Project Summary
Task scheduling and blocked algorithms for parallel processing.
Dask is a flexible parallel computing library for analytics. Dask emphasizes the following virtues:
Familiar: Provides parallelized NumPy array and Pandas DataFrame objects
Native: Enables distributed computing in Pure Python with access to the PyData stack.
Fast: Operates with low overhead, low latency, and minimal serialization necessary for fast numerical algorithms
Flexible: Supports complex and messy workloads
Scales up: Runs resiliently on clusters with 100s of nodes
Scales down: Trivial to set up and run on a laptop in a single process
Responsive: Designed with interactive computing in mind it provides rapid feedback and diagnostics to aid humans
In a Nutshell, PyData Dask...
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has a well established, mature codebase
maintained by a very large development team
with decreasing Y-O-Y commits -
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took an estimated 35 years of effort (COCOMO model)
starting with its first commit in December, 2014
ending with its most recent commit 3 days ago
Quick Reference
3-Clause BSD License
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Project Security
Vulnerabilities per Version ( last 10 releases )
Project Vulnerability Report
Security Confidence Index
Vulnerability Exposure Index
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30 Day SummaryMay 3 2026 — Jun 2 2026
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12 Month SummaryJun 2 2025 — Jun 2 2026
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