Using STYLE/MERGE and STYLE/REPLACE in PROC REPORT
In this edition of Problem Solvers, SAS Technical Support's Kathryn McLawhorn walks you through two PROC REPORT techniques.
Tag: Problem Solvers
How to debug 5 common SAS® software font issues
Martin Mincey of SAS Technical Support shows you how to debug when listing registered SAS fonts, registering new fonts, getting SAS SG procedures to use a new font and more.
Building Machine Learning Models by Integrating Python and SAS® Viya®
SAS' Kris Stobbe shows how you can predict survival rates of Titanic passengers with a combination of both Python and CAS using SWAT, then see how the models performed.
Why does my SAS date have the wrong century?
2020 is here and systems that use 2-digit years in date values are getting confused. Do we mean 1920 or 2020? In SAS, the YEARCUTOFF= option lets you decide.
Machine Learning with SASPy: Exploring and Preparing your data - Part 3
Parts 1 and 2 of this blog post discussed exploring and preparing your data using SASPy. To recap, Part 1 discussed how to explore data using the SASPy interface with Python. Part 2 continued with an explanation of how to prepare your data to use it with a machine-learning model.
Old reliable: DATA step tips and tricks
The DATA step remains a popular way to create and manipulate SAS data sets. Whether you are reshaping a data set entirely or simply assigning values to a new variable, there are numerous tips and tricks that you can use to save time and keystrokes.
How to make the most of arrays with SAS® software and streamline your programming
Grace Whiteis of SAS Technical Support shows you how to capitalize on arrays using SAS software -- and streamline your code, whether it involves arrays or not.
Creating a word cloud using Python and SAS® software
Generating a word cloud (also known as a tag cloud) is a good way to mine internet text. Word (or tag) clouds visually represent the occurrence of keywords found in internet data such as Twitter feeds.
Machine learning with SASPy: Exploring and preparing your data (part 2)
This article continues a series that began with Machine learning with SASPy: Exploring and preparing your data (part 1). Part 1 showed you how to explore data using SASPy with Python. Here, in part 2, you will learn how to begin to prepare your data to use it within a
Machine learning with SASPy: Exploring and preparing your data (part 1)
SASPy is a powerful Python library that interfaces with SAS and can help with your machine-learning solutions. SASPy was created for Python programmers to leverage the power of SAS within their Python scripts. If you are not familiar with SASPy, see the following resources: Introducing SASPy: Use Python code to