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DataFrames for
the new era

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Fast01Fast

Polars is written from the ground up with performance in mind. Its
multi-threaded query engine is written in Rust and designed for
effective paralellism. Its vectorized and columnar processing enable
cache-coherent algorithms and high performance on modern processors.

Easy to use02Easy to use

You will feel right at home with Polars if you are familiar with data
wrangling. Its expressions are intuitive and empower you to write
readable and performant code at the same time.

Open Source03Open Source

Polars is and always will be open source. Driven by an active
community of developers. Everyone is encouraged to add new features
and contribute. It is free to use under the MIT license.

Built by developers for developers to achieve up to 50x Performance

Why use Polars

Polars was benchmarked against several other solutions on the
independent TPCH Benchmark. This benchmark aims to replicate data
wrangling operations used in practice. Polars easily trumps other
solutions due to its parallel execution engine, efficient algorithms
and use of vectorization with SIMD (Single Instruction, Multiple
Data). Compared to pandas, it can achieve more than 30x performance
gains.

Learn more -

The benchmark was performed on a n2-highmem-16 with scale factor 10
including IO. The queries are open source.

Performance illustration

Quick install

Polars is written from the ground up making it easy to install.
Select your programming language and get started!

Python Rust JavaScript
pip install polars
[dependencies]
polars = { version = "x", features = ["lazy", ...]}
const pl = require('nodejs-polars');
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Support for all common data formats

Polars supports reading and writing to all common data formats. This
allows you to easily integrate Polars into your existing data stack.

  * Text: CSV & JSON
  * Binary: Parquet, Delta Lake, AVRO & Excel
  * IPC: Feather, Arrow
  * Databases: MySQL, Postgres, SQL Server, Sqlite, Redshift & Oracle
  * Cloud Storage: S3, Azure Blob & Azure File

How Polars will
make your life easier

01

Easy
to use

Write your queries the way they were intended. Polars, internally,
will determine the most efficient way to execute using its query
optimiser.

02

Embarrassingly
Parallel

Complete your queries faster! Polars fully utilises the power of your
machine by dividing the workload among the available CPU cores
without any additional configuration, nor serialization overhead.

03

Apache
Arrow

Polars utilises the Apache Arrow memory model allowing you to easily
integrate with existing tools in the data landscape. It supports
zero-copy data sharing for efficient collaboration.

04

Close to
the metal

Polars is written from the ground up, designed close to the machine
and without external dependencies. This allows for full control of
the ecosystem (API, memory & execution).

05

Written
in Rust

The core of Polars is written in Rust, one of the fastest growing
programming languages in the world. Rust allows for high performance
with fine grained control over memory.

06

Out
of core

Want to process large data sets that are bigger than your memory? Our
streaming API allows you to process your results efficiently skipping
the need to keep all the data in memory.

 

User guide

Discover more
in our User Guide

Your One-Stop Resource for all information

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Service offerings

Our services, your next step forward

Transforming possibilities to solutions

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