2021-05-02, 16:00–16:45, PyData Track 1
The tutorial will introduce two interactive plots libraries: HoloViews, and panel and show how those can be used to create static html files with interactive graphics
The HoloViz project provides a set of Python libraries for high-level visualization of complex datasets. They are particularly useful for handling big data and multi-dimensional data that is common in machine-learning applications.
HoloViz technologies support multiple graphical engine backends and integrate seamlessly with flexible development and deployment environments like Jupyter notebooks and modern web browsers. The visualization outputs are interactive, with features such as widgets like sliders or selection boxes or hover tools to inspect data, while not requiring any JavaScript, HTML, CSS, or other web-technology expertise.
This tutorial will focus on two HoloViz libraries:
HoloViews: high level interface providing plots (heat maps, histograms, spikes, etc.) in many spatial and temporal combinations, with or without widgets for selecting along dimensions
Panel: simple application and dashboard creation from images, plots, Markdown, LaTeX, and other elements into one HTML page incorporating interactive tabs and widgets.
During the tutorial an interactive presentation will be constructed to show the attendees how to construct their own interactive poster / presentation.
Sample References:
• HoloViz web site: https://holoviz.org
• HoloViz on Github: https://github.com/holoviz/holoviz
• Jacob Barhak, Joshua Schertz, Visualizing Machine Learning of Units of Measure using PyViz, PyData Austin 2019, 6-7 December 2019, Galvanize Austin. Presentation: https://jacob-barhak.github.io/Presentation_PyData_Austin_2019.html Video: https://youtu.be/KS-sRpUvnD0