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 use is widespread, across all industries and scales. Dask is used anywhere Python is used and people experience pain due to large scale data, or intense computing.

Dask has utilities and documentation on how to deploy in-house, on the cloud, or on HPC super-computers. It supports encryption and authentication using TLS/SSL certificates.

This turns lazy Dask collections into Dask collections with the same metadata, but now with their results fully computed or actively computing in the background.

Dask DataFrame helps you process large tabular data by parallelizing pandas, either on your laptop for larger-than-memory computing, or on a distributed cluster of computers.

Get inspired by learning how people are using Dask in the real world today, from biomedical research and earth science to financial services and urban engineering. Explore these examples of APIs to …

Quansight offers a number of PyData courses, including Dask and Dask-ML. For a more comprehensive list of past talks and other resources see Talks & Tutorials in the Dask documentation.

This is a short overview of Dask geared towards new users. Additional Dask information can be found in the rest of the Dask documentation.

These examples show how to use Dask in a variety of situations. First, there are some high level examples about various Dask APIs like arrays, dataframes, and futures, then there are more in-depth …

The parent library Dask contains objects like dask.array, dask.dataframe, dask.bag, and dask.delayed, which automatically produce parallel algorithms on larger datasets.