In this article, part of the “Moving from Python to esProc” series, we’ll look at esProc SPL’s data manipulation capabilities compared to Python.
As you may already know, real-world data rarely arrives in a clean, analysis-ready format. You’ll frequently need to clean messy data, reshape it to suit your analysis needs, merge multiple datasets, and perform complex filtering and calculations before extracting meaningful insights. Luckily, esProc SPL handles these common data manipulation tasks with remarkable elegance and efficiency.
How to clean ‘messy’ data with esProc SPL
Datasets are rarely perfect. Missing values, inconsistent formats, and outliers can significantly impact analysis