Xarray & Dask Tutorial | Dask Down Under: Introduction to Xarray & Dask | Dask Summit 2021

This is a tutorial and introduction to Xarray & Dask given at the 2021 Dask Summit.

The speakers are Nick Mortimer, Ben Leighton, and Tisham Dhar.

Dask down under is a chance for everyone in Oceania to forge links and build community here in our backyard. Dask down under we feature talks, tutorials and panel discussions on using Dask to accelerate research. All levels from beginner to expert are encouraged to attend.

Featuring five talks, tutorials, a panel discussion, and a meet and greet session, there's sure to be plenty to choose from!

What is the Dask Summit?

The Dask Distributed Summit is where users, contributors, and newcomers can share experiences to learn from one another and grow together. The Dask Distributed Summit provides content, information, and learning opportunities for attendees of all levels of Dask familiarity and expertise.

What is Xarray?

xarray (formerly xray) is an open source project and Python package that makes working with labelled multi-dimensional arrays simple, efficient, and fun!

Xarray introduces labels in the form of dimensions, coordinates and attributes on top of raw NumPy-like arrays, which allows for a more intuitive, more concise, and less error-prone developer experience. The package includes a large and growing library of domain-agnostic functions for advanced analytics and visualization with these data structures.

Xarray is inspired by and borrows heavily from pandas, the popular data analysis package focused on labelled tabular data. It is particularly tailored to working with netCDF files, which were the source of xarray’s data model, and integrates tightly with d

What is Dask?

Dask is a free and open-source library for parallel computing in Python. Originally developed by Matthew Rocklin, Dask is a community project maintained by developers and organizations.

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KEY MOMENTS
00:00:00 Intro
00:04:10 Xarray Tutorial Overview
00:06:00 Xarray Primer
00:13:02 Multidimensional Data
00:24:45 Multidimensional Indexing
00:33:40 More Complicated Example: Weighted Mean
00:46:03 Lab Exercise
00:55:25 Lab Solution
01:21:53 Dask Tutorial
01:28:17 Dask Arrays
01:32:11 Bigger Calculations
01:37:40 Dask Delayed