Polars Fast DataFrames Engine

🚀 Performance improvements

• Optimize not(bool\_f) to not\_bool\_f (#28474)

✨ Enhancements

• Allow callback sinks on cloud (#28458)

🐞 Bug fixes

• Fix panic on self join of scan\_delta / scan\_iceberg (#28468)
• Fix UB on first/last\_non\_null with empty chunk (#28495)
• Do not CSE non-column height expr on streaming engine (#28480)
• Propagate adjusted sortedness through Gather (#28493)
• Deduplicate the expression equality checks (#28483)
• Address 3VL issue with SQL NOT IN interaction with NULL values and joins (#28484)
• Use total comparison in Expr literal values (#28482)
• Ensure SQL SUM and CORR aggregates return NULL for all-null inputs, add TOTAL (#28475)
• Share null_count_dtype helper between Delta and Iceberg, fixing SchemaError (#28479)
• Remove non-output columns from the equi-join and semi/anti-join operators (#28446)
• Fix dropped slice on multiple unions (#28477)
• Fix in-memory engine incorrect slice on maintain order join (#28478)
• Check join schema by position (#28455)
• Block predicate pushdown past overwritten window keys (#28429)

📖 Documentation

• Update and restructure README (#28490)
• Minor fixes to Spark migration guide (#27730)
• Relocate Polars Cloud \& On-Prem User Guide (#28462)
• Add notes on k8s operator (#28445)

🛠️ Other improvements

• Make hive\_part extraction a function and public (#28507)
• Deduplicate the expression equality checks (#28483)
• Bump ruff and mypy package versions (#28456)
• Fix flaky test (#28454)

Thank you to all our contributors for making this release possible!

@AnirudhRahul, @EndPositive, @Jesse-Bakker, @alexander-beedie, @carnarez, @dancsi, @mdroogh, @mroeschke, @nameexhaustion, @nchammas, @orlp, @r-brink and @ritchie46

<h1 align="center">

<a href="https://pola.rs">

<img src="https://raw.githubusercontent.com/pola-rs/polars-static/master/banner/polars_github_banner.svg" alt="Polars logo">

</a>

</h1>

<div align="center">

<a href="https://crates.io/crates/polars">

<img src="https://img.shields.io/crates/v/polars.svg" alt="crates.io Latest Release"/>

</a>

<a href="https://pypi.org/project/polars/">

<img src="https://img.shields.io/pypi/v/polars.svg" alt="PyPi Latest Release"/>

</a>

<a href="https://www.npmjs.com/package/nodejs-polars">

<img src="https://img.shields.io/npm/v/nodejs-polars.svg" alt="NPM Latest Release"/>

</a>

<a href="https://community.r-multiverse.org/polars">

<img src="https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fcommunity.r-multiverse.org%2Fapi%2Fpackages%2Fpolars&query=%24.Version&label=r-multiverse" alt="R-multiverse Latest Release"/>

</a>

<a href="https://doi.org/10.5281/zenodo.7697217">

<img src="https://zenodo.org/badge/DOI/10.5281/zenodo.7697217.svg" alt="DOI Latest Release"/>

</a>

</div>

<p align="center">

<b>Documentation</b>:

<a href="https://docs.pola.rs/api/python/stable/reference/index.html">Python</a>

-

<a href="https://docs.rs/polars/latest/polars/">Rust</a>

-

<a href="https://pola-rs.github.io/nodejs-polars/index.html">Node.js</a>

-

<a href="https://pola-rs.github.io/r-polars/index.html">R</a>

|

<b>Agents</b>:

<a href="https://github.com/polars-inc/skills/tree/main/polars">Skill</a>

-

<a href="https://docs.pola.rs/user-guide/misc/polars_llms/">MCP</a>

|

<a href="https://docs.pola.rs/">User guide</a>

|

<a href="https://discord.gg/4UfP5cfBE7">Discord</a>

</p>

Polars: Extremely fast Query Engine for DataFrames

Polars is an analytical query engine for DataFrames, written in Rust. It is designed to be fast,

easy to use and expressive. Key features are:

• Fast: written from the ground up in Rust with multi-threaded, vectorized (SIMD) execution
• Lazy & eager execution: with query optimization out of the box
• Larger-than-RAM: the streaming engine processes datasets that don't fit in memory
• Expressive API: compose complex queries with powerful expressions
• Extensible: extend Polars natively with custom code through

I/O and Expression plugins

• Multi-language: bindings for Python, Rust, Node.js, R, and SQL
• GPU support: optionally accelerate queries on NVIDIA GPUs
• Interoperable: uses the

Apache Arrow Columnar Format for zero-copy

data sharing

To learn more, read the user guide.

Polars in action

Queries are composed from expressions. This lazy query gets optimized out of the box and runs in

parallel across all available cores:

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"),
        pl.len().alias("n_orders"),
    )
    .sort("total", descending=True)
    .collect()
)

Performance

Polars is very fast. In fact, it is one of the best performing Dataframe solutions available. See

the PDS-H benchmarks results.

Handles larger-than-RAM data

If you have data that does not fit into memory, Polars' query engine is able to process your query

(or parts of your query) in a streaming fashion. This drastically reduces memory requirements, so

you might be able to process your 250GB dataset on your laptop. Collect with

collect(engine='streaming') to run the query streaming.

Installation

Python

Install the latest Polars version with:

pip install polars

See the User Guide for more details

on optional dependencies

<details>

<summary><b>Compile Polars from source</b></summary>

If you want a bleeding edge release you should compile Polars from source. Advanced users can also

compile for maximum performance for their architecture.

This can be done by going through the following steps in sequence:

1. Install the latest Rust compiler
2. Install maturin: pip install maturin
3. cd py-polars and choose one of the following:

• make build, slow binary with debug assertions and limited symbols, fast compile times
• make build-debug, same as make build, but with all symbols, produces large binaries
• make build-release, fast binary without debug assertions, minimal debug symbols, long compile

times

• make build-nodebug-release, same as build-release but without any debug symbols, slightly

faster to compile

• make build-debug-release, same as build-release but with full debug symbols, slightly slower

to compile

• make build-dist-release, fastest binary, extreme compile times

By default the binary is compiled with optimizations turned on for a modern CPU. Specify LTS_CPU=1

with the command if your CPU is older and does not support e.g. AVX2.

Note that the Rust crate implementing the Python bindings is called py-polars to distinguish from

the wrapped Rust crate polars itself. However, both the Python package and the Python module are

named polars, so you can pip install polars and import polars.

</details>

Check the Installation guide for more advanced

installations. For example when you expect more than 2^32 (~4.2 billion) rows, run on an old CPU

(e.g. dating from before 2011), or on an x86-64 build of Python on Apple Silicon under Rosetta.

Contributing

Want to contribute? Read our contributing guide

and check the issue tracker for accepted issues.

Contributors new to the codebase can look for the good first issue label to get familiar with the

project.

You can join the Polars Discord server for any help along the way.

Distributed Polars

Running into hardware limitations executing your queries? Read how you can

horizontally scale your Polars query on a cluster.

License

Polars is licensed under the MIT License (SPDX: MIT).