# Ds Polars Performance > High-performance data processing using the Rust-based Polars library. Use when this capability is needed. - Skill: `tomevault-io/ds-polars-performance` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add tomevault-io/ds-polars-performance` - Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/ds-polars-performance/raw - Safety review: pending (external: skill-scanner PASS, skillspector PASS) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - Author: tomevault-io (https://skillmd.com/u/tomevault-io) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/tomevault-io/ds-polars-performance --- # 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. --- > Converted and distributed by [TomeVault](https://tomevault.io/claim/jcorpac) — claim your Tome and manage your conversions.