polars

<h1 align="center">
  <a href="https://pola.rs">
    <img src="https://raw.githubusercontent.com/pola-rs/polars-static/master/banner/polars_github_banner.svg" alt="Polars logo">
  </a>
</h1>

<div align="center">
  <a href="https://crates.io/crates/polars">
    <img src="https://img.shields.io/crates/v/polars.svg" alt="crates.io Latest Release"/>
  </a>
  <a href="https://pypi.org/project/polars/">
    <img src="https://img.shields.io/pypi/v/polars.svg" alt="PyPi Latest Release"/>
  </a>
  <a href="https://www.npmjs.com/package/nodejs-polars">
    <img src="https://img.shields.io/npm/v/nodejs-polars.svg" alt="NPM Latest Release"/>
  </a>
  <a href="https://community.r-multiverse.org/polars">
    <img src="https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fcommunity.r-multiverse.org%2Fapi%2Fpackages%2Fpolars&query=%24.Version&label=r-multiverse" alt="R-multiverse Latest Release"/>
  </a>
  <a href="https://doi.org/10.5281/zenodo.7697217">
    <img src="https://zenodo.org/badge/DOI/10.5281/zenodo.7697217.svg" alt="DOI Latest Release"/>
  </a>
</div>

<p align="center">
  <b>Documentation</b>:
  <a href="https://docs.pola.rs/api/python/stable/reference/index.html">Python</a>
  -
  <a href="https://docs.rs/polars/latest/polars/">Rust</a>
  -
  <a href="https://pola-rs.github.io/nodejs-polars/index.html">Node.js</a>
  -
  <a href="https://pola-rs.github.io/r-polars/index.html">R</a>
  -
  <a href="https://docs.pola.rs/api/python/stable/reference/sql/index.html">SQL</a>
  |
  <b>Agents</b>:
  <a href="https://github.com/polars-inc/skills/tree/main/polars">Skill</a>
  -
  <a href="https://docs.pola.rs/user-guide/misc/polars_llms/">MCP</a>
  |
  <a href="https://docs.pola.rs/">User guide</a>
  |
  <a href="https://discord.gg/4UfP5cfBE7">Discord</a>
</p>

## Polars: Extremely fast Query Engine for DataFrames and SQL

Polars is an analytical query engine written in Rust. Write your queries with the DataFrame API, in
SQL, or mix both: they run through the same optimizer and the same engine. 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
- **Spill to disk**: Operations that exceed available RAM can spill to disk
- **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/)
- **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

### DataFrame API

Queries are composed from expressions or SQL. 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.