Book description
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
- Grasp 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
- Foreword
- About This Book
- About These Authors
- Chapter 1: Installing Python, SAS SWAT, and CAS
- Installing Python
- Installing SAS SWAT
- Installing CAS
- Making Your First Connection
- Conclusion
- Chapter 2: The Ten-Minute Guide to Using CAS from Python
- Importing SWAT and Getting Connected
- Running CAS Actions
- Loading Data
- Executing Actions on CAS Tables
- Data Visualization
- Closing the Connection
- Conclusion
- Chapter 3: The Fundamentals of Using Python with CAS
- Connecting to CAS
- Running CAS Actions
- Details
- Conclusion
- Chapter 4: Managing Your Data in CAS
- Overview
- Getting Started with Caslibs and CAS Tables
- Uploading Data Files to CAS Tables
- Uploading Data from URLs to CAS Tables
- Uploading Data from a Pandas DataFrame to a CAS Table
- Using Data Message Handlers
- Writing Your Own Data Message Handlers
- Managing Caslibs
- Conclusion
- Chapter 5: The CASAction and CASTable Objects
- Getting Started with the CASAction Objects
- First Steps with the CASTable Object
- Conclusion
- Chapter 6: Working with CAS Tables
- Using CASTable Objects like a DataFrame
- Sorting, Data Selection, and Iteration
- Data Wrangling on the Fly
- Conclusion
- Chapter 7: Data Exploration and Summary Statistics
- Overview
- Summarizing Continuous Variables
- Summarizing Categorical Variables
- Variable Transformation and Dimension Reduction
- Conclusion
- Chapter 8: Modeling Continuous Variables
- Linear Regressions
- Extensions of Ordinary Linear Regression
- Conclusion
- Chapter 9: Modeling Categorical Variables
- Logistic Regression
- Decision Trees
- Gradient Boosting, Forests, and Neural Networks
- Conclusion
- Chapter 10: Advanced Topics
- Binary vs. REST Interfaces
- Result Processing Workflows
- Connecting to Existing Sessions
- Communicating Securely
- Conclusion
- Appendix A: A Crash Course in Python
- IPython and Jupyter
- Data Types and Collections
- Flow Control
- Functions
- Classes and Objects
- Exceptions
- Context Managers
- Using the Pandas Package
- Creating Plots and Charts
- Conclusion
- Appendix B: Troubleshooting
- Software Version Issues
- Connection Issues
- Index
Product information
- Title: SAS Viya
- Author(s):
- Release date: February 2018
- Publisher(s): SAS Institute
- ISBN: 9781629608839
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