polars

Blazingly fast DataFrame library

polars Permissive license Copyright (c) 2025 Ritchie Vink Copyright (c) 2024 (Some portions) NVIDIA CORPORATION & AFFILIATES. All rights reserved. Permission is hereby granted, free of charge, to any person obtaining a copy… (full text in the JSON record) Active ★ 39,348 v1.43.2 released 2026-08-01

Install

polars on PyPI

pip

pip install polars

uv

uv add polars

poetry

poetry add polars

Package facts

License	Copyright (c) 2025 Ritchie Vink Copyright (c) 2024 (Some portions) NVIDIA CORPORATION & AFFILIATES. All rights reserved. Permission is hereby granted, free of charge, to any person obtaining a copy… (full text in the JSON record) (permissive)
Python support	supports the current Python release (>=3.10)
Install friction	low — pure-Python wheel
Runtime dependencies	1 — polars-runtime-32
Maintenance	actively maintained — 12 days since the last release
Last repo commit	2026-08-13
First released	2021-03-15
Popularity	one of the top 1,000 most-downloaded packages on PyPI (30-day window, as of 2026-08-13)
Known vulnerabilities	none known (OSV.dev, checked 2026-08-13)

Evidence: polars-1.43.2-py3-none-any.whl

Keywords: dataframe, arrow, out-of-core

Development Status :: 5 - Production/StableEnvironment :: ConsoleIntended Audience :: Science/ResearchLicense :: OSI Approved :: MIT LicenseOperating System :: OS IndependentProgramming Language :: PythonProgramming Language :: Python :: 3Programming Language :: Python :: 3 :: OnlyProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: RustTopic :: Scientific/EngineeringTyping :: Typed

About polars

from the package's own PyPI description — quoted content, verbatim

<p align="center"> <b>Documentation</b>: <a...

Read as markdown · JSON record · Source repository · Homepage · Docs

AI interpretation — verify before relying

AI-generated interpretation of the package facts above; every digit, version, license, or vulnerability id it cites is grounded in the facts already shown on this page

Polars is a Rust-based analytical query engine for DataFrames that executes complex data transformations with lazy or eager evaluation, multi-threaded vectorized processing, and support for datasets larger than available RAM.

Installation is straightforward with low friction—a pure wheel distribution (polars-1.43.2-py3-none-any.whl) with a single runtime dependency. The project is actively maintained with a release 12 days ago, indicating strong ongoing development.

Polars is licensed under the MIT License (permissive), allowing free use, modification, and distribution with minimal restrictions—suitable for both commercial and open-source projects.

Usage

pip install polars

import polars as pl

df = (
    pl.scan_parquet("orders.parquet")
    .filter(pl.col("status") == "shipped")
    .group_by("customer_id")
    .agg(pl.col("amount").sum().alias("total"))
    .collect()
)

Requires Python 3.10 or later; for datasets exceeding 2^32 rows or older CPUs lacking AVX2 support, consult the installation guide for special build options.

Verdict: Polars is a production-ready, actively maintained dataframe library with zero known vulnerabilities, low installation friction, and permissive licensing. Its Rust foundation and lazy query optimization make it well-suited for large-scale analytical workloads, though users should verify compatibility with their specific CPU architecture or memory constraints.

Needs verification

Whether polars-runtime-32 is a required system library or a Python package dependency that may have its own constraints.
Specific performance gains or memory reductions compared to alternatives in your use case.
GPU acceleration availability and setup requirements for NVIDIA hardware.

fast dataframe library pythonlazy query execution dataframeout of core data processingvectorized dataframe operationsarrow columnar format dataframerust dataframe enginestreaming large datasetsparallel dataframe queries