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Blazingly Fast DataFrame Library

Polars is a highly performant DataFrame library for manipulating structured data. The core is written in Rust, but the library is also available in Python. Its key features are:

  • Fast: Polars is written from the ground up, designed close to the machine and without external dependencies.
  • I/O: First class support for all common data storage layers: local, cloud storage & databases.
  • Easy to use: Write your queries the way they were intended. Polars, internally, will determine the most efficient way to execute using its query optimizer.
  • Out of Core: Polars supports out of core data transformation with its streaming API. Allowing you to process your results without requiring all your data to be in memory at the same time
  • Parallel: Polars fully utilises the power of your machine by dividing the workload among the available CPU cores without any additional configuration.
  • Vectorized Query Engine: Polars uses Apache Arrow, a columnar data format, to process your queries in a vectorized manner. It uses SIMD to optimize CPU usage.

Performance 馃殌 馃殌

Polars is very fast, and in fact is one of the best performing solutions available. See the results in h2oai's db-benchmark, revived by the DuckDB project.

Polars TPC-H Benchmark results are now available on the official website.


scan_csv filter group_by collect

import polars as pl

q = (
    .filter(pl.col("sepal_length") > 5)

df = q.collect()

LazyCsvReader filter group_by collect Available on feature csv Available on feature streaming

use polars::prelude::*;

let q = LazyCsvReader::new("docs/data/iris.csv")

let df = q.collect()?;


Polars has a very active community with frequent releases (approximately weekly). Below are some of the top contributors to the project:

ritchie46 stinodego alexander-beedie MarcoGorelli zundertj ghuls reswqa universalmind303 orlp mcrumiller matteosantama nameexhaustion Dandandan magarick ibENPC c-peters moritzwilksch jorgecarleitao jonashaag marcvanheerden borchero cjermain josh ryanrussell cnpryer marioloko thatlittleboy braaannigan cmdlineluser illumination-k jakob-keller messense mhconradt rben01 sorhawell SeanTroyUWO svaningelgem chitralverma YuRiTan elbaro nickray adamgreg CloseChoice jrycw owrior paq JulianCologne olivier-lacroix romanovacca


We appreciate all contributions, from reporting bugs to implementing new features. Read our contributing guide to learn more.


This project is licensed under the terms of the MIT license.