Bokeh Tutorial

A3. High-Level Charting with Holoviews

Bokeh is designed to make it possible to construct rich, deeply interactive browser-based visualizations from Python source code. It has a syntax more compact and natural than older libraries like Matplotlib, particularly when using the Charts API, but it still requires a good bit of code to do relatively common data-science tasks like complex multi-figure layouts, animations, and widgets for parameter space exploration.

To make it feasible to generate complex interactive visualizations "on the fly" in Jupyter notebooks while exploring data, we have created the new HoloViews library built on top of Bokeh.

HoloViews allows you to annotate your data with a small amount of metadata that makes it instantly visualizable, usually without writing any plotting code. HoloViews makes it practical to explore datasets and visualize them from every angle interactively, wrapping up Bokeh code for common tasks into a set of configurable and composable components. HoloViews installs separately from Bokeh, e.g. using conda install holoviews, and also works with matplotlib.

In [1]:
import holoviews as hv
import numpy as np
hv.notebook_extension('bokeh')