polars 0.12.13


Blazingly fast DataFrame library

Blazingly fast DataFrame library

Stars: 4254, Watchers: 4254, Forks: 236, Open Issues: 84

The pola-rs/polars repo was created 1 years ago and was last updated 2 hours ago.
The project is very popular with an impressive 4254 github stars!

How to Install polars

You can install polars using pip

pip install polars

or add it to a project with poetry

poetry add polars

Package Details

ritchie46 <[email protected]>
GitHub Repo


No  polars  pypi packages just yet.


A list of common polars errors.

No  polars  errors just yet.

Code Examples

Here are some polars code examples and snippets.

No  polars  code examples just yet.

GitHub Issues

The polars package as 84 open issues on GitHub

  • Implement the Array API
  • Suggested improvements for the Polars user guide book
  • CoGrouped Apply
  • add rolling options for rolling median and quantile
  • slice pushdown
  • Read Excel Files in Rust using Polars
  • Parallel reading parquet files from S3
  • Add more interpolation strategies.
  • Feature req: service/wrapper exposing results in arrow format over http
  • Re-infer schema for a dataframe column
  • quantile always return float
  • Minor: inconsistent table repr for float values
  • Serde logical types
  • Read CSV files created by R (header line is one line shorter)
  • Rust Python wrapper code depends on Python code

See more issues on GitHub

See Also

pandas-ta 0.3.14b

An easy to use Python 3 Pandas Extension with 130+ Technical Analysis Indicators. Can be called from a Pandas DataFrame or standalone like TA-Lib. Correlation tested with TA-Lib.

woodwork 0.11.1

a two-dimensional data object with labeled axes and typing information

pantsbuild.pants 2.9.0

The ergonomic and hermetic software build system for Python, Java, Scala, Go, and Shell. Pants lets you fearlessly scale up your codebase.

pandera 0.8.1

A light-weight and flexible data validation and testing tool for dataframes.

orjson 3.6.5

Fast, correct Python JSON library supporting dataclasses, datetimes, and numpy