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 expressive and easy to use. Polars Cloud is currently available to a group of select organizations.

Jun 28, 2024 · Polars is a blazingly fast Data Manipulation library for Python, specifically designed for handling large datasets with efficiency. It leverages Rust's memory model and parallel processing capabilities, offering significant performance advantages over …

Welcome to the world of Polars, a powerful DataFrame library for Python! In this showcase tutorial, you'll get a hands-on introduction to Polars' core features and see why this library is catching so much buzz.

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.

Constructing a Series with a specific dtype: It is possible to construct a Series with values as the first positional argument. This syntax considered an anti-pattern, but it can be useful in certain scenarios. You must specify any other arguments through keywords. Methods: Compute absolute values. Rename the series.

Data in a Polars DataFrame can be visualized using common visualization libraries. We illustrate plotting capabilities using the Iris dataset. We read a CSV and then plot one column against another, colored by a yet another column. Polars has a plot method to create plots using Altair: This is shorthand for:

Apr 11, 2025 · Polars: Blazingly fast DataFrames in Rust, Python, Node.js, R, and SQL. Polars is a DataFrame interface on top of an OLAP Query Engine implemented in Rust using Apache Arrow Columnar Format as the memory model. Lazy | eager execution; Multi-threaded; SIMD; Query optimization; Powerful expression API; Hybrid Streaming (larger-than-RAM datasets)

Apr 6, 2025 · Here's a cheat sheet for the Polars Python package, covering many of its key functions and features: # Install Polars with all optional dependencies: . # From list of dictionaries df = pl. DataFrame ([{'A': 1, 'B': 'a'}, {'A': 2, 'B': 'b'}]) # From CSV df = pl. read_csv ('file.csv')

Polars: Blazingly fast DataFrames in Rust, Python, Node.js, R, and SQL Polars is a DataFrame interface on top of an OLAP Query Engine implemented in Rust using Apache Arrow Columnar Format as the memory model.

Oct 24, 2023 · Here are 3 potential solutions to the problem (adjusted slightly to provide a more comprehensive look at the questions by adding a group column): Data: for j in range(m): groups.append(i) "id_": list(range(0, m)) * n, "group": groups, "value": [random.randint(1, 50) / 100 for _ in range(n*m)] pl.col("value").cast(pl.Float32).rolling_map(