Next-gen Python DataFrame operations for genomics!

polars-bio is a blazing fast Python DataFrame library for genomics🧬 built on top of Apache DataFusion, Apache Arrow and polars. It is designed to be easy to use, fast and memory efficient with a focus on genomics data.

Key Features

optimized for peformance and memory efficiency for large-scale genomics datasets analyses both when reading input data and performing operations
popular genomics operations with a DataFrame API (both Pandas and polars)
SQL-powered bioinformatic data querying or manipulation/pre-processing
native parallel engine powered by Apache DataFusion and sequila-native
out-of-core/streaming processing (for data too large to fit into a computer's main memory) with Apache DataFusion and polars
support for federated and streamed reading data from cloud storages (e.g. S3, GCS) with Apache OpenDAL enabling processing large-scale genomics data without materializing in memory
zero-copy data exchange with Apache Arrow
bioinformatics file formats with noodles and exon
fast overlap operations with COITrees: Cache Oblivious Interval Trees
pre-built wheel packages for Linux, Windows and MacOS (arm64 and x86_64) available on PyPI

See quick start for the installation options.

Citing

If you use polars-bio in your work, please cite:

@article {Wiewiorka2025.03.21.644629,
    author = {Wiewiorka, Marek and Khamutou, Pavel and Zbysinski, Marek and Gambin, Tomasz},
    title = {polars-bio - fast, scalable and out-of-core operations on large genomic interval datasets},
    elocation-id = {2025.03.21.644629},
    year = {2025},
    doi = {10.1101/2025.03.21.644629},
    publisher = {Cold Spring Harbor Laboratory},
    URL = {https://www.biorxiv.org/content/early/2025/03/25/2025.03.21.644629},
    eprint = {https://www.biorxiv.org/content/early/2025/03/25/2025.03.21.644629.full.pdf},
    journal = {bioRxiv}
}

Next-gen Python DataFrame operations for genomics!

Key Features

Citing

Performance benchmarks

Single-thread 🏃‍

Parallel 🏃‍🏃‍