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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

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).