This tutorial will take you through all of the steps involved in exploring data of many different types and sizes, building simple and complex figures, working with billions of data points, adding interactive behavior, widgets and controls, and deploying full dashboards and applications.
The tutorial outlined here is given as a half-day course led by trained instructors. For self-paced usage, you should consult the main tutorial index.
We'll be using a wide range of open-source Python libraries, including the Anaconda-supported tools HoloViews, GeoViews, Bokeh, Datashader, and Param:
These libraries have been carefully designed to work together to address a very wide range of data-analysis and visualization tasks, making it simple to discover, understand, and communicate the important properties of your data. For more background on these tools and why and how they are integrated, see pyviz.org/background.
This notebook serves as the homepage of the tutorial, including a table of contents listing each tutorial section. Here, timings listed in brackets ("[2 min]") indicate material that will be skimmed in the live tutorial, but which can be examined later in more detail as self-paced material.
Overview
Making data visualizable
Datasets and collections of data
Dynamic interactions
Apps and dashboards
You will find extensive support material on the websites for each package. You may find these links particularly useful during the tutorial: