{"categories":[{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/6"}],"enrichment":{"capability":"A Rust-based analytical query engine for DataFrames that executes lazy and eager queries with multi-threaded, vectorized performance and can process datasets larger than available RAM through streaming.","skillfed_tags":["dataframe-engine","query-optimization","out-of-core-processing"],"use_cases":["Process multi-gigabyte CSV or Parquet files on a machine with limited RAM using the streaming engine.","Build analytical pipelines that filter, group, and aggregate time-series or transactional data with lazy query optimization.","Compose complex multi-step queries (joins, window functions, aggregations) that benefit from automatic query optimization.","Interoperate with Arrow-based tools and libraries by leveraging zero-copy columnar data sharing.","Execute vectorized data transformations where compiled Rust execution delivers measurable performance gains."],"what_it_does":"This package is a DataFrame query engine written in Rust that prioritizes speed and memory efficiency. It supports both lazy (optimized) and eager execution modes, allowing you to compose complex analytical queries using a chainable expression API. The lazy execution model optimizes your query plan before running it, and the streaming engine can process datasets that exceed available RAM by processing data in chunks.\n\nYou install it via pip and import it as `polars`. It has no runtime dependencies beyond the compiled Rust library bundled in the wheel. It's designed for analytical workloads\u2014filtering, grouping, aggregating, and sorting large datasets\u2014and integrates with Apache Arrow for zero-copy data sharing. The package is actively maintained, widely used (top 15000 PyPI packages by downloads), and carries no known security vulnerabilities.","worth_installing":"Yes. This package is production-stable, actively maintained, permissively licensed, and carries no known vulnerabilities. Install it if you need fast analytical queries on large datasets or want performance-critical data transformation work. The medium install friction (compiled wheels, Python 3.10+ requirement) is standard for Rust-based Python packages and not a barrier for most modern environments."},"id":"polars-runtime-64","links":{"html":"https://skillfed.io/packages/polars-runtime-64","md":"https://skillfed.io/packages/polars-runtime-64.md","pypi":"https://pypi.org/project/polars-runtime-64/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-01","license_spdx":null,"license_treatment":"permissive","name":"polars-runtime-64","python_support":"supports_current","summary":"Blazingly fast DataFrame library"},"popularity":{"monthly_downloads":161231,"position":10639,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"1.43.2"}