Use import polars.selectors as cs for selector examples.

Bundled upstream support

Supporting files are included at skill-relative paths. See UPSTREAM-NOTICE.md for their pinned sources, licenses, hashes, and import changes.

Polars

When to Use

• You need a faster in-memory DataFrame workflow than pandas for data that still fits in RAM.
• You are building ETL, analytics, or transformation pipelines that benefit from lazy evaluation and parallel execution.
• You want expression-based tabular operations on top of Apache Arrow semantics.

Overview

Polars is a lightning-fast DataFrame library for Python and Rust built on Apache Arrow. 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.

Correctness and execution boundaries