Dask
技能 已验证 活跃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.
To allow users to scale their existing pandas and NumPy workflows beyond memory limits or across clusters using the Dask library.
功能
- Larger-than-RAM execution on single machines
- Parallel processing across multiple cores
- Distributed computation for terabyte-scale datasets
- Familiar pandas/NumPy APIs for DataFrames and Arrays
- Task-based parallelization with Futures
使用场景
- Process datasets that exceed available RAM
- Scale pandas or NumPy operations to larger datasets
- Parallelize computations for performance improvements
- Process multiple files efficiently (CSVs, Parquet, JSON, text logs)
非目标
- Out-of-core analytics on a single machine (use vaex)
- In-memory speed optimization (use polars)
安装
npx skills add K-Dense-AI/claude-scientific-skills通过 npx 运行 Vercel skills CLI(skills.sh)— 需要本地安装 Node.js,以及至少一个兼容 skills 的智能体(Claude Code、Cursor、Codex 等)。前提是仓库遵循 agentskills.io 格式。
质量评分
已验证类似扩展
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