PySUS

This package collects a set of utilities for handling with public databases published by Brazil's DATASUS The documentation of how to use PySUS can be found here

Features

• Decode encoded patient age to any time unit (years, months, etc)
• Convert .dbc files to DBF databases or read them into pandas dataframes. DBC files are basically DBFs compressed by a proprietary algorithm.
• Read SINAN dbf files returning DataFrames with properly typed columns
• Download SINASC data
• Download SIH data
• Download SIA data
• Download SIM data
• Download CIHA data

Instalation

There are some dependencies which can't be installed through pip, namely libffi. Therefore on an ubuntu system:

sudo apt install libffi-dev

Then you can proceed to

sudo pip install PySUS

Examples

Reading SINAN files:

>>> from pysus.preprocessing.sinan import read_sinan_dbf

>>> df = read_sinan_dbf('mytest.dbf', encoding='latin-1')
>>> df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 65535 entries, 0 to 65534
Data columns (total 10 columns):
DT_DIGITA     65469 non-null object
DT_NOTIFIC    65535 non-null object
DT_SIN_PRI    65535 non-null object
ID_AGRAVO     65535 non-null object
ID_BAIRRO     50675 non-null float64
ID_MUNICIP    65535 non-null int64
NM_BAIRRO     60599 non-null object
NU_ANO        65535 non-null int64
SEM_NOT       65535 non-null int64
SEM_PRI       65535 non-null int64
dtypes: float64(1), int64(4), object(5)
memory usage: 5.0+ MB

>>> df.DT_DIGITA[0]
datetime.date(2016, 4, 1)

Reading .dbc file:

>>> from pysus.utilities.readdbc import read_dbc

>>> df = read_dbc(filename, encoding='iso-8859-1')
>>> df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1239 entries, 0 to 1238
Data columns (total 58 columns):
AP_MVM        1239 non-null object
AP_CONDIC     1239 non-null object
AP_GESTAO     1239 non-null object
AP_CODUNI     1239 non-null object
AP_AUTORIZ    1239 non-null object
AP_CMP        1239 non-null object
AP_PRIPAL     1239 non-null object
AP_VL_AP      1239 non-null float64
...

Downloading and reading SINASC data:

In[1]: from pysus.online_data.sinasc import download
In[2]: df = download('SE', 2015)
In[3]: df.head()
Out[3]: 
   NUMERODN        CODINST ORIGEM    ...     TPROBSON PARIDADE KOTELCHUCK
0  19533794  MSE2805100001      1    ...           11        1          9
1  52927108  MSE2802700001      1    ...           11        1          9
2  54673238  MSE2804400001      1    ...           11        1          5
3  54673239  MSE2804400001      1    ...           10        1          3
4  54695292  MBA2916500001      1    ...           03        1          2
[5 rows x 64 columns]

Dowloading and reading SIM data:

In[1]: from pysus.online_data.SIM import download
In[2]: df = download('ba', 2007)
In[3]: df.head()
Out[3]: 
   NUMERODO TIPOBITO   DTOBITO  ...   UFINFORM        CODINST CB_PRE
0  01499664        2  30072007  ...         29  RBA2914800001   C229
1  09798190        2  04072007  ...         29  RBA2914800001    R98
2  01499665        2  25082007  ...         29  RBA2914800001    I10
3  10595623        2  11092007  ...         29  RBA2914800001   G839
4  10599666        2  09082007  ...         29  EBA2927400001   I499
[5 rows x 56 columns]

Dowloading and reading CIHA data:

In[1]: from pysus.online_data.CIHA import download
In[2]: df = download('mg', 2009, 7)
In[3]: df.head()
Out[3]: 
  ANO_CMPT MES_CMPT ESPEC        CGC_HOSP  ...  CAR_INT HOMONIMO     CNES FONTE
0     2009       07        16505851000126  ...                    2126796     1
1     2009       07        16505851000126  ...                    2126796     2
2     2009       07        16505851000126  ...                    2126796     6
3     2009       07        16505851000126  ...                    2126796     6
4     2009       07        16505851000126  ...                    2126796     1
[5 rows x 27 columns]