Welcome to the continuation of my series Getting Started with Python Integration to SAS Viya. In this post I'll show how to impute missing values in a distributed CAS table using the fillna method from the Pandas API in the SWAT package and the impute CAS action. Load and prepare data
Welcome to the continuation of my series Getting Started with Python Integration to SAS Viya. In this post I'll discuss how to count missing values in a CAS table using the Python SWAT package. Load and prepare data First, I connect my Python client to the distributed CAS server and named
SAS SQL handles missing values differently than the ANSI standard for SQL. PROC SQL follows the SAS convention for handling missing values: numerical missing values are always interpreted as less or smaller than all nonmissing values. My first blog showed that missing values can be troublemakers in non-grouped descriptive statistics.
This article discusses how to use SAS to filter variables in a dataset based on the percentage of missing values or duplicate values. The missing value statistics can be implemented by either DATA step programming on your own or reusing the existing powerful PROC FREQ.
Most SAS programmers would agree that they use the SET statement without giving much thought to the syntax, because it’s such a widely used statement of choice. We routinely name the expected data sets and possibly a few options, and away we go. A visit to the documentation can be
Don’t worry! This is not an excerpt from a romantic love letter. The title of this blog post is an allusion to my talk on "Missing Values", at the A2013 conference in June in London. There is not much time for emotions: dealing with missing values in analysis is not
