Part 2 - Functions¶
After loading the database we can use some of the functions of the class object Cases Frame.
[1]:
import arcovid19 as ac
[2]:
cases = ac.load_cases()
Using the method last_growth_rate and specifying the province argument of the function, we obtain the latest growth rate available for the whole country or by province.
If provincia = None, the method will yield the value of Argentina’s last growth rate.
[3]:
cases.last_growth_rate(provincia=None)
[3]:
0.05519270795952047
If you want to get the latest growth rate for any particular province, you must replace None for the full name of the province.
[4]:
cases.last_growth_rate(provincia='córdoba')
/home/docs/checkouts/readthedocs.org/user_builds/arcovid19/envs/latest/lib/python3.7/site-packages/arcovid19-0.6b0-py3.7.egg/arcovid19/cases.py:731: RuntimeWarning: divide by zero encountered in true_divide
growth_rate = np.array((I_n / I_n_1) - 1)
/home/docs/checkouts/readthedocs.org/user_builds/arcovid19/envs/latest/lib/python3.7/site-packages/arcovid19-0.6b0-py3.7.egg/arcovid19/cases.py:731: RuntimeWarning: invalid value encountered in true_divide
growth_rate = np.array((I_n / I_n_1) - 1)
[4]:
-0.002178649237472796
In the case that you want to estimate the growth rate in the period where we have data by province or country, you can use the grateful_full_period method.
Again, if the province argument equals None, the series shown corresponds to Argentina.
[5]:
cases.grate_full_period(provincia=None).head(5)
[5]:
2020-03-04 0
2020-03-05 1
2020-03-06 3
2020-03-07 0.125
2020-03-08 0.333333
Name: (ARG, growth_rate_C), dtype: object
[6]:
cases.grate_full_period(provincia='córdoba').head(5)
[6]:
2020-03-04 NaN
2020-03-05 NaN
2020-03-06 NaN
2020-03-07 0.0
2020-03-08 0.0
dtype: float64
In the case that you want the observations by date, that is, not cumulative, you can use the restore_time_serie method as shown in the next cell.
[7]:
cases.restore_time_serie().head(4)
[7]:
| 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 | 0.0 | 0.0 | 4.0 | 1.0 | 2.0 | 1.0 | 1.0 | ... | 224.0 | 257.0 | 379.0 | 404.0 | 400.0 | 494.0 | 375.0 | 327.0 | 362.0 | 425.0 |
| R | CABA_R | CABA Casos Recuperados | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 262.0 | 0.0 | 0.0 | 0.0 | 0.0 | 539.0 | 0.0 | 0.0 | 0.0 | 285.0 | |
| D | CABA_D | CABA Casos Muertos | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | ... | 5.0 | 2.0 | 2.0 | 7.0 | 6.0 | 3.0 | 8.0 | 5.0 | 4.0 | 3.0 | |
| A | CABA_A | CABA Casos Activos | 1.0 | 0.0 | 0.0 | 4.0 | 0.0 | 2.0 | 1.0 | 1.0 | ... | -43.0 | 255.0 | 377.0 | 397.0 | 394.0 | -48.0 | 367.0 | 322.0 | 358.0 | 137.0 |
4 rows × 89 columns