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-polarsand choose one of the following:make build, slow binary with debug assertions and limited symbols, fast compile timesmake build-debug, same asmake build, but with all symbols, produces large binariesmake build-release, fast binary without debug assertions, minimal debug symbols, long compile timesmake build-nodebug-release, same as build-release but without any debug symbols, slightly faster to compilemake build-debug-release, same as build-release but with full debug symbols, slightly slower to compilemake 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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