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.
Just when you think you’ve seen it all, life can surprise you in a big way, making you wonder what else you've missed. That is what happened when I recently had a chance to work with the SAS® Scalable Performance Data Server, a product that's been around a while, but
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
