SAS Viya: The Python Perspective

438 Pages·2017·4.782 MB·English

by  Kevin D. Smith, Xiangxiang Meng PhD

SAS® Viya™
The Python Perspective
Kevin D.
Smith Xiangxiang Meng
The correct bibliographic citation for this manual is as follows: Smith, Kevin D.,
Xiangxiang Meng. 2017.
® ™
SAS Viya : The Python Perspective. Cary, NC: SAS Institute Inc.
® ™
SAS Viya : The Python Perspective
Copyright © 2017, SAS Institute Inc., Cary, NC, USA
ISBN 978-1-62960-276-9 (Hard copy)
ISBN 978-1-62960-883-9 (EPUB)
ISBN 978-1-62960-884-6 (MOBI)
ISBN 978-1-62960-885-3 (PDF)
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February 2017
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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
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
Foreword
SAS® Viya™ marks a new and important chapter in our ever-evolving SAS
software. A unified, open, powerful, and cloud-ready platform built on
excellence in data management, advanced analytics, and high-performance
computing.
These pillars of SAS Viya are important individually, but it is through their
combination that the platform comes to life. The ability to access a central
data management and computing environment through public APIs and
from multiple programming languages, with consistent security and data
models is a core competency of the modern analytic platform.
The Python language has quickly grown into one of the important
programming languages for data science and analytics. As the SAS R&D
team embarked on building SAS Cloud Analytic Services (CAS), the engine
of the SAS Viya platform, it was obvious that that access from Python
would be important.
The Python client for SAS Viya was developed by Kevin Smith as a
member of the core team that designed and developed SAS Cloud Analytic
Services. This book by Kevin and Xiangxiang Meng takes you on a journey
to learn and apply Python programming in the context of the SAS Viya
platform. Their deep understanding of the SAS Viya server architecture,
the client architecture, and the Python language implementation shines
through in every chapter.
As a lifelong learner, I greatly enjoyed the journey and am sure that you
will, too.