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## 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](https://arrow.apache.org/docs/format/Columnar.html) To learn more, read the [user guide](https://docs.pola.rs/). ## Performance 🚀🚀 ### Blazingly fast Polars is very fast. In fact, it is one of the best performing solutions available. See the [PDS-H benchmarks](https://www.pola.rs/benchmarks.html) 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: ```sh pip install polars ``` See the [User Guide](https://docs.pola.rs/user-guide/installation/#feature-flags) for more details on optional dependencies To see the current Polars version and a full list of its optional dependencies, run: ```python pl.show_versions() ``` ## Contributing Want to contribute? Read our [contributing guide](https://docs.pola.rs/development/contributing/). ## Managed/Distributed Polars Do you want a managed solution or scale out to distributed clusters? Consider our [offering](https://cloud.pola.rs/) 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: 1. Install the latest [Rust compiler](https://www.rust-lang.org/tools/install) 2. Install [maturin](https://maturin.rs/): `pip install maturin` 3. `cd py-polars` and choose one of the following: - `make build`, slow binary with debug assertions and symbols, fast compile times - `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`. ## 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](https://en.wikipedia.org/wiki/Advanced_Vector_Extensions) target features.

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