Polars is an open-source library for data manipulation, known for being one of the fastest data processing solutions on a single machine. It features a well-structured, typed API that is both …

Getting started This chapter is here to help you get started with Polars. It covers all the fundamental features and functionalities of the library, making it easy for new users to familiarise themselves with …

Polars is a blazingly fast DataFrame library for manipulating structured data. The core is written in Rust, and available for Python, R and NodeJS. Key features Fast: Written from scratch in Rust, designed …

Python API reference # This page gives a high-level overview of all public Polars objects, functions and methods. All classes and functions exposed in the polars.* namespace are public.

Installation Polars is a library and installation is as simple as invoking the package manager of the corresponding programming language.

iterator of tuples (default) or dictionaries (if named) of python row values iterator of tuples (default) or dictionaries (if named) of python row values Warning Row iteration is not optimal as the underlying …

Pandas doesn't have an expression system and often requires Python lambda s to express the complexity you want. Polars sees the requirement of a Python lambda as a lack of expressiveness of …

User-defined Python functions Note Before writing custom Python functions, consider using Polars plugins which offer better performance and integration. Check out the expression plugins …

Basic arithmetic Polars supports basic arithmetic between series of the same length, or between series and literals. When literals are mixed with series, the literals are broadcast to match the length of the …

This might only speed up your code if the amount of work per element is significant and the python function releases the GIL (e.g. via calling a c function) ‘thread_local’: run the python function on a …