polars

Documentation: Python - Rust - Node.js - R | Agents: Skill - MCP | User guide | Discord

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

Compile Polars from source

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:

Install the latest Rust compiler
Install maturin: pip install maturin
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.

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).

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