Dask Data Science
技能 已验证 活跃Part of the AlterLab Academic Skills suite. 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 provide an expert assistant for scaling data science workflows using Dask, enabling users to process datasets that exceed single-machine memory or require parallel computation.
功能
- Distributed computing for pandas/NumPy
- Larger-than-memory data processing
- Parallel file processing
- Integration with existing pandas/NumPy code
- Scales from laptops to clusters
使用场景
- Scaling pandas operations to larger datasets
- Parallelizing computations for performance
- Processing multiple files efficiently (CSVs, Parquet, JSON)
- Distributing workloads across multiple cores or machines
非目标
- Out-of-core analytics on a single machine (use vaex)
- In-memory speed optimization (use polars)
- Replacing core pandas/NumPy functionality for in-memory data
工作流
- Load data using Dask's parallel readers (read_csv, read_parquet)
- Perform operations (filtering, transformations, aggregations) on Dask DataFrames, Arrays, or Bags
- Leverage Dask's lazy evaluation and task graph construction
- Trigger computation with .compute() or dask.compute()
- Optimize performance through chunking, persist, and scheduler selection
- Save results or convert to pandas for final analysis
安装
npx skills add AlterLab-IEU/AlterLab-Academic-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
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