Pandas vs Polars
A head-to-head on NYC taxi data, run enough times to actually trust the numbers, to find where the library choice pays off and where it just doesn't matter.
The setup
I took fifty thousand NYC Yellow Taxi trip records and put pandas and Polars through the same set of typical data-processing tasks. To keep the comparison honest, every operation ran a hundred times, so the reported timings reflect steady behavior rather than a lucky first pass.
15× faster CSV loads — 0.0032s versus 0.0463s.Rust-based multithreaded parsing, with identical analytical output.
What I found
Polars loaded CSVs about fifteen times faster than pandas, thanks to its Rust-based multithreaded parser. Both libraries produced identical analytical results, so the speed came at no cost to correctness. Polars also used roughly ten percent less memory, 8.34 megabytes against 9.25, because of its Arrow-native columnar storage. The gap narrowed on small operations and widened on larger ones.
When it's worth switching
The value of the exercise was not declaring a winner. It was building a clearer sense of when the library choice matters and when it is noise. On a small one-off script, the difference disappears. On a pipeline that touches real volume repeatedly, it compounds into something you can feel.