A1 Exploration with Containers

In the first two sections of this tutorial we discovered how to declare static HoloViews elements and compose them one by one into composite objects, allowing us to quickly visualize data as we explore it. However, many datasets contain numerous additional dimensions of data, such as the same measurement repeated across a large number of different settings or parameter values. To address these common situations, HoloViews provides containers that allow you to explore extra dimensions of your data using widgets, as animations, or by "faceting" it (splitting it into "small multiples" ) in various ways.

To begin with we will discover how we can quickly explore the parameters of a function by having it return an element and then evaluating the function over the parameter space.

In [1]:
import numpy as np
import holoviews as hv
%opts Curve Area [width=600]