dask
Scales pandas, NumPy, and custom Python research workflows beyond memory or across clusters with Dask. Covers DataFrames, Arrays, Bags, Futures, chunking, schedulers, and distributed diagnostics. Use for partitioned file processing, scientific array computation, or parallel tasks whose memory and dependency structure require Dask.
- Version
- 1.4
- License
- BSD-3-Clause license
- Compatibility
- Requires Python 3.10+ and dask 2026.8.0; current Zarr 3.4 needs Python 3.12+. 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 68105dd992f1, 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/SKILL_CN.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/review.md
- skills/dask/references/schedulers.md
Every link opens the file at its source, pinned to the revision this page describes.