Polars is a fast, efficient, and memory-safe DataFrame library designed for data manipulation and analytics. Built in Rust and with bindings for Python, it offers high-performance operations on structured data. It supports both eager and lazy execution modes, making it suitable for batch processing and large-scale analytical workflows.
Polars is comparable to pandas but much faster because it’s written in Rust and optimized for parallel processing. It uses less memory and can handle much larger datasets without slowing down. With features like lazy evaluation, it processes data only when needed, making complex data transformations more efficient.
Polars provides high performance due to its Rust-based backend, supports multi-threading for parallel operations, and has a low memory footprint. It also includes a versatile API, lazy evaluation for optimization, and excellent support for both structured and semi-structured data.