Polars is an in-memory data frame library implemented in Rust. Unlike other data frames (such as pandas), Polars is multithreaded, supports lazy execution and is safe for parallel operations. The in-memory data is organized in the Apache Arrow format for efficient analytic operations and to enable interoperability with other tools. If you're familiar with pandas, you can quickly get started with Polars' Python bindings. We believe Polars, with Rust implementation and Python bindings, is a performant in-memory data frame for your analytical needs. Our teams continue to have a good experience with Polars which is why we're moving it to Trial.
Polars is an in-memory data frame library implemented in Rust. Unlike other data frames (such as Pandas), Polars is multithreaded and safe for parallel operations. The in-memory data is organized in the Apache Arrow format for efficient analytic operations and to enable interoperability with other tools. If you're familiar with Pandas, you can quickly get started with Polars' Python bindings. We believe Polars, with Rust implementation and Python bindings, is a performant in-memory data frame to assess for your analytical needs.
