dask

Parallel PyData with Task Scheduling

dask Permissive license BSD-3-Clause Active ★ 13,886 v2026.7.1 released 2026-07-14

Install

dask on PyPI

pip

pip install dask

uv

uv add dask

poetry

poetry add dask

Package facts

License	BSD-3-Clause (permissive)
Python support	supports the current Python release (>=3.10)
Install friction	low — pure-Python wheel
Runtime dependencies	8 — click, cloudpickle, fsspec, packaging, partd, pyyaml, toolz, importlib_metadata
Maintenance	actively maintained — 30 days since the last release
Last repo commit	2026-08-10
First released	2015-01-29
Popularity	one of the top 1,000 most-downloaded packages on PyPI (30-day window, as of 2026-08-13)
Known vulnerabilities	none known (OSV.dev, checked 2026-08-13)

Evidence: dask-2026.7.1-py3-none-any.whl

Keywords: task-scheduling, parallel, numpy, pandas, pydata

Development Status :: 5 - Production/StableIntended Audience :: DevelopersIntended Audience :: Science/ResearchOperating System :: OS IndependentProgramming Language :: PythonProgramming Language :: Python :: 3Programming Language :: Python :: 3 :: OnlyProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Programming Language :: Python :: Free Threading :: 1 - UnstableTopic :: Scientific/EngineeringTopic :: System :: Distributed Computing

About dask

from the package's own PyPI description — quoted content, verbatim

Dask

Dask is a flexible parallel computing library for analytics. See documentation_ for more information.

LICENSE

New BSD. See License File <https://github.com/dask/dask/blob/main/LICENSE.txt>__.

.. _documentation: https://dask.org .. |Build Status| image:: https://github.com/dask/dask/actions/workflows/tests.yml/badge.svg :target: https://github.com/dask/dask/actions/workflows/tests.yml .. |Coverage| image:: https://codecov.io/gh/dask/dask/branch/main/graph/badge.svg :target: https://codecov.io/gh/dask/dask/branch/main :alt: Coverage status .. |Doc Status| image:: https://readthedocs.org/projects/dask/badge/?version=latest :target: https://dask.org :alt: Documentation Status .. |Discourse| image:: https://img.shields.io/discourse/users?logo=discourse&server=https%3A%2F%2Fdask.discourse.group :alt: Discuss Dask-related things and ask for help :target: https://dask.discourse.group .. |Version Status| image:: https://img.shields.io/pypi/v/dask.svg :target: https://pypi.python.org/pypi/dask/ .. |NumFOCUS| image::...

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AI interpretation — verify before relying

AI-generated interpretation of the package facts above; every digit, version, license, or vulnerability id it cites is grounded in the facts already shown on this page

Dask is a flexible parallel computing library that enables distributed task scheduling and analytics workloads across multiple cores or machines, integrating with NumPy and Pandas data structures.

Low install friction with a pure-Python wheel distribution and eight lightweight runtime dependencies. Actively maintained with a release 30 days ago and strong community adoption; supports Python 3.10–3.14.

BSD-3-Clause (permissive) allows commercial and private use with minimal restrictions, requiring only license and copyright notice retention.

Usage

pip install dask
import dask.dataframe as dd
df = dd.read_csv('data.csv')
result = df.groupby('column').mean().compute()

Requires Python ≥3.10; distributed computing features may require additional scheduler setup.

Verdict: Dask is a mature, actively maintained parallel computing framework with permissive BSD-3-Clause licensing and low installation friction. No known vulnerabilities and broad Python version support make it a reliable choice for analytics workloads requiring task scheduling or distributed computation.

Needs verification

Whether optional dependencies (e.g., dask-distributed, dask-cuda) are required for specific use cases beyond local scheduling.
Performance characteristics and scalability limits for the specific analytics workload you intend to run.

parallel computing librarydistributed task schedulingdask dataframeparallel analyticstask scheduler pythondistributed numpy pandasscalable data processing