I Rewrote a Real Data Workflow in Polars. Pandas Didn’t Stand a Chance.
This title could be clearer and more informative.Try out Clickbait Shieldfor free (5 uses left this month).
A hands-on comparison of Polars vs optimized Pandas on a 1-million-row e-commerce pipeline. Starting from a 61-second naive Pandas implementation, the author first optimized it to 0.31 seconds using vectorization, then rewrote it in Polars. Polars eager mode was slower (0.83s), but switching to lazy evaluation via `pl.scan_csv()` and `.collect()` dropped runtime to 0.20 seconds with zero manual optimization. The post explains three key mental model shifts: lazy vs eager execution, query optimizations like predicate pushdown and projection pruning, and Apache Arrow's columnar memory layout. It concludes with honest guidance on when Pandas still wins (small datasets, exploration, ecosystem integrations).
Table of contents
Isn’t Pandas Enough?The WorkflowThe Pandas VersionInstalling Polars and First ImpressionsThe Eager VersionThe Lazy VersionMental Model Shift #1 — Lazy vs Eager ExecutionMental Model Shift #2 — Query OptimizationMental Model Shift #3 — Columnar MemoryWhere Pandas Still WinsConclusionSort: