Overview

What is this?

This module provides Python APIs to the SAS system. You can start a SAS session and run analytics from Python through a combination of object-oriented methods and Python magics.

The APIs provide interfaces for the following:

  • Start a SAS session on the same host as Python or a remote host.
  • Exchange data between SAS data sets and Pandas data frames.
  • Use familiar methods such as describe() and head() to work with data.

Additional functionality such as machine learning, econometrics, and quality control are organized in Python classes.

See Getting started for programming examples.

Dependencies

  • Python3.4 or higher.
  • SAS 9.4 or higher. SAS Viya 3.1 or higher is also supported.
  • To use the integrated object method (IOM) access method (one of four connection methods) requires Java 7 or higher on the client and four (7 w/ encryption enabled) JAR files from your SAS installation.

You can connect to SAS on any platform that is supported for the specified SAS releases.

Jupyter magics

Typically, programming with this module is performed with the Python functions that are provided by the package. For example, to view the first few rows of a data set, you can use the head() method. However, if you are an experienced SAS programmer, there might be occasions in which you prefer to run SAS statements–such as running PROC PRINT to view the first few rows of a data set.

The magics that are available with the package enable you to bypass Python and submit programming statements to your SAS session.

The %%SAS magic enables you to submit the contents of a cell to your SAS session. The cell magic executes the contents of the cell and returns any results.

%%SAS
proc print data=sashelp.class;
run;

data work.a;
  set sashelp.cars;
run;

If you are also invoking saspy methods directly in other cells within the same notebook, you may indicate that a %%SAS cell should share the same SAS session by passing your existing session as a parameter.

In a prior cell in the notebook:

import saspy
my_session = saspy.SASsession()

# sending a dataset to SAS as 'mydata'
mydata = sas.df2sd(some_pandas_dataframe, 'mydata')

Then later, invoke a SAS cell and pass your existing session so that the remote data set is accessible:

%%SAS my_session
proc print data=work.mydata;
run;

The %%IML magic enables you to submit the contents of a cell to your SAS session for processing with PROC IML. The cell magic executes the contents of the cell and returns any results. The PROC IML statement and the trailing QUIT; statement are submitted automatically.

%%IML
a = I(6); * 6x6 identity matrix;
b = j(5,5,0); *5x5 matrix of 0's;
c = j(6,1); *6x1 column vector of 1's;
d=diag({1 2 4});
e=diag({1 2, 3 4});

The %%OPTMODEL magic enables you to submit the contents of a cell to your SAS session for processing with PROC OPTMODEL. The cell magic executes the contents of the cell and returns any results. The PROC OPTMODEL statement and the trailing QUIT; statement are submitted automatically.

%%OPTMODEL
/* declare variables */
var choco >= 0, toffee >= 0;

/* maximize objective function (profit) */
maximize profit = 0.25*choco + 0.75*toffee;

/* subject to constraints */
con process1:    15*choco +40*toffee <= 27000;
con process2:           56.25*toffee <= 27000;
con process3: 18.75*choco            <= 27000;
con process4:    12*choco +50*toffee <= 27000;
/* solve LP using primal simplex solver */
solve with lp / solver = primal_spx;
/* display solution */
print choco toffee;