PySpark to Pandas/scikit-learn: A Practical...

If you've spent years writing PySpark pipelines, the first time you open a Jupyter notebook full of pd.DataFrame and sklearn.fit() calls, it can feel like you've switched languages entirely. You haven't. The concepts are the same: transformations, aggregations, pipelines, model evaluation. But the execution model, API design, and idioms are different enough to cause real friction when you're trying to learn ML fast. This guide is not a beginner tutorial. It's a translation layer, a direct mappin