Documentation: Python - Rust - Node.js - R - SQL | Agents: Skill - MCP | User guide | Discord

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
• 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.

Queries are composed from expressions or SQL. 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()
)

import polars as pl

df = pl.sql("""
    WITH totals AS (
        SELECT customer_id, SUM(amount) AS total, COUNT(*) AS n_orders
        FROM read_parquet('orders.parquet')
        WHERE status = 'shipped'
        GROUP BY customer_id
    )
    SELECT c.name, t.total, t.n_orders, RANK() OVER (ORDER BY t.total DESC) AS rank
    FROM totals t
    JOIN read_parquet('customers.parquet') c USING (customer_id)
    ORDER BY t.total DESC
""").collect()

DataFrames and LazyFrames in scope can be queried by name, and the result of a SQL query is a regular LazyFrame, so you can switch between SQL and expressions freely:

orders = pl.scan_parquet("orders.parquet")

df = (
    pl.sql("SELECT customer_id, SUM(amount) AS total FROM orders GROUP BY customer_id")
    .filter(pl.col("total") > 1_000)
    .collect()
)

Read more in the SQL user guide.

Polars is very fast. In fact, it is one of the best performing query engines available, for both DataFrame and SQL workloads. See the PDS-H benchmarks results.

Install the latest Polars version with:

pip install polars

See the User Guide for more details on optional dependencies

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.

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.

Running into hardware limitations executing your queries? Read how you can horizontally scale your Polars query on a cluster.

Polars is licensed under the MIT License (SPDX: MIT).