SAS Viya: The Python Perspective

Learn how to access analytics from SAS Cloud Analytic Services (CAS) using Python and the SAS® Viya platform.

SAS® Viya: The Python Perspective is an introduction to using the Python client on the SAS Viya platform. SAS Viya is a high-performance, fault-tolerant analytics architecture that can be deployed on both public and private cloud infrastructures. While SAS Viya can be used by various SAS applications, it also enables you to access analytic methods from SAS, Python, Lua, and Java, as well as through a REST interface using HTTP or HTTPS. This book focuses on the perspective of SAS Viya from Python.

SAS Viya is made up of multiple components. The central piece of this ecosystem is SAS Cloud Analytic Services (CAS). CAS is the cloud-based server that all clients communicate with to run analytical methods. The Python client is used to drive the CAS component directly using objects and constructs that are familiar to Python programmers.

Some knowledge of Python would be helpful before using this book; however, there is an appendix that covers the features of Python that are used in the CAS Python client. Knowledge of CAS is not required to use this book. However, you will need to have a CAS server set up and running to execute the examples in this book.

With this book, you will learn how to:

• Install the required components for accessing CAS from Python
• Connect to CAS, load data, and run simple analyses
• Work with CAS using APIs familiar to Python users
• Learn about general CAS workflows and advanced features of the CAS Python client
• SAS® Viya: The Python Perspective covers topics that will be useful to beginners as well as experienced CAS users. It includes examples from creating connections to CAS all the way to simple statistics and machine learning, but it is also useful as a desktop reference.

Table of Contents

Chapter 1: Installing Python, SAS SWAT, and CAS
Chapter 2: The Ten-Minute Guide to Using CAS from Python
Chapter 3: The Fundamentals of Using Python with CAS
Chapter 4: Managing Your Data in CAS
Chapter 5: The CASAction and CASTable Objects
Chapter 6: Working with CAS Tables
Chapter 7: Data Exploration and Summary Statistics
Chapter 8: Modeling Continuous Variables
Chapter 9: Modeling Categorical Variables
Chapter 10: Advanced Topics
Appendix A: A Crash Course in Python
Appendix B: Troubleshooting