dask
Parallel PyData with Task Scheduling
Decision gist · record as of 2026-08-14
Yes. Dask is production-stable (Development Status 5), actively maintained, permissively licensed, and widely adopted (top 1000 PyPI packages). Install friction is low and there are no known vulnerabilities. It is the standard choice for scaling analytics workloads in Python when you need parallelism.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later.
- Low friction installation with a pure-Python wheel and eight common dependencies.
- Active maintenance with a recent release (31 days old) and strong community engagement (13888 GitHub stars).
License · maintenance · safety
BSD-3-Clause (permissive) — BSD-3-Clause (permissive) license allows commercial and private use with minimal restrictions beyond attribution and liability disclaimers.
last release 2026-07-14 (31 days) · last repo commit 2026-08-10 · 13,888 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 30,340,726 downloads/mo, #803 on PyPI
Alternatives
Verify before relying
pip install dask
import dask.dataframe as dd
df = dd.read_csv('data.csv')
result = df.groupby('column').sum().compute()- Whether the package supports free-threading mode (classifier mentions 'Free Threading :: 1 - Unstable') and what that means for production use
- Performance characteristics and overhead compared to synchronous execution for small datasets
What it is and what it does
Dask extends the PyData ecosystem with parallel and distributed computing capabilities. It uses task graphs and lazy evaluation to defer computation until explicitly triggered, allowing you to work with datasets larger than memory by breaking them into manageable chunks. The library handles scheduling across multiple cores on a single machine or across a cluster, making it useful for analytics pipelines that would otherwise be bottlenecked by sequential execution or memory constraints.
The package integrates with familiar data structures—Dask DataFrames mimic Pandas, Dask Arrays mimic NumPy—so existing code often requires minimal changes. It depends on click, cloudpickle, fsspec, packaging, partd, pyyaml, toolz, and importlib_metadata to handle CLI interaction, serialization, file systems, dependency resolution, data partitioning, configuration, functional utilities, and metadata discovery.
Use it for
- Process multi-gigabyte CSV or Parquet files that don't fit in RAM by partitioning them into chunks
- Parallelize machine learning model training or hyperparameter tuning across multiple cores or nodes
- Build ETL pipelines that combine data loading, transformation, and aggregation with automatic task scheduling
- Scale operations across a cluster without rewriting core logic
- Defer expensive computations until needed, then execute them efficiently in parallel
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
Dask is production-stable (Development Status 5), actively maintained, permissively licensed, and widely adopted (top 1000 PyPI packages). Install friction is low and there are no known vulnerabilities. It is the standard choice for scaling analytics workloads in Python when you need parallelism.
Install
dask on PyPI
Before you install
Low friction installation with a pure-Python wheel and eight common dependencies. Active maintenance with a recent release (31 days old) and strong community engagement (13888 GitHub stars).
Requires Python 3.10 or later.
License in practice
BSD-3-Clause (permissive) license allows commercial and private use with minimal restrictions beyond attribution and liability disclaimers.
Quickstart
pip install dask
import dask.dataframe as dd
df = dd.read_csv('data.csv')
result = df.groupby('column').sum().compute()Verify before relying
- Whether the package supports free-threading mode (classifier mentions 'Free Threading :: 1 - Unstable') and what that means for production use
- Performance characteristics and overhead compared to synchronous execution for small datasets
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 packagesclickcloudpicklefsspecpackagingpartdpyyamltoolzimportlib_metadata |
| Maintenance | Actively maintained 31 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 30,340,726 / month, #803 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | 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 |
Evidence: dask-2026.7.1-py3-none-any.whl
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See also coiled · daft · dask-image · distributed · dask-ml · dask-geopandas · dask-jobqueue · dask-awkward · mapply · pandarallel