# Ds Polars Performance

> High-performance data processing using the Rust-based Polars library.

- Skill: `jcorpac/ds-polars-performance` (Agent Skill)
- Install (CLI): `npx skillmds@latest add jcorpac/ds-polars-performance`
- Raw SKILL.md: https://api.skillmd.com/api/skills/jcorpac/ds-polars-performance/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: jcorpac (https://skillmd.com/u/jcorpac)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/jcorpac/ds-polars-performance

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# Polars Performance

Polars is a lightning-fast DataFrame library written in Rust, designed for massive datasets where Pandas hits memory or speed limits.

## Core Concepts
- **Eager vs Lazy**: Use `pl.scan_csv()` for lazy execution, allowing Polars to optimize the query plan before execution.
- **Expressions**: Polars uses a powerful expression API (`pl.col("name").filter(...)`) that is more readable and faster than standard indexing.

## Memory Management
- **Zero-copy**: Polars uses Apache Arrow memory format, which allows for efficient data sharing.
- **Streaming**: Handle datasets larger than RAM by processing them in chunks.

## Best Practices
- **Prefer Expressions**: Avoid using `apply()` or custom Python loops; use built-in Polars expressions for maximum performance.
- **Type Safety**: Leverage Polars' strict schema enforcement to prevent data quality issues.