Polars Skill

Polars DataFrame library for high-performance data manipulation in Python. Covers lazy/eager execution, expressions, I/O (CSV, Parquet, JSON, database), aggregations, joins, string/datetime operations, pandas/NumPy interop, and performance optimization. Use when working with Polars DataFrames, migrating from pandas, reading Parquet files, or optimizing data pipeline performance.

Comprehensive skill for high-performance data manipulation with Polars. Use decision trees below to find the right guidance, then load detailed references.

What is Polars?

Polars is a fast DataFrame library for Python (and Rust):

• Fast: Written in Rust, optimized for modern CPUs with SIMD and parallelism
• Lazy Evaluation: Build query plans that get optimized before execution
• Expressive: Powerful expression API for complex transformations
• Memory Efficient: Columnar format, streaming for larger-than-memory data
• No Dependencies: Pure Rust core, no NumPy/Pandas required

Version Notes