dask
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.
- Version
- 1.1
- License
- BSD-3-Clause license
- Compatibility
- Requires Python 3.10+ and dask 2025.1+. DataFrame workflows need pandas 2+ and PyArrow 16+. Cloud paths (s3://, gcs://) need s3fs or gcsfs. Cluster deployment uses dask.distributed (included with dask[complete]).
Pinned to revision de66e10cd0c8, so it is the text this page describes rather than whatever the author pushed since.
Pre-approved tools experimental
Experimental field. Support varies between clients, so this list is what the author declared, not what your client will enforce.
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- Bash
Files
- skills/dask/SKILL.md
- skills/dask/references/arrays.md
- skills/dask/references/bags.md
- skills/dask/references/best-practices.md
- skills/dask/references/dataframes.md
- skills/dask/references/futures.md
- skills/dask/references/schedulers.md
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