Python and SAS Viya: 3 Reasons We Fell In Love
06/29/2017 by Ivan Gomez Modernization - Analytics
Fierce preferences for specific tools are prevalent in the data analytics community. Equally prevalent is the idea that using the right tool for the job is the most efficient way to get results. Connect to SAS Viya Platform using the Python SWAT package allows users to seamlessly switch between preferred tools to efficiently achieve results.
To uncover the realities of using these tools together we went to work on a dataset containing all the FBS College Football games from the 2011-2016 seasons. Our goal was to observe statistics across both conferences and teams. To do this we transformed the game-level data to a team-level dataset with one row per team per game.
Upon completion of this exploration, we found that the coupling of Python and SAS Viya had three advantages: Access to Python’s Pandas indexing and data selection capabilities, SAS Visual Analytics streamlined visual data exploration and SAS Cloud Analytic Services’ (CAS) server at the center of our project.
Depending on the transformation needed, a SQL statement or a data step could be the best choice. In the case of our problem, and many others, leveraging the indexing power of Pandas is a major advantage that allows for the creation of transformed datasets and new variables in the most efficient way.
Once the transformations are complete the new dataset can be loaded to SAS CAS server with a single line of code.
SAS Visual Analytics an industry-leading data visualization tool that we can now just pop our newly transformed data into and quickly uncover a variety of insights.
We found the Measure Detail viewer to be particularly helpful with early data exploration.
Moving data between technologies often comes with high switching costs. These costs arise when data must be exported from one tool, imported into another, and exported yet again when work is complete. Connecting Python with SAS CAS server allows us to load our data once and access it from our tool of choice without incurring the time and effort costs of switching from one tool to another.
Using Python SWAT to connect to SAS Viya empowers the use of the correct tool for the job and the flexibility to switch between the two with ease. We look forward to seeing how the coupling of these tools allows us, and others, to get to higher quality results more quickly in the future.
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