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
- Decode encoded patient age to any time unit (years, months, etc)
- Convert
.dbcfiles 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
- Download SINAN data (only case investigation tables)
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
If you use windows, or for some other reason is not able to install PySUS on you OS, you can run it from a docker container.
First, clone the Pysus repository:
git clone https://github.com/fccoelho/PySUS.gitthen from within the PySUS directory build the container
cd PySUS
docker build -t pysus .You only have to do this once. On the first time it will take a few minutes. Then you can launch jupyter from the container a just use PySUS:
docker run -p 8888:8888 pysus:latestPoint your browser to http://127.0.0.1:8888 and have fun.
Once you are done, you can stop the container with a simple ctrl-c from the terminal you started it or use the following command:
# to find the container ID
docker ps
docker stop <CONTAINER ID>If you don't want you work to disappear when you stop the container, you must mount your working directory on the container. In the example below, I am mounting the /home/fccoelho/Downloads/pysus on the /home/jovyan/work directory inside the container. This means that everything that is saved inside the work directory will actually be saved in the /home/fccoelho/Downloads/pysus. Modify according to your needs.
docker run -e NB_USER=fccoelho -e NB_UID=1000 -v /home/fccoelho/Downloads/pysus:/home/jovyan/work -p 8888:8888 pysus:latestFor more options about interacting with your container check jupyter-docker-stacks documentation.
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]Dowloading and reading SIA data:
In[1]: from pysus.online_data.SIA import download
In[2]: bi, ps = download('AC', 2020, 3, group=["BI", "PS"])
In[3]: bi.head()
Out[3]:
CODUNI GESTAO CONDIC UFMUN TPUPS ... VL_APROV UFDIF MNDIF ETNIA NAT_JUR
0 2000733 120000 EP 120040 07 ... 24.2 0 0 1023
1 2001063 120000 EP 120040 36 ... 7.3 0 0 1023
2 2001063 120000 EP 120040 36 ... 7.3 0 0 1023
3 2001586 120000 EP 120040 05 ... 38.1 0 0 1147
4 2000083 120000 EP 120033 05 ... 64.8 0 0 1023
[5 rows x 36 columns]
In[4]: ps.head()
Out[4]:
CNES_EXEC GESTAO CONDIC UFMUN ... PERMANEN QTDATE QTDPCN NAT_JUR
0 2002094 120000 EP 120040 ... 30 1 1 1023
1 2002094 120000 EP 120040 ... 0 0 1023
2 2002094 120000 EP 120040 ... 0 0 1023
3 2002094 120000 EP 120040 ... 0 0 1023
4 2002094 120000 EP 120040 ... 0 0 1023
[5 rows x 45 columns]