SIM FTP Database¶

Sistema de Informação sobre Mortalidade¶

[1]:

from pysus.ftp.databases.sim import SIM
sim = SIM().load() # Loads the files from DATASUS

[2]:

sim.metadata

[2]:

{'long_name': 'Sistema de Informação sobre Mortalidade',
 'source': 'http://sim.saude.gov.br',
 'description': ''}

[3]:

sim.groups

[3]:

{'CID10': 'DO', 'CID9': 'DOR'}

[4]:

sim.paths

[4]:

[/dissemin/publicos/SIM/CID10/DORES, /dissemin/publicos/SIM/CID9/DORES]

[5]:

sim.get_files("CID9", uf="SP", year=1995)

[5]:

[DORSP95.DBC]

[6]:

sim.get_files("CID10", uf=["SP", "RJ"], year=[2019, 2020, 2021])

[6]:

[DORJ2019.dbc,
 DORJ2020.dbc,
 DORJ2021.dbc,
 DOSP2019.dbc,
 DOSP2020.dbc,
 DOSP2021.dbc]

[7]:

files = sim.get_files(["CID9", "CID10"], uf=["SP"], year=[1995, 2020])
sp_cid9, sp_cid10 = files

[8]:

sim.describe(sp_cid9)

[8]:

{'name': 'DORSP95.DBC',
 'uf': 'São Paulo',
 'year': 1995,
 'group': 'CID9',
 'size': '8.2 MB',
 'last_update': '2020-01-31 02:48PM'}

[9]:

sim.describe(sp_cid10)

[9]:

{'name': 'DOSP2020.dbc',
 'uf': 'São Paulo',
 'year': 2020,
 'group': 'CID10',
 'size': '28.7 MB',
 'last_update': '2022-03-31 04:19PM'}

You can rather download multiple files or download them individually:

[10]:

sim.download(sp_cid9) # Downloads to default directory

DORSP95.parquet: 100%|█████████████| 434k/434k [00:12<00:00, 36.0kB/s]

[10]:

[/home/bida/pysus/DORSP95.parquet]

[11]:

parquet = sp_cid9.download() # Or in a custom directory with `local_dir=`
parquet

[11]:

/home/bida/pysus/DORSP95.parquet

@Note: If the file has been downloaded already, it’s required to delete it in order to download the lastest updated file from DATASUS.

PySUS uses Parquets as output, use the method to_dataframe() to read the file as pandas DataFrame

[12]:

parquet.to_dataframe()

[12]:

	contador	CARTORIO	REGISTRO	DATAREG	TIPOBITO	DATAOBITO	ESTCIVIL	SEXO	DATANASC	IDADE	...	FONTINFO	ACIDTRAB	LOCACID	CRITICA	NUMEXPORT	CRSOCOR	CRSRES	RACACOR	ETNIA	UFINFORM
0	180001			951006	2	951002	2	1	19291003	465	...		0	0							35
1	180002			951006	2	951002	3	2	18980317	497	...		0	0							35
2	180003			951006	2	951003	2	2	19281002	467	...		0	0							35
3	180004			951006	2	951003	3	1	19110613	484	...		0	0							35
4	180005			951006	2	951004	1	1	19610914	434	...		0	0							35
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
227832	179996			951004	2	951001	4	1	19380423	457	...		0	0							35
227833	179997			951004	2	951001	2	1	19470130	448	...		0	0							35
227834	179998			951004	2	951001	3	2	19160113	479	...		0	0							35
227835	179999			951006	2	951001	1	1	19550901	440	...		0	0							35
227836	180000			951006	2	951001	1	1	19700510	425	...		0	0							35

227837 rows × 50 columns