SAS Viya : The Python Perspective

Learn how to access analytics from SAS Cloud Analytic Services (CAS) using Python and the SAS ® Viya TM platform. SAS ® Viya TM : 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 TM : 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. 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 Specifying Action Parameters CAS Action Results Working with CAS Action Sets Details Getting Help Dealing with Errors SWAT Options CAS Session Options Conclusion Chapter 4: Managing Your Data in CAS Overview Getting Started with Caslibs and CAS Tables Loading Data into a CAS Table Displaying Data in a CAS Table Computing Simple Statistics Dropping a CAS Table CAS Data Types Caslib and CAS Table Visibility The Active Caslib 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 The HTML Data Message Handler The Excel Data Message Handler The PandasDataFrame Data Message Handler Using Data Message Handlers with Databases Writing Your Own Data Message Handlers Variable Definition Details Adding Data Transformers Managing Caslibs Creating a Caslib Setting an Active Caslib Dropping a Caslib Conclusion Chapter 5: The CASAction and CASTable Objects Getting Started with the CASAction Objects Setting Nested Parameters Setting Parameters as Attributes Retrieving and Removing Action Parameters First Steps with the CASTable Object Manually Creating a CASTable Object CASTable Action Interface Setting CASTable Parameters Managing Parameters Using the Method Interface Managing Parameters Using the Attribute Interface Materializing CASTable Parameters Conclusion Chapter 6: Working with CAS Tables Using CASTable Objects like a DataFrame CAS Table Introspection Computing Simple Statistics Creating Plots from CASTable Data Exporting CASTables to Other Formats Sorting, Data Selection, and Iteration Fetching Data with a Sort Order Iterating through Columns and Rows Techniques for Indexing and Selecting Data Data Wrangling on the Fly Creating Computed Columns BY-Group Processing Conclusion Chapter 7: Data Exploration and Summary Statistics Overview Summarizing Continuous Variables Descriptive Statistics Histograms Percentiles Correlations Summarizing Categorical Variables Distinct Counts Frequency Top K Cross Tabulations Variable Transformation and Dimension Reduction Variable Binning Variable Imputation Conclusion Chapter 8: Modeling Continuous Variables Linear Regressions Extensions of Ordinary Linear Regression Generalized Linear Models Regression Trees 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 The Binary Interface The REST Interface The Pros and Cons of Each Interface Result Processing Workflows The Easy Way Using Response and Result Callback Functions Handling Responses from Multiple Sessions Simultaneously Connecting to Existing Sessions Communicating Securely Conclusion Appendix A: A Crash Course in Python IPython and Jupyter Data Types and Collections Numeric Data Types Character Data Types Booleans Lists and Tuples Other Types Flow Control Conditional Code Looping Functions Classes and Objects Exceptions Context Managers Using the Pandas Package Data Structures Data Selection Creating Plots and Charts Plotting from Pandas DataFrame Methods Plotting DataFrames with Plotly and Cufflinks Creating Graphics with Matplotlib Interactive Visualization with Bokeh Conclusion Appendix B: Troubleshooting Software Version Issues Connection Issues Missing Linux Library Dependencies Incorrect SAS Threaded Kernel Configuration Unable to Import _pyXXswat Refused Connection Authentication Problems Index

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.

Taking you on a journey to learn and apply Python programming in the context of the SAS Viya platform, this book includes examples from creating connections to CAS all the way to simple statistics and machine learning. -- Edited summary from book Taking you on a journey to learn and apply Python programming in the context of the SAS Viya platform, this book includes examples from creating connections to CAS all the way to simple statistics and machine learning. -- Résumé du livre