# 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 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 plots on a grid or simply laying them out consecutively. Here we will introduce the  HoloMap  ,  NdOverlay  ,  NdLayout  and  GridSpace  , which make all this possible.

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