HoloViz Tutorial

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.

We'll be using a wide range of open-source Python libraries, but focusing on the tools we help maintain as part of the HoloViz project: Panel, hvPlot, HoloViews, GeoViews, Datashader, Param, and Colorcet.

These tools were previously part of PyViz.org, but have been pulled out into HoloViz.org to allow PyViz to be fully neutral and general.

The HoloViz tools have been carefully designed to work together with each other and with the SciPy ecosystem 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.

This notebook serves as the homepage of the tutorial, including a table of contents letting you launch each tutorial section.

Index and Schedule

  • Introduction and setup

    •   5 min  Setup: Setting up the environment and data files.
    • 20 min  Overview: Overview of the HoloViz tools, philosophy, and approach.
  • Building dashboards using Panel

    • 15 min  Building_Panels: How to make apps and dashboards from Python objects.
    •   5 min  Exercise 1: Using a mix of visualizable types, create a panel and serve it.
    • 10 min  Interlinked Panels: Customizing linkages between widgets and displayable objects.
    •   5 min  Exercise 2: Add widgets to control your dashboard.
    • 10 min  Break
  • The .plot API: a data-centric approach to visualization
    • 30 min  Basic Plotting: Quick introduction to the .plot interface.
    • 10 min  Composing Plots: Overlaying and laying out .hvplot outputs to show relationships.
    • 10 min  Exercise 3: Add some .plot or .hvplot visualizations to your dashboard.
    • 10 min  Break
  • Custom interactivity
    • 25 min  Interlinked Plots: Connecting HoloViews "streams" to customize behavior.
    • 10 min  Exercise 4: Add a linked visualization with HoloViews.
  • Working with large datasets
    • 20 min  Large Data: Using Datashader to pre-render data in Python
    • 10 min  Break
  • Building advanced dashboards
    • 15 min  Advanced Dashboards: Using Panel to create an advanced dashboard with linked plots and streams.
    • 30 min  Exercise 5: Build a new dashboard using everything you've learned so far.

You will find extensive support material on the websites for each package. You may find these links particularly useful during the tutorial: