Handling NULL (or None) values is a crucial task in data processing, as missing data can skew analysis, produce errors in data transformations, and degrade the performance of machine learning models. In PySpark, dealing with NULL values is a common operation when working with distributed datasets. PySpark provides several methods and techniques to detect, manage, and clean up missing or NULL values in a DataFrame.

In this blog post, we’ll explore how to handle NULL values in PySpark DataFrames, covering essential methods like filtering, filling, dropping, and replacing NULL values.