This repository is a Ruby gem (polars-df) that provides a high-performance DataFrame library by binding to the Rust-based Polars engine. It serves as a native extension, allowing Ruby developers to perform ETL, data transformation, and analytics tasks with Rust-level speed while maintaining Ruby syntax. The architecture consists of a Rust core (ext/polars) handling computation and memory management, wrapped by Ruby modules (lib/polars) that expose a fluent DSL for data manipulation. It supports both eager (DataFrame/Series) and lazy (LazyFrame/Expr) evaluation modes, with extensive I/O capabilities for formats like CSV, Parquet, and Arrow, as well as cloud storage and database integration. The codebase demonstrates strong engineering practices, utilizing the `magnus` crate for safe Ruby-Rust FFI and implementing complex type conversion logic to bridge the two languages. It includes comprehensive test suites and CI pipelines for cross-platform compilation. However, the tight coupling to the upstream Polars Rust library introduces maintenance overhead, as breaking changes in the Rust core require synchronous updates to the Ruby bindings. Several features, particularly around cloud catalogs (Iceberg, Delta) and advanced selectors, are marked as experimental or contain incomplete implementations (Todo blocks), indicating an active but evolving codebase. For AI agents and engineers, this library is the primary tool for data engineering within the Ruby ecosystem where performance is critical. It is suitable for building data pipelines, analytics services, or backend systems that require efficient data processing without leaving the Ruby environment. The presence of `unstable` markers and `Todo` blocks suggests that while the core functionality is robust, some advanced features should be used with caution in production environments.