Dask is a Python library for parallel and distributed computing. Dask is: Easy to use and set up (it’s just a Python library) Powerful at providing scale, and unlocking complex algorithms and Fun 🎉

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 routinely run on thousand-machine clusters to process hundreds of terabytes of data efficiently within secure environments. Dask has utilities and documentation on how to deploy in-house, on the cloud, or on HPC super-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.

How to Install Dask You can install Dask with conda, with pip, or install from source.

Dask is a parallel and distributed computing library that scales the existing Python and PyData ecosystem. Dask can scale up to your full laptop capacity and out to a cloud cluster.

This page contains suggestions for Dask best practices and includes solutions to common Dask problems. This document specifically focuses on best practices that are shared among all of the Dask APIs.

Dask collections match existing numpy and pandas methods, so they should feel familiar. Call the method to set up the task graph, and then call compute to get the result.

A Dask DataFrame is a large parallel DataFrame composed of many smaller pandas DataFrames, split along the index. These pandas DataFrames may live on disk for larger-than-memory computing on a single machine, or on many different machines in a cluster.

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.