When your pandas DataFrame won't fit in RAM or NumPy operations are crawling on a single core, this is what you reach for. It parallelizes familiar pandas and NumPy APIs across multiple cores or machines without rewriting your code. The lazy evaluation model is key: operations build a task graph until you call compute(), so you can chain transformations efficiently. Choose DataFrames for tabular data, Arrays for scientific computing, Bags for log files and JSON, or Futures when you need fine-grained control over task execution. The threaded scheduler works great for numeric operations, but switch to processes for Python-heavy work that hits the GIL.
- skills.sh installs
- 246
- GitHub stars
- 33.2K
- last updated
- Aug 21, 2026
Install
$ npx -y skills add davila7/claude-code-templates --skill dask --agent claude-codeInstalls into .claude/skills of the current project.
Files
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:
- 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)
- Build custom parallel workflows with task dependencies
- Distribute workloads across multiple cores or machines
Core Capabilities
Dask provides five main components, each suited to different use cases:
1. DataFrames - Parallel Pandas Operations
Purpose: Scale pandas operations to larger datasets through parallel processing.
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- Repository
- github.com/davila7/claude-code-templates
- Categories
- Data Science & ML, Automation & Workflows
- First seen
- Jun 3, 2026
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