Dask | Scale the Python tools you love
Dask is a flexible open-source Python library for parallel computing maintained by OSS contributors across dozens of companies including Anaconda, Coiled, SaturnCloud, and nvidia.
Dask is an open-source library for parallel computing in Python that scales from single machines to clusters of thousands of nodes. It is designed to integrate seamlessly with the existing Python ecosystem, including libraries like NumPy, Pandas, and Scikit-learn, enabling users to scale their data science workflows without significant changes to their code.
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Website Key Features
Scalability
Dask scales from single machines to clusters of thousands of nodes, making it suitable for both small and large-scale data processing tasks.
Integration
Seamlessly integrates with the existing Python ecosystem, including libraries like NumPy, Pandas, and Scikit-learn.
Dynamic Task Scheduling
Optimizes task scheduling dynamically to improve performance and resource utilization.
Fault Tolerance
Provides mechanisms for fault tolerance, ensuring that computations can continue even in the presence of failures.
Interactive Computing
Supports interactive computing with tools like Jupyter notebooks, making it easier to explore and analyze data.
Dataframe API
Offers a DataFrame API that is similar to Pandas, enabling users to work with large datasets that don't fit into memory.
Array API
Provides an Array API that is similar to NumPy, allowing for parallel and distributed computation on large arrays.
Machine Learning
Includes support for parallel and distributed machine learning, compatible with Scikit-learn.
Customizable
Allows for customization and extension, enabling users to tailor Dask to their specific needs.
Community Support
Backed by a vibrant community of users and contributors, providing a wealth of resources and support.
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Additional information
License
Dask is released under the BSD 3-Clause License, making it free for both personal and commercial use.
Development
Actively developed and maintained by a dedicated team of contributors, with regular updates and new features.
Documentation
Comprehensive documentation is available, including tutorials, examples, and API references, to help users get started and make the most of Dask.
Community and Support
Dask has a strong community presence, with active forums, mailing lists, and chat rooms where users can seek help and share knowledge.
Use Cases
Dask is used in a wide range of applications, from academic research to industry, for tasks such as data analysis, machine learning, and scientific computing.
HTTP headers
Security headers report is a very important part of user data protection. Learn more about http headers for dask.org