HoloViz Tutorial#

Welcome to the HoloViz tutorial! These notebooks 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 thousands or 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, Lumen, Param, and Colorcet.

The HoloViz tools have been carefully designed to work together with each other and with the SciPy/PyData 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. Other Python viz tools not included in HoloViz are reviewed at PyViz.org.

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.

    • 10 min  Overview: Overview of the HoloViz tools, philosophy, and approach.

  • The .plot API: a data-centric approach to visualization

    • 30 min  Plotting: Quick introduction to the .plot interface.

    • 20 min  Composing Plots: Overlaying and laying out .hvplot outputs to show relationships.

    • 15 min     Exercise 1: Building some .hvplot visualizations and composing them together

    • 10 min  Break

  • Building interactivity

    • 20 min  Interlinked_Plots: Creating linkages between plots

    • 15 min    Exercise 2: Linking the plots from the first exercise

    • 20 min  Interactive_Pipelines: Creating interactive pipelines that display data, controlled by widgets

    • 15 min     Exercise 3: Building your own pipelines and visualizations controlled by widgets.

    • 10 min  Break

  • Building dashboards using Panel

    • 30 min  Dashboards: How to make apps and dashboards from Python objects.

    • 30 min     Exercise 4: Using a mix of visualizable types, create a panel and serve it.

    • 10 min  Break

  • Advanced dashboards (optional)

This web page was generated from a Jupyter notebook and not all interactivity will work on this website. Right click to download and run locally for full Python-backed interactivity.