1332.0 | 373.0 | 2.0 | Local transmission | 0 | 0 | 1505.0 | False | False | 173.0 |
914 | Republic of Korea | 20200318 | 81 | 8320.0 | 84.0 | 6.0 | Local transmission | 0 | 0 | 8404.0 | False | False | 84.0 |