Polars Bio

A Polars-native library for genomic interval operations that's 6-38x faster than bioframe on real-world benchmarks. Handles the usual interval arithmetic (overlap, nearest, merge, coverage, complement, subtract) plus reads and writes BED, VCF, BAM, CRAM, GFF, FASTA, and FASTQ files. Built on Apache Arrow and DataFusion, so you get streaming support for datasets that don't fit in memory and native cloud I/O for S3, GCS, and Azure. Ships with both functional and method-chaining APIs, plus a SQL interface if that's your thing. Worth trying if you're doing genomic analysis in Python and already use Polars, or if you're hitting performance walls with existing tools.

skills.sh installs: 2K
GitHub stars: 56.6K
last updated: Aug 21, 2026

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Install

$ npx -y skills add k-dense-ai/scientific-agent-skills --skill polars-bio --agent claude-code

Installs into .claude/skills of the current project.

Files

polars-bio

Use this skill for genomic interval arithmetic and bioinformatics file I/O through Polars and DataFusion. It targets polars-bio 0.36.0, tested with Polars 1.44.2 on Python 3.13. The upstream package requires Polars >=1.37.1, PyArrow >=23.0.1,<25, DataFusion >=53,<54 and polars-config-meta >=0.3.2,<1. Keep this environment separate from packages needing incompatible Arrow or DataFusion releases.

uv pip install "polars-bio==0.36.0" "polars==1.44.2"
# Optional pandas interoperability (requires pandas >=3):
uv pip install "polars-bio[pandas]==0.36.0" "polars==1.44.2"

Verify new releases against the official release notes and package requirements. The examples below with named files are templates: substitute actual files and check their schemas. The synthetic interval example and small local format round trips were executed during this review.