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](https://docs.pola.rs/user-guide/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](https://arrow.apache.org/docs/format/Columnar.html) for zero-copy data sharing To learn more, read the [user guide](https://docs.pola.rs/). ## 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: ```python 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](https://www.pola.rs/benchmarks.html) 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: ```sh pip install polars ``` See the [User Guide](https://docs.pola.rs/user-guide/installation/#feature-flags) 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: 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 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](https://docs.pola.rs/user-guide/installation/) 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](https://docs.pola.rs/development/contributing/) 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](https://discord.gg/4UfP5cfBE7) 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](https://docs.pola.rs/polars-cloud/). ## License Polars is licensed under the [MIT License](LICENSE) (SPDX: `MIT`).