Blazingly fast DataFrame library
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Documentation: Python - Rust - Node.js - R | StackOverflow: Python - Rust - Node.js - R | User guide | Discord
Polars: Extremely fast Query Engine for DataFrames, written in Rust
Polars is an analytical query engine written for DataFrames. It is designed to be fast, easy to use and expressive. Key features are:
- Lazy | Eager execution
- Streaming (larger-than-RAM datasets)
- Query optimization
- Multi-threaded
- Written in Rust
- SIMD
- Powerful expression API
- Front end in Python | Rust | NodeJS | R | SQL
- Apache Arrow Columnar Format
To learn more, read the user guide.
Performance 🚀🚀
Blazingly fast
Polars is very fast. In fact, it is one of the best performing solutions available. See the PDS-H benchmarks results.
Lightweight
Polars is also very lightweight. It comes with zero required dependencies, and this shows in the import times:
- polars: 70ms
- numpy: 104ms
- pandas: 520ms
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.
Setup
Python
Install the latest Polars version with:
pip install polars
See the User Guide for more details on optional dependencies
To see the current Polars version and a full list of its optional dependencies, run:
pl.show_versions()
Contributing
Want to contribute? Read our contributing guide.
Managed/Distributed Polars
Do you want a managed solution or scale out to distributed clusters? Consider our offering and help the project!
Python: compile Polars from source
If you want a bleeding edge release or maximal performance you should compile Polars from source.
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 symbols, fast compile timesmake 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.
Using custom Rust functions in Python
Extending Polars with UDFs compiled in Rust is easy. We expose PyO3 extensions for DataFrame and
Series data structures. See more in https://github.com/pola-rs/polars/tree/main/pyo3-polars.
Going big...
Do you expect more than 2^32 (~4.2 billion) rows? Compile Polars with the bigidx feature flag or,
for Python users, install pip install polars[rt64].
Don't use this unless you hit the row boundary as the default build of Polars is faster and consumes less memory.
Legacy
Do you want Polars to run on an old CPU (e.g. dating from before 2011), or on an x86-64 build of
Python on Apple Silicon under Rosetta? Install pip install polars[rtcompat]. This version of
Polars is compiled without AVX target
features.
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