Part 1 - Load cases

The first step is to import arcovid19, then we load the COVID-19 database from Argentina (CASES_URL). As a result, a table is obtained that presents a multiple pandas index, with the following hierarchy:

level 0: cod_provincia - Argentina states

level 1: cod_status - Four states of disease patients (R = Recovered, C = Confirmed, A = Active, D = Dead)

[1]:
import arcovid19 as ac
[2]:
cases = ac.load_cases()
[3]:
cases.head(4)
[3]:
provincia_status Pcia_status 2020-03-03 00:00:00 2020-03-04 00:00:00 2020-03-05 00:00:00 2020-03-06 00:00:00 2020-03-07 00:00:00 2020-03-08 00:00:00 2020-03-09 00:00:00 2020-03-10 00:00:00 ... 2020-05-19 00:00:00 2020-05-20 00:00:00 2020-05-21 00:00:00 2020-05-22 00:00:00 2020-05-23 00:00:00 2020-05-24 00:00:00 2020-05-25 00:00:00 2020-05-26 00:00:00 2020-05-27 00:00:00 2020-05-28 00:00:00
cod_provincia cod_status
CABA C CABA_C CABA Casos Confirmados 1.0 1.0 1.0 5.0 6.0 8.0 9.0 10.0 ... 3566.0 3823.0 4202.0 4606.0 5006.0 5500.0 5875.0 6202.0 6564.0 6989.0
R CABA_R CABA Casos Recuperados 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 1139.0 1139.0 1139.0 1139.0 1139.0 1678.0 1678.0 1678.0 1678.0 1963.0
D CABA_D CABA Casos Muertos 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 ... 135.0 137.0 139.0 146.0 152.0 155.0 163.0 168.0 172.0 175.0
A CABA_A CABA Casos Activos 1.0 1.0 1.0 5.0 5.0 7.0 8.0 9.0 ... 2292.0 2547.0 2924.0 3321.0 3715.0 3667.0 4034.0 4356.0 4714.0 4851.0

4 rows × 89 columns

[4]:
cases.shape
[4]:
(101, 89)
[5]:
cases.describe()
[5]:
2020-03-03 2020-03-04 2020-03-05 2020-03-06 2020-03-07 2020-03-08 2020-03-09 2020-03-10 2020-03-11 2020-03-12 ... 2020-05-19 2020-05-20 2020-05-21 2020-05-22 2020-05-23 2020-05-24 2020-05-25 2020-05-26 2020-05-27 2020-05-28
count 100.000000 101.000000 101.000000 101.000000 101.000000 101.000000 101.000000 101.000000 101.000000 101.000000 ... 101.000000 101.000000 101.000000 101.000000 101.000000 101.000000 101.000000 101.000000 101.000000 101.000000
mean 0.040000 0.039604 0.089109 0.346535 0.357673 0.478548 0.677393 0.753640 0.832725 1.232438 ... 348.871805 367.644097 393.307622 421.743290 449.624417 478.258056 500.119264 523.881659 551.802508 582.257972
std 0.196946 0.196000 0.349257 1.359674 1.432113 1.941601 2.603514 2.926856 3.252652 4.689155 ... 1176.302303 1248.414401 1348.455749 1460.412128 1572.673190 1664.493107 1752.146024 1823.126460 1933.166375 2034.023400
min 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 ... 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
25% 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 ... 2.000000 2.000000 2.000000 2.000000 2.000000 2.000000 2.000000 2.000000 3.000000 3.000000
50% 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 ... 14.000000 14.000000 15.000000 16.000000 16.000000 16.000000 17.000000 17.000000 17.000000 14.000000
75% 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 ... 89.000000 89.000000 90.000000 90.000000 90.000000 90.000000 90.000000 90.000000 89.000000 110.000000
max 1.000000 1.000000 2.000000 8.000000 9.000000 12.000000 17.000000 19.000000 21.000000 31.000000 ... 8809.000000 9283.000000 9931.000000 10649.000000 11353.000000 12076.000000 12628.000000 13228.000000 13933.000000 14702.000000

8 rows × 87 columns

Remember that the cases object has the properties of a DataFrame.