Polars

If you're hitting pandas performance walls but your data still fits in RAM, this is the obvious next step. Built on Apache Arrow with lazy evaluation and parallel execution by default, it's genuinely 5-10x faster for typical operations on datasets between 1GB and 100GB. The expression API is cleaner than pandas once you adjust to no index and strict typing. Use `scan_csv` instead of `read_csv` for large files to get automatic query optimization and predicate pushdown. The skill covers the core concepts well, including when to go lazy versus eager and how window functions actually work with `over()`. Just remember it won't help if your data exceeds RAM, that's when you need dask or vaex instead.

skills.sh installs: 124
GitHub stars: 26.9K
last updated: Jun 3, 2026

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Install

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

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Files

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")

SKILL.md on GitHub