Polars

A blazingly fast DataFrame library built on Apache Arrow that slots in when pandas becomes a bottleneck but your data still fits in RAM. The expression-based API is cleaner than pandas chaining, lazy evaluation gives you automatic query optimization, and everything parallelizes by default. Think 1-100GB datasets, ETL pipelines, or anywhere you're tired of waiting for pandas to finish. The learning curve is real if you're coming from pandas since there's no index and the syntax is different, but the speed gains and cleaner code usually justify it. Use lazy mode with scan_csv instead of read_csv for anything sizeable, and stick to native expressions instead of Python functions to keep things fast.

skills.sh installs: 2.2K
GitHub stars: 56.6K
last updated: Aug 21, 2026

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Install

$ npx -y skills add k-dense-ai/scientific-agent-skills --skill polars --agent claude-code

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Polars

Overview

Polars is a columnar DataFrame library for Python and Rust with Arrow interoperability. Work with Polars' expression-based API, lazy evaluation framework, and high-performance data manipulation capabilities for efficient data processing, pandas migration, and data pipeline optimization.

Reviewed against the official stable documentation and native Polars 1.44.2. Local regression tests cover the corrected APIs and scientific failure cases; cloud, GPU, BigQuery, and remote database recipes are illustrative and require provider setup. Fragments using undefined columns, paths, or ... require adaptation to the dataset.

Quick Start

Installation and Basic Usage

Install the current stable Polars release verified during this refresh:

uv pip install "polars==1.44.2"

Install optional integrations only when needed:

uv pip install "polars[excel,database,fsspec,pandas,numpy]==1.44.2"

Basic DataFrame creation and operations:

import polars as pl

# Create DataFrame
df = pl.DataFrame({
    "name": ["Alice", "Bob", "Charlie"],
    "age": [25, 30, 35],
    "city": ["NY", "LA", "SF"]
})

# Select columns
df.select("name", "age")

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