Tutorials and Examples

Dask Futures Tutorial

Parallelize your custom Python workflows by scheduling tasks with Dask Futures. Learn to scale for-loop-y code that scrapes, parses, and cleans data from Stack Overflow.

Dask DataFrames Tutorial

Learn best practices for working with larger-than-memory DataFrames. Tune Parquet storage, navigate inconvenient file sizes and data types, optimize queries, build features, and explore the challenging NYC Uber/Lyft dataset with pandas and Dask.

Introduction to Dask Tutorial Playlist

This 6-module tutorial walks through the basics of Dask and shows how PyData users can unlock the power of parallel computing.

In-Depth Dask Tutorial

This is a 90-minute Dask tutorial covering the basics of using Dask, from Dask community leader Jacob Tomlinson. This tutorial introduces Dask and parallel data analysis more generally.

Python Code, In Parallel

Dask lets you parallelize your Python code, regardless of what libraries you use. In this example we show Dask futures, a low level API to run your Python functions in parallel either on your own computer or on a cluster.

Python Code, In Parallel

Dask and Pandas work together to provide intuitive data processing at very large scale. This video loads a few hundred gigabytes of Parquet data loaded from Amazon S3, and then does some basic analysis. It gives a sense for how Dask and Pandas are to use together.