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dask

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Distributed computing for larger-than-RAM pandas/NumPy workflows. Use when you need to scale existing pandas/NumPy code beyond memory or across clusters. Best for parallel file processing, distributed ML, integration with existing pandas code. For out-of-core analytics on single machine use vaex; for in-memory speed use polars.

skills/11-数据分析与统计建模/dask/SKILL.md

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# Dask

## Overview

Dask is a Python library for parallel and distributed computing that enables three critical capabilities:
- **Larger-than-memory execution** on single machines for data exceeding available RAM
- **Parallel processing** for improved computational speed across multiple cores
- **Distributed computation** supporting terabyte-scale datasets across multiple machines

Dask scales from laptops (processing ~100 GiB) to clusters (processing ~100 TiB) while maintaining familiar Python APIs.

## When to Use This Skill

This skill should be used when:
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