COVID19 -- Checking the WHO Numbers¶
In [88]:
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)
In [87]:
frame = pd.concat(li, axis=0, ignore_index=True)
frame
Out[87]:
| 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
In [95]:
grouped = pd.DataFrame(frame.groupby(['country', 'date','total_deaths']).sum())
In [96]:
grouped
Out[96]:
| 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
In [98]:
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)
In [100]:
frame = pd.concat(li, axis=0, ignore_index=True)
grouped = pd.DataFrame(frame.groupby(['country', 'date','total_deaths']).sum())
Out[100]:
| 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
TESTING ITALY¶
In [102]:
grouped.reset_index(inplace=True)
italy = grouped[grouped['country'] == 'Italy']
In [103]:
italy
Out[103]:
| 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 |
In [118]:
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']
In [122]:
df['conf_death'] = df['corrections_death'] == df['total_new_deaths']
df['conf_new'] = df['corrections_new'] == df['total_confirmed']
In [123]:
df
Out[123]:
| 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 |
In [127]:
countries = set(grouped['country'])
len(countries)
Out[127]:
73
In [128]:
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
In [129]:
# 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())
In [130]:
updated_df
Out[130]:
| 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
In [131]:
updated_df.to_csv('covid19_whositreps_merged_and_checked.csv', index=False)
Fixing the NaN issue¶
In [135]:
import numpy as np
false_death = updated_df[(updated_df['conf_death'] == False) & (np.isnan(updated_df['corrections_new']) == False )]
In [136]:
false_death
Out[136]:
| 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 |
In [137]:
false_new = updated_df[(updated_df['conf_new'] == False) & (np.isnan(updated_df['corrections_new']) == False )]
In [139]:
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.
In [140]:
false_new
Out[140]:
| 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 |
In [141]:
import seaborn as sns; sns.set()
import matplotlib.pyplot as plt
In [148]:
for country in countries:
df = grouped[grouped['country'] == country]
plt.figure()
ax = sns.lineplot(x="date", y="total_confirmed", data=df)
plt.show
/Users/danielcaraway/.local/lib/python3.7/site-packages/ipykernel_launcher.py:5: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). """
In [151]:
df = grouped.copy()
df['datetime'] = pd.to_datetime(df['date'])
grouped = df.copy()
In [181]:
for country in countries:
df = grouped[grouped['country'] == country]
plt.figure()
ax = sns.lineplot(x="datetime", y="total_confirmed", hue="total_new", data=df)
plt.xticks(rotation=70)
plt.title(country)
plt.show
In [182]:
# grouped = grouped_sorted.copy()
countries = set(grouped_sorted[:50]['country'])
for country in countries:
df = grouped[grouped['country'] == country]
ax = sns.lineplot(x="datetime", y="total_confirmed", data=df, label=country)
# ax = sns.lineplot(x="datetime", y="total_deaths", data=df, label=country)
ax.legend()
plt.xticks(rotation=70)
In [163]:
grouped_sorted = grouped.sort_values(by='total_confirmed', ascending=False)
ax = sns.lineplot(x="datetime", y="total_confirmed", data=grouped_sorted[:10], hue="country")
In [165]:
grouped_sorted
Out[165]:
| country | date | total_deaths | total_confirmed | total_new | total_new_deaths | transmission_class | days_since_report | datetime | |
|---|---|---|---|---|---|---|---|---|---|
| 157 | China | 20200322 | 3267 | 81498.0 | 82.0 | 6.0 | Local transmission | 0 | 2020-03-22 |
| 156 | China | 20200321 | 3261 | 81416.0 | 116.0 | 8.0 | Local transmission | 0 | 2020-03-21 |
| 155 | China | 20200320 | 3253 | 81300.0 | 126.0 | 11.0 | Local transmission | 0 | 2020-03-20 |
| 154 | China | 20200319 | 3242 | 81174.0 | 58.0 | 11.0 | Local transmission | 0 | 2020-03-19 |
| 153 | China | 20200318 | 3231 | 81116.0 | 39.0 | 13.0 | Local transmission | 0 | 2020-03-18 |
| 152 | China | 20200317 | 3231 | 81116.0 | 39.0 | 13.0 | Local transmission | 0 | 2020-03-17 |
| 151 | China | 20200316 | 3218 | 81077.0 | 29.0 | 14.0 | Local Transmission | 0 | 2020-03-16 |
| 438 | Italy | 20200322 | 4827 | 53578.0 | 6557.0 | 795.0 | Local transmission | 0 | 2020-03-22 |
| 437 | Italy | 20200321 | 4032 | 47021.0 | 5986.0 | 625.0 | Local transmission | 0 | 2020-03-21 |
| 436 | Italy | 20200320 | 3407 | 41035.0 | 5322.0 | 429.0 | Local transmission | 0 | 2020-03-20 |
| 435 | Italy | 20200319 | 2978 | 35713.0 | 4207.0 | 473.0 | Local transmission | 0 | 2020-03-19 |
| 434 | Italy | 20200318 | 2503 | 31506.0 | 3526.0 | 345.0 | Local transmission | 0 | 2020-03-18 |
| 433 | Italy | 20200317 | 2503 | 27980.0 | 3233.0 | 349.0 | Local transmission | 0 | 2020-03-17 |
| 827 | Spain | 20200322 | 1326 | 24926.0 | 4946.0 | 324.0 | Local transmission | 0 | 2020-03-22 |
| 432 | Italy | 20200316 | 1809 | 24747.0 | 3590.0 | 368.0 | Local transmission | 0 | 2020-03-16 |
| 318 | Germany | 20200322 | 67 | 21463.0 | 3140.0 | 22.0 | Local transmission | 0 | 2020-03-22 |
| 431 | Italy | 20200315 | 1441 | 21157.0 | 3497.0 | 173.0 | Local transmission | 0 | 2020-03-15 |
| 826 | Spain | 20200321 | 1002 | 19980.0 | 2833.0 | 235.0 | Local transmission | 0 | 2020-03-21 |
| 317 | Germany | 20200321 | 45 | 18323.0 | 7324.0 | 25.0 | Local transmission | 0 | 2020-03-21 |
| 430 | Italy | 20200314 | 1268 | 17660.0 | 2547.0 | 252.0 | Local transmission | 0 | 2020-03-14 |
| 825 | Spain | 20200320 | 767 | 17147.0 | 3431.0 | 169.0 | Local transmission | 0 | 2020-03-20 |
| 429 | Italy | 20200313 | 1016 | 15113.0 | 2651.0 | 189.0 | Local transmission | 0 | 2020-03-13 |
| 282 | France | 20200322 | 562 | 14296.0 | 1821.0 | 112.0 | Local transmission | 0 | 2020-03-22 |
| 824 | Spain | 20200319 | 598 | 13716.0 | 2538.0 | 107.0 | Local transmission | 0 | 2020-03-19 |
| 281 | France | 20200321 | 450 | 12475.0 | 1598.0 | 78.0 | Local transmission | 0 | 2020-03-21 |
| 428 | Italy | 20200312 | 827 | 12462.0 | 2313.0 | 196.0 | Local transmission | 0 | 2020-03-12 |
| 823 | Spain | 20200318 | 491 | 11178.0 | 1987.0 | 182.0 | Local transmission | 0 | 2020-03-18 |
| 316 | Germany | 20200320 | 20 | 10999.0 | 2801.0 | 8.0 | Local transmission | 0 | 2020-03-20 |
| 280 | France | 20200320 | 372 | 10877.0 | 1834.0 | 128.0 | Local transmission | 0 | 2020-03-20 |
| 427 | Italy | 20200311 | 631 | 10149.0 | 977.0 | 168.0 | Local transmission | 0 | 2020-03-11 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 219 | Estonia | 20200303 | 0 | 1.0 | 0.0 | 0.0 | Imported cases only | 5 | 2020-03-03 |
| 109 | Brunei Darussalam | 20200311 | 0 | 1.0 | 0.0 | 0.0 | Imported cases only | 1 | 2020-03-11 |
| 218 | Estonia | 20200302 | 0 | 1.0 | 0.0 | 0.0 | Imported cases only | 4 | 2020-03-02 |
| 580 | North Macedonia | 20200304 | 0 | 1.0 | 0.0 | 0.0 | Imported cases only | 6 | 2020-03-04 |
| 579 | North Macedonia | 20200303 | 0 | 1.0 | 0.0 | 0.0 | Imported cases only | 6 | 2020-03-03 |
| 578 | North Macedonia | 20200302 | 0 | 1.0 | 0.0 | 0.0 | Imported cases only | 5 | 2020-03-02 |
| 828 | Sri Lanka | 20200302 | 0 | 1.0 | 0.0 | 0.0 | Imported cases only | 35 | 2020-03-02 |
| 130 | Cambodia | 20200302 | 0 | 1.0 | 0.0 | 0.0 | Imported cases only | 35 | 2020-03-02 |
| 131 | Cambodia | 20200303 | 0 | 1.0 | 0.0 | 0.0 | Imported cases only | 36 | 2020-03-03 |
| 132 | Cambodia | 20200304 | 0 | 1.0 | 0.0 | 0.0 | Imported cases only | 36 | 2020-03-04 |
| 133 | Cambodia | 20200305 | 0 | 1.0 | 0.0 | 0.0 | Imported cases only | 37 | 2020-03-05 |
| 528 | Mongolia | 20200316 | 0 | 1.0 | 0.0 | 0.0 | Imported cases only | 6 | 2020-03-16 |
| 527 | Mongolia | 20200315 | 0 | 1.0 | 0.0 | 0.0 | Imported cases only | 5 | 2020-03-15 |
| 526 | Mongolia | 20200314 | 0 | 1.0 | 0.0 | 0.0 | Imported cases only | 4 | 2020-03-14 |
| 519 | Monaco | 20200303 | 0 | 1.0 | 0.0 | 0.0 | Under investigation | 2 | 2020-03-03 |
| 340 | Holy See | 20200307 | 0 | 1.0 | 1.0 | 0.0 | Under investigation | 1 | 2020-03-07 |
| 607 | Papua New Guinea | 20200321 | 0 | 1.0 | 1.0 | 0.0 | Imported cases only | 0 | 2020-03-21 |
| 70 | Belarus | 20200302 | 0 | 1.0 | 0.0 | 0.0 | Imported cases only | 3 | 2020-03-02 |
| 71 | Belarus | 20200303 | 0 | 1.0 | 0.0 | 0.0 | Imported cases only | 4 | 2020-03-03 |
| 72 | Belarus | 20200304 | 0 | 1.0 | 0.0 | 0.0 | Imported cases only | 5 | 2020-03-04 |
| 518 | Monaco | 20200302 | 0 | 1.0 | 0.0 | 0.0 | Under investigation | 1 | 2020-03-02 |
| 520 | Monaco | 20200304 | 0 | 1.0 | 0.0 | 0.0 | Under investigation | 3 | 2020-03-04 |
| 525 | Mongolia | 20200313 | 0 | 1.0 | 0.0 | 0.0 | Imported cases only | 3 | 2020-03-13 |
| 730 | San Marino | 20200302 | 0 | 1.0 | 0.0 | 0.0 | Local transmission | 2 | 2020-03-02 |
| 521 | Monaco | 20200305 | 0 | 1.0 | 0.0 | 0.0 | Under investigation | 4 | 2020-03-05 |
| 522 | Mongolia | 20200310 | 0 | 1.0 | 1.0 | 0.0 | Imported cases only | 0 | 2020-03-10 |
| 523 | Mongolia | 20200311 | 0 | 1.0 | 0.0 | 0.0 | Imported cases only | 1 | 2020-03-11 |
| 524 | Mongolia | 20200312 | 0 | 1.0 | 0.0 | 0.0 | Imported cases only | 2 | 2020-03-12 |
| 82 | Belgium | 20200302 | 0 | 1.0 | 0.0 | 0.0 | Imported cases only | 27 | 2020-03-02 |
| 478 | Luxembourg | 20200303 | 0 | 1.0 | 0.0 | 0.0 | Imported cases only | 1 | 2020-03-03 |
919 rows × 9 columns
In [176]:
import seaborn as sns; sns.set()
import matplotlib.pyplot as plt
fmri = sns.load_dataset("fmri")
ax = sns.lineplot(x="timepoint", y="signal", data=fmri)
In [178]:
ax = sns.lineplot(x="timepoint", y="signal",
hue="event", style="event",
markers=True, dashes=False, data=fmri)
In [179]:
ax = sns.lineplot(x="timepoint", y="signal", hue="event",
units="subject", estimator=None, lw=1,
data=fmri.query("region == 'frontal'"))
In [177]:
fmri
Out[177]:
| subject | timepoint | event | region | signal | |
|---|---|---|---|---|---|
| 0 | s13 | 18 | stim | parietal | -0.017552 |
| 1 | s5 | 14 | stim | parietal | -0.080883 |
| 2 | s12 | 18 | stim | parietal | -0.081033 |
| 3 | s11 | 18 | stim | parietal | -0.046134 |
| 4 | s10 | 18 | stim | parietal | -0.037970 |
| 5 | s9 | 18 | stim | parietal | -0.103513 |
| 6 | s8 | 18 | stim | parietal | -0.064408 |
| 7 | s7 | 18 | stim | parietal | -0.060526 |
| 8 | s6 | 18 | stim | parietal | -0.007029 |
| 9 | s5 | 18 | stim | parietal | -0.040557 |
| 10 | s4 | 18 | stim | parietal | -0.048812 |
| 11 | s3 | 18 | stim | parietal | -0.047148 |
| 12 | s2 | 18 | stim | parietal | -0.086623 |
| 13 | s1 | 18 | stim | parietal | -0.046659 |
| 14 | s0 | 18 | stim | parietal | -0.075570 |
| 15 | s13 | 17 | stim | parietal | -0.008265 |
| 16 | s12 | 17 | stim | parietal | -0.088512 |
| 17 | s7 | 9 | stim | parietal | 0.058897 |
| 18 | s10 | 17 | stim | parietal | -0.016847 |
| 19 | s9 | 17 | stim | parietal | -0.121574 |
| 20 | s8 | 17 | stim | parietal | -0.076287 |
| 21 | s7 | 17 | stim | parietal | -0.043812 |
| 22 | s6 | 17 | stim | parietal | -0.014746 |
| 23 | s5 | 17 | stim | parietal | -0.056682 |
| 24 | s4 | 17 | stim | parietal | -0.044582 |
| 25 | s3 | 17 | stim | parietal | -0.053514 |
| 26 | s2 | 17 | stim | parietal | -0.077292 |
| 27 | s1 | 17 | stim | parietal | -0.038021 |
| 28 | s0 | 17 | stim | parietal | -0.071300 |
| 29 | s13 | 16 | stim | parietal | -0.002856 |
| ... | ... | ... | ... | ... | ... |
| 1034 | s5 | 13 | cue | frontal | -0.014985 |
| 1035 | s4 | 13 | cue | frontal | -0.021514 |
| 1036 | s3 | 13 | cue | frontal | -0.047639 |
| 1037 | s2 | 13 | cue | frontal | 0.047918 |
| 1038 | s1 | 13 | cue | frontal | 0.028379 |
| 1039 | s0 | 13 | cue | frontal | -0.021729 |
| 1040 | s13 | 12 | cue | frontal | -0.020686 |
| 1041 | s12 | 12 | cue | frontal | -0.003034 |
| 1042 | s11 | 12 | cue | frontal | 0.055766 |
| 1043 | s10 | 12 | cue | frontal | 0.005711 |
| 1044 | s9 | 12 | cue | frontal | 0.024292 |
| 1045 | s7 | 12 | cue | frontal | -0.014005 |
| 1046 | s2 | 7 | cue | frontal | -0.078363 |
| 1047 | s10 | 10 | cue | frontal | -0.016124 |
| 1048 | s8 | 10 | cue | frontal | -0.015141 |
| 1049 | s10 | 8 | cue | frontal | -0.052505 |
| 1050 | s9 | 8 | cue | frontal | -0.008729 |
| 1051 | s8 | 8 | cue | frontal | 0.007278 |
| 1052 | s7 | 8 | cue | frontal | 0.015765 |
| 1053 | s6 | 8 | cue | frontal | -0.063961 |
| 1054 | s5 | 8 | cue | frontal | -0.028292 |
| 1055 | s4 | 8 | cue | frontal | -0.160821 |
| 1056 | s3 | 8 | cue | frontal | -0.033848 |
| 1057 | s2 | 8 | cue | frontal | -0.069666 |
| 1058 | s1 | 8 | cue | frontal | -0.136059 |
| 1059 | s0 | 8 | cue | frontal | 0.018165 |
| 1060 | s13 | 7 | cue | frontal | -0.029130 |
| 1061 | s12 | 7 | cue | frontal | -0.004939 |
| 1062 | s11 | 7 | cue | frontal | -0.025367 |
| 1063 | s0 | 0 | cue | parietal | -0.006899 |
1064 rows × 5 columns
In [184]:
dots = sns.load_dataset("dots").query("align == 'dots'")
from matplotlib.colors import LogNorm
ax = sns.lineplot(x="time", y="firing_rate",
hue="coherence", style="choice",
hue_norm=LogNorm(), data=dots)
In [190]:
grouped = grouped.fillna(0)
ax = sns.lineplot(x="datetime", y="total_confirmed",
hue="total_deaths", style="country",
hue_norm=LogNorm(), data=grouped)
--------------------------------------------------------------------------- ValueError Traceback (most recent call last) <ipython-input-190-1d7766995544> in <module> 2 ax = sns.lineplot(x="datetime", y="total_confirmed", 3 hue="total_deaths", style="country", ----> 4 hue_norm=LogNorm(), data=grouped) ~/anaconda3/lib/python3.7/site-packages/seaborn/relational.py in lineplot(x, y, hue, size, style, data, palette, hue_order, hue_norm, sizes, size_order, size_norm, dashes, markers, style_order, units, estimator, ci, n_boot, sort, err_style, err_kws, legend, ax, **kwargs) 1076 dashes=dashes, markers=markers, style_order=style_order, 1077 units=units, estimator=estimator, ci=ci, n_boot=n_boot, -> 1078 sort=sort, err_style=err_style, err_kws=err_kws, legend=legend, 1079 ) 1080 ~/anaconda3/lib/python3.7/site-packages/seaborn/relational.py in __init__(self, x, y, hue, size, style, data, palette, hue_order, hue_norm, sizes, size_order, size_norm, dashes, markers, style_order, units, estimator, ci, n_boot, sort, err_style, err_kws, legend) 670 self.parse_hue(plot_data["hue"], palette, hue_order, hue_norm) 671 self.parse_size(plot_data["size"], sizes, size_order, size_norm) --> 672 self.parse_style(plot_data["style"], markers, dashes, style_order) 673 674 self.units = units ~/anaconda3/lib/python3.7/site-packages/seaborn/relational.py in parse_style(self, data, markers, dashes, order) 492 493 dashes = self.style_to_attributes( --> 494 levels, dashes, self.default_dashes, "dashes" 495 ) 496 ~/anaconda3/lib/python3.7/site-packages/seaborn/relational.py in style_to_attributes(self, levels, style, defaults, name) 303 if any(missing_levels): 304 err = "These `style` levels are missing {}: {}" --> 305 raise ValueError(err.format(name, missing_levels)) 306 307 return attrdict ValueError: These `style` levels are missing dashes: {'Lithuania', 'Croatia', 'the United Kingdom', 'the United \nKingdom^^', 'Norway', 'Russian Federation', 'Serbia††', 'Holy See', 'Italy', 'Bosnia and', 'Finland', 'Israel', 'Georgia', 'Slovenia', 'Brunei Darussalam', 'Iceland', 'Hungary', 'Latvia', 'Switzerland^^', 'Turkey', 'Mongolia', 'Germany', 'Fiji', 'Sweden', 'Viet Nam', 'Cambodia', 'Nepal', 'San Marino', 'Poland', 'Malaysia', 'Estonia', 'Ireland', 'Republic of Korea', 'Slovakia', 'Czechia', 'Malta', 'Monaco', 'North Macedonia', 'Serbia', 'Belarus', 'France^^', 'Netherlands', 'The United Kingdom', 'Luxembourg', 'Thailand', 'Ukraine', 'Philippines', 'Spain', 'Papua New Guinea', 'France', 'Portugal', 'Denmark', 'China', 'Bosnia and \nHerzegovina', 'India', 'New Zealand', 'Japan', 'Bulgaria', 'Liechtenstein', 'Indonesia', 'Romania', 'Greece', 'Singapore', 'Sri Lanka', 'Belgium', 'the United \nKingdom¶', 'Switzerland'}
In [188]:
dots
Out[188]:
| align | choice | time | coherence | firing_rate | |
|---|---|---|---|---|---|
| 0 | dots | T1 | -80 | 0.0 | 33.189967 |
| 1 | dots | T1 | -80 | 3.2 | 31.691726 |
| 2 | dots | T1 | -80 | 6.4 | 34.279840 |
| 3 | dots | T1 | -80 | 12.8 | 32.631874 |
| 4 | dots | T1 | -80 | 25.6 | 35.060487 |
| 5 | dots | T1 | -80 | 51.2 | 34.987571 |
| 6 | dots | T1 | -60 | 0.0 | 32.868526 |
| 7 | dots | T1 | -60 | 3.2 | 35.119048 |
| 8 | dots | T1 | -60 | 6.4 | 34.841076 |
| 9 | dots | T1 | -60 | 12.8 | 35.648148 |
| 10 | dots | T1 | -60 | 25.6 | 33.610568 |
| 11 | dots | T1 | -60 | 51.2 | 34.277344 |
| 12 | dots | T1 | -40 | 0.0 | 34.561753 |
| 13 | dots | T1 | -40 | 3.2 | 34.895833 |
| 14 | dots | T1 | -40 | 6.4 | 34.963325 |
| 15 | dots | T1 | -40 | 12.8 | 36.471193 |
| 16 | dots | T1 | -40 | 25.6 | 32.534247 |
| 17 | dots | T1 | -40 | 51.2 | 35.009766 |
| 18 | dots | T1 | -20 | 0.0 | 35.358566 |
| 19 | dots | T1 | -20 | 3.2 | 31.101190 |
| 20 | dots | T1 | -20 | 6.4 | 35.513447 |
| 21 | dots | T1 | -20 | 12.8 | 34.516461 |
| 22 | dots | T1 | -20 | 25.6 | 34.784736 |
| 23 | dots | T1 | -20 | 51.2 | 36.669922 |
| 24 | dots | T1 | 0 | 0.0 | 36.254980 |
| 25 | dots | T1 | 0 | 3.2 | 32.366071 |
| 26 | dots | T1 | 0 | 6.4 | 36.919315 |
| 27 | dots | T1 | 0 | 12.8 | 35.699588 |
| 28 | dots | T1 | 0 | 25.6 | 35.861057 |
| 29 | dots | T1 | 0 | 51.2 | 37.060547 |
| ... | ... | ... | ... | ... | ... |
| 364 | dots | T2 | 520 | 6.4 | 37.140278 |
| 365 | dots | T2 | 520 | 12.8 | 36.878647 |
| 366 | dots | T2 | 540 | 0.0 | 41.366654 |
| 367 | dots | T2 | 540 | 3.2 | 37.178237 |
| 368 | dots | T2 | 540 | 6.4 | 35.826389 |
| 369 | dots | T2 | 540 | 12.8 | 33.765733 |
| 370 | dots | T2 | 560 | 0.0 | 42.141137 |
| 371 | dots | T2 | 560 | 3.2 | 39.487807 |
| 372 | dots | T2 | 560 | 6.4 | 36.821442 |
| 373 | dots | T2 | 560 | 12.8 | 33.732677 |
| 374 | dots | T2 | 580 | 0.0 | 40.628540 |
| 375 | dots | T2 | 580 | 3.2 | 41.238455 |
| 376 | dots | T2 | 580 | 6.4 | 36.142279 |
| 377 | dots | T2 | 600 | 0.0 | 38.626129 |
| 378 | dots | T2 | 600 | 3.2 | 38.985971 |
| 379 | dots | T2 | 600 | 6.4 | 36.459301 |
| 380 | dots | T2 | 620 | 0.0 | 41.631373 |
| 381 | dots | T2 | 620 | 3.2 | 40.994116 |
| 382 | dots | T2 | 620 | 6.4 | 35.340847 |
| 383 | dots | T2 | 640 | 0.0 | 41.309376 |
| 384 | dots | T2 | 640 | 3.2 | 42.942077 |
| 385 | dots | T2 | 640 | 6.4 | 37.852326 |
| 386 | dots | T2 | 660 | 0.0 | 40.660000 |
| 387 | dots | T2 | 660 | 3.2 | 39.341914 |
| 388 | dots | T2 | 680 | 0.0 | 42.454916 |
| 389 | dots | T2 | 680 | 3.2 | 37.806267 |
| 390 | dots | T2 | 700 | 0.0 | 43.464959 |
| 391 | dots | T2 | 700 | 3.2 | 38.994559 |
| 392 | dots | T2 | 720 | 0.0 | 41.987121 |
| 393 | dots | T2 | 720 | 3.2 | 41.716057 |
394 rows × 5 columns
In [192]:
df = grouped.copy()
df['datetime'] = pd.to_datetime(df['date'])
grouped = df.copy()
In [193]:
italy = grouped[grouped['country'] == 'Italy']
In [194]:
italy
Out[194]:
| country | date | total_deaths | total_confirmed | total_new | total_new_deaths | transmission_class | days_since_report | datetime | |
|---|---|---|---|---|---|---|---|---|---|
| 418 | Italy | 20200302 | 35.0 | 1689.0 | 561.0 | 6.0 | Local transmission | 0 | 2020-03-02 |
| 419 | Italy | 20200303 | 52.0 | 2036.0 | 347.0 | 17.0 | Local transmission | 0 | 2020-03-03 |
| 420 | Italy | 20200304 | 80.0 | 2502.0 | 466.0 | 28.0 | Local transmission | 0 | 2020-03-04 |
| 421 | Italy | 20200305 | 107.0 | 3089.0 | 587.0 | 27.0 | Local transmission | 0 | 2020-03-05 |
| 422 | Italy | 20200306 | 148.0 | 3858.0 | 769.0 | 41.0 | Local transmission | 0 | 2020-03-06 |
| 423 | Italy | 20200307 | 197.0 | 4636.0 | 778.0 | 49.0 | Local transmission | 0 | 2020-03-07 |
| 424 | Italy | 20200308 | 234.0 | 5883.0 | 1247.0 | 37.0 | Local transmission | 0 | 2020-03-08 |
| 425 | Italy | 20200309 | 366.0 | 7375.0 | 1492.0 | 132.0 | Local transmission | 0 | 2020-03-09 |
| 426 | Italy | 20200310 | 463.0 | 9172.0 | 1797.0 | 97.0 | Local transmission | 0 | 2020-03-10 |
| 427 | Italy | 20200311 | 631.0 | 10149.0 | 977.0 | 168.0 | Local transmission | 0 | 2020-03-11 |
| 428 | Italy | 20200312 | 827.0 | 12462.0 | 2313.0 | 196.0 | Local transmission | 0 | 2020-03-12 |
| 429 | Italy | 20200313 | 1016.0 | 15113.0 | 2651.0 | 189.0 | Local transmission | 0 | 2020-03-13 |
| 430 | Italy | 20200314 | 1268.0 | 17660.0 | 2547.0 | 252.0 | Local transmission | 0 | 2020-03-14 |
| 431 | Italy | 20200315 | 1441.0 | 21157.0 | 3497.0 | 173.0 | Local transmission | 0 | 2020-03-15 |
| 432 | Italy | 20200316 | 1809.0 | 24747.0 | 3590.0 | 368.0 | Local transmission | 0 | 2020-03-16 |
| 433 | Italy | 20200317 | 2503.0 | 27980.0 | 3233.0 | 349.0 | Local transmission | 0 | 2020-03-17 |
| 434 | Italy | 20200318 | 2503.0 | 31506.0 | 3526.0 | 345.0 | Local transmission | 0 | 2020-03-18 |
| 435 | Italy | 20200319 | 2978.0 | 35713.0 | 4207.0 | 473.0 | Local transmission | 0 | 2020-03-19 |
| 436 | Italy | 20200320 | 3407.0 | 41035.0 | 5322.0 | 429.0 | Local transmission | 0 | 2020-03-20 |
| 437 | Italy | 20200321 | 4032.0 | 47021.0 | 5986.0 | 625.0 | Local transmission | 0 | 2020-03-21 |
| 438 | Italy | 20200322 | 4827.0 | 53578.0 | 6557.0 | 795.0 | Local transmission | 0 | 2020-03-22 |
In [195]:
ax = sns.lineplot(x="datetime", y="total_confirmed", data=italy)
In [198]:
N = 5
menMeans = (20, 35, 30, 35, 27)
womenMeans = (25, 32, 34, 20, 25)
menStd = (2, 3, 4, 1, 2)
womenStd = (3, 5, 2, 3, 3)
ind = np.arange(N) # the x locations for the groups
width = 0.35 # the width of the bars: can also be len(x) sequence
p1 = plt.bar(ind, menMeans, width, yerr=menStd)
p2 = plt.bar(ind, womenMeans, width,
bottom=menMeans, yerr=womenStd)
plt.ylabel('Scores')
plt.title('Scores by group and gender')
plt.xticks(ind, ('G1', 'G2', 'G3', 'G4', 'G5'))
plt.yticks(np.arange(0, 81, 10))
plt.legend((p1[0], p2[0]), ('Men', 'Women'))
plt.show()
In [202]:
italy['total_confirmed'].values
Out[202]:
1689.0
In [210]:
p1 = plt.bar(3, height=italy['total_confirmed'].values, width = 0.35)
In [ ]: