Analyzing Chikungunya data
[1]:
from pysus.ftp.databases.sinan import SINAN
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
[2]:
sinan = SINAN().load()
[3]:
casos = sinan.download(sinan.get_files('CHIK', 2015)).to_dataframe()
100%|██████████████████████████████████████████████████████████████████████████████████████████████████████| 827k/827k [00:00<00:00, 355MB/s]
[4]:
casos
[4]:
| 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 | ||||||
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 53266 | 2 | A920 | 2015-09-08 | 201536 | 2015 | 29 | 292630 | 1381 | 2015-09-06 | ... | 1 | 292630 | 20151009 | 0 | 2 | 1 | |||||
| 53267 | 2 | A920 | 2015-09-08 | 201536 | 2015 | 29 | 292630 | 1381 | 2015-09-07 | ... | 1 | 292630 | 20151009 | 0 | 2 | 1 | |||||
| 53268 | 2 | A920 | 2015-09-08 | 201536 | 2015 | 29 | 292630 | 1381 | 2015-09-06 | ... | 1 | 292630 | 20151009 | 0 | 2 | 1 | |||||
| 53269 | 2 | A920 | 2015-09-08 | 201536 | 2015 | 29 | 292630 | 1381 | 2015-09-07 | ... | 1 | 292630 | 20151009 | 0 | 2 | 1 | |||||
| 53270 | 2 | A920 | 2015-09-08 | 201536 | 2015 | 29 | 292630 | 1381 | 2015-09-05 | ... | 1 | 292630 | 20151009 | 0 | 2 | 1 |
53271 rows × 38 columns
[5]:
casos = casos[casos.ID_AGRAVO=='A920']
casos.head()
[5]:
| 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
[6]:
casos.DT_NOTIFIC = pd.to_datetime(casos.DT_NOTIFIC)
[7]:
casos = casos.set_index('DT_NOTIFIC')
[8]:
casos.ID_AGRAVO.resample('1W').count().plot();
