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.