Polars
Blazing-fast DataFrame library written in Rust — handles datasets in the 10GB–100GB range that Pandas struggles with
Visit Polars →Polars is written in Rust with a query-optimizing execution engine underneath, and it shows — operations that take minutes in Pandas on a large dataset often run in seconds, with a fraction of the memory overhead, specifically because it can parallelize and lazily optimize a chain of operations before running any of it.
The API is deliberately similar to Pandas’ but not identical — worth reaching for once a Pandas workflow is genuinely too slow or memory-hungry (multi-gigabyte datasets), not as a default replacement for smaller, everyday data work where Pandas’ larger ecosystem and familiarity still win.