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It is designed to be easy to use, fast and memory efficient with a focus on genomics data.
## Key Features
* optimized for [peformance](#performance) and large-scale genomics datasets
* optimized for [peformance](performance.md#results-summary-) and large-scale genomics datasets
* popular genomics [operations](features.md#genomic-ranges-operations) with a DataFrame API (both [Pandas](https://pandas.pydata.org/) and [polars](https://pola.rs/))
* native parallel engine powered by Apache DataFusion and [sequila-native](github.com/sequila/sequila-native)
* out-of-core processing ([streaming](https://docs.pola.rs/user-guide/concepts/_streaming/)) with [polars](https://pola.rs/)
* zero-copy data exchange with Apache Arrow
* pre-built wheel packages for Linux, Windows and MacOS (arm64 and x86_64) available on [PyPI](https://pypi.org/project/polars-bio/#files)
* native parallel engine powered by Apache DataFusion and [sequila-native](https://github.com/biodatageeks/sequila-native)
* out-of-core processing ([streaming](https://docs.pola.rs/user-guide/concepts/_streaming/)) with [Apache DataFusion](https://datafusion.apache.org/) and [polars](https://pola.rs/)
* zero-copy data exchange with [Apache Arrow](https://arrow.apache.org/)
* pre-built wheel packages for *Linux*, *Windows* and *MacOS* (*arm64* and *x86_64*) available on [PyPI](https://pypi.org/project/polars-bio/#files)
[//]: #(* support for common genomics file formats (VCF, BAM and FASTQ))
