Analyzing Chikungunya data

from pysus.online_data import SINAN, parquets_to_dataframe
import pandas as pd
%pylab inline

%pylab is deprecated, use %matplotlib inline and import the required libraries.
Populating the interactive namespace from numpy and matplotlib

casos = parquets_to_dataframe(SINAN.download('Chikungunya', 2015))

casos = casos[casos.ID_AGRAVO=='A920']

casos.head()

	TP_NOT	ID_AGRAVO	CS_SUSPEIT	DT_NOTIFIC	SEM_NOT	NU_ANO	SG_UF_NOT	ID_MUNICIP	ID_REGIONA	DT_SIN_PRI	...	COPAISINF	COMUNINF	DOENCA_TRA	EVOLUCAO	DT_OBITO	DT_ENCERRA	CS_FLXRET	FLXRECEBI	TP_SISTEMA	TPUNINOT
0	2	A920		2015-09-08	201536	2015	29	292630	1381	2015-09-05	...	1	292630				20151009	0	2	1
1	2	A920		2015-09-08	201536	2015	29	291360	1385	2015-08-28	...	1	291360				20151228	0	2	1
2	2	A920		2015-09-08	201536	2015	29	292740	1380	2015-09-01	...	0					20160111	0	2	1
3	2	A920		2015-09-08	201536	2015	29	292895	1381	2015-09-04	...	0					20151111	0	2	1
4	2	A920		2015-09-08	201536	2015	29	292895	1381	2015-09-05	...	0					20151111	0	2	1

5 rows × 38 columns

casos.DT_NOTIFIC = pd.to_datetime(casos.DT_NOTIFIC)

casos = casos.set_index('DT_NOTIFIC')

casos.ID_AGRAVO.resample('1W').count().plot();