Dimensioned Containers

So far we've seen how to wrap data in elements and compose those Elements into Overlays and Layout. In this guide will see how we can use containers to hold Elements and declare parameter spaces to explore a multi-dimensional parameter spaces visually. These containers allow faceting your data by one or more variables and exploring the resulting parameter space with widgets, positioning it on a grid or simply laying consecutively in a layout. Here we will introduce the HoloMap , NdOverlay , NdLayout and GridSpace , which make all this possible.

In [1]:
import numpy as np
import pandas as pd
import holoviews as hv
hv.notebook_extension('bokeh')
%opts Curve (line_width=1)