Polars

Polars is a high-performance DataFrame library built from the ground up in Rust, designed to handle large-scale analytical workloads on a single machine. It uses Apache Arrow as its native memory model and leverages Rust's ownership and concurrency guarantees to provide true multi-threaded query execution without a GIL. Polars supports both eager and lazy evaluation modes — the lazy API applies query optimization before execution, reducing unnecessary computation. Benchmarks consistently show Polars to be 5–15x faster than Pandas for common operations like filtering, grouping, sorting, and joining, while using 2–4x less memory. Released in 2020 and grown to over 37k GitHub stars, Polars has become the go-to Pandas replacement for performance-critical data engineering pipelines. In 2025, the team raised €18M Series A and launched Polars Cloud for distributed workloads.