polars
Blazingly fast DataFrame library
Decision gist · record as of 2026-08-14
Yes, if you work with analytical DataFrames and performance or memory efficiency matters. Polars is production-stable, actively maintained, permissively licensed, and has no known vulnerabilities. Install friction is low. The main consideration is whether your use case benefits from its Rust-backed speed and streaming capabilities—for small datasets or simple operations, the overhead may not justify switching from pandas.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later.
- Installation is straightforward via pip with low friction.
- The package is actively maintained with a recent release (13 days old), strong community engagement (39349 stars), and production-stable status.
License · maintenance · safety
permissive license (permissive) — MIT license permits unrestricted commercial and private use, modification, and distribution with minimal obligations—only requiring license and copyright notice preservation.
last release 2026-08-01 (13 days) · last repo commit 2026-08-13 · 39,349 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 70,930,564 downloads/mo, #466 on PyPI
Alternatives
Verify before relying
pip install polars
import polars as pl
df = (
pl.scan_parquet("data.parquet")
.filter(pl.col("status") == "shipped")
.group_by("customer_id")
.agg(pl.col("amount").sum().alias("total"))
.collect()
)- Whether the streaming engine's memory efficiency gains are material for your specific dataset size and available RAM.
- GPU acceleration availability and performance benefit on your hardware (NVIDIA GPU support mentioned but not detailed).
- Whether lazy query optimization overhead is worth the benefit for your typical query patterns.
What it is and what it does
Polars is a DataFrame query engine implemented in Rust that brings analytical database performance to Python. It processes data using multi-threaded, vectorized (SIMD) execution and supports both lazy evaluation (with automatic query optimization) and eager execution modes. The engine can handle datasets larger than available RAM through streaming, making it suitable for processing gigabyte-scale data on resource-constrained machines.
The package provides an expressive API for composing complex analytical queries through expressions, with zero-copy interoperability via Apache Arrow columnar format. It supports multiple language bindings (Python, Rust, Node.js, R) and optional GPU acceleration on NVIDIA hardware. Polars is designed as a modern alternative to pandas for analytical workloads where performance and memory efficiency matter.
Use it for
- Processing multi-gigabyte datasets that exceed available RAM using the streaming engine.
- Running complex analytical queries with automatic optimization on large parquet or CSV files.
- Building data pipelines where query performance is a bottleneck compared to pandas.
- Interoperating with Arrow-native data formats and systems without serialization overhead.
- Scaling analytical workloads across multiple CPU cores with minimal configuration.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you work with analytical DataFrames and performance or memory efficiency matters.
Polars is production-stable, actively maintained, permissively licensed, and has no known vulnerabilities. Install friction is low. The main consideration is whether your use case benefits from its Rust-backed speed and streaming capabilities—for small datasets or simple operations, the overhead may not justify switching from pandas.
Install
polars on PyPI
Before you install
Installation is straightforward via pip with low friction. The package is actively maintained with a recent release (13 days old), strong community engagement (39349 stars), and production-stable status.
Requires Python 3.10 or later.
License in practice
MIT license permits unrestricted commercial and private use, modification, and distribution with minimal obligations—only requiring license and copyright notice preservation.
Quickstart
pip install polars
import polars as pl
df = (
pl.scan_parquet("data.parquet")
.filter(pl.col("status") == "shipped")
.group_by("customer_id")
.agg(pl.col("amount").sum().alias("total"))
.collect()
)Verify before relying
- Whether the streaming engine's memory efficiency gains are material for your specific dataset size and available RAM.
- GPU acceleration availability and performance benefit on your hardware (NVIDIA GPU support mentioned but not detailed).
- Whether lazy query optimization overhead is worth the benefit for your typical query patterns.
Package facts
| License | permissive license permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 1 packagepolars-runtime-32 |
| Maintenance | Actively maintained 13 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 70,930,564 / month, #466 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | 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 |
Evidence: polars-1.43.2-py3-none-any.whl
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See also arcticdb · polars-cloud · polars-ds · polars-ols · polars-runtime-32 · polars-runtime-64 · polars-runtime-compat · grizz · polars-lts-cpu · patito