![]() ![]() Run your Python code from the Python Code Editor.Īs SAS Studio works with SAS Compute Server, invoke the Python procedure on the SAS Compute Server, which starts a Python subprocess.The following diagram summarizes what happens when you run a program in the Python Code Editor.įigure 2 - Running Python code in SAS Studio - what happens? What happens when you run your Python program in the SAS coding interface? Let's take an architectural deep dive to explain. How is the Python code handled at runtime? Using these callback methods, you can create a workflow that mixes SAS and Python programming as needed. ![]() The documentation for the Python procedure provides examples for each of these. This module provides callback methods enabling variable sharing between Python and SAS (SAS.symget or SAS.symput), move data between SAS data sets and Pandas DataFrames (SAS.sd2df or SAS.df2sd), invoke SAS and FCMP functions (SAS.sasfnc), and submit SAS code within your Python statements (SAS.submit). The next section explains this in more detail. The PYTHON procedure creates a Python subprocess from a SAS Compute Server process and automatically imports the SAS code enabling interaction between SAS and your Python instance. When the Python code yields results stored in a SAS table, the table displays in the Output Data tab, where you can interactively browse its content. Considering Figure 1, the Python editor appears similar to the SAS Code Editor however, notice the Python.py filename, this is Python code! The Code tab is for editing your Python Code and the Log tab displays the executed statements and information from the Python console. This procedure enables running Python statements within SAS code. When you run your Python code from the Python Code Editor, SAS Studio runs SAS code invoking the PYTHON Procedure and embeds the Python code in a submit/endsubmit block. By the way, the SAS table could have been a SAS data set or a DBMS table that is supported via SAS/Access software, which includes support for ODBC and JDBC.įigure 1 - Python Code Editor in SAS Studio In the example outlined below, we use data in a SAS table called sashelp.class and apply data transformation using Python. The Python Code Editor allows you to write, run, and save Python programs. Details about these configuration options are described in detail in the section Configure SAS to Run External Languages in the SAS Viya Administration documentation. Suffice it to mention here, Python 3.x is supported and you can use any Python package, as long as it is available inside the directory structure that contains Python.exe. Your Kubernetes and/or SAS Administrator can enable Python support in lockdown mode and configure the default location for the SAS environment. How to use SAS lineage diagrams to present Python table usageĪ standard SAS Viya installation does not have Python support enabled, as the environment runs in a lockdown mode, for security purposes. What happens from a high-level architecture perspective how to create a single program or a flow integrating python code in a SAS environment.This post demonstrates the use of this capability with some simple Python code. Leverage the benefits of using SAS Viya with open source for achieving business value Support Information Governance initiatives by displaying tables created by Python, using SAS linage diagrams use Python inside a SAS environment for query, preparation and analysis depending on users' skills, comfort and preferences, as well as the problem they are trying to solveĮfficiently create a flow to integrate SAS and Python code for consistent delivery of analytics-ready data pipelines.Data Scientists and Python programmers can now code, execute and schedule Python scripts from within the SAS code editor interface (SAS Studio) or add Python steps to a SAS Flow quickly and intuitively.īoth options offer Data Scientists the flexibility to: With the October 2021 release of SAS Viya, we introduced the Python Code Editor. The goal is to enable all types of users to leverage their best skills, ensuring governance of assets, explainable AI and operationalization of models ( ModelOps). Let's just say each team is unique.Īs part of our Continuous Integration/Continuous Delivery with monthly releases, we are always looking to extend SAS Viya integration capabilities to support open-source users and technology. Some of them use SAS, others may have analytical assets already built in Python or R. Data science teams are multidisciplinary, each with different skills and technologies of choice. ![]()
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