Polars Beats Pandas by 5-10x on Million-Row Python Data Tasks

1 articles · Updated · KDnuggets · May 12

Three benchmarked data problems showed Polars consistently outpacing Pandas, with the biggest gains reaching 5-10x on tables containing millions of rows.
Rust-based Polars gains speed from lazy query planning, parallel execution across CPU cores and single-pass operations, while Pandas executes steps eagerly and often creates multiple intermediate copies.
In the email-ranking example, Polars replaced Pandas' costly rank(method='first') flow with sort plus with_row_count(); in the returning-users task, it avoided five separate Pandas objects and a memory-heavy pivot.
The sales example highlighted predicate pushdown before joins and faster cumulative calculations, reinforcing the article's broader point that Polars' advantages become material as datasets scale beyond memory-friendly sizes.

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