03 Customizing Visual Appearance

The previous tutorial focused on specifying elements and simple collections of them. This one explains how the visual appearance can be adjusted to bring out the most salient aspects of your data, or just to make the style match the overall theme of your document. We'll use data in Pandas, and HoloViews, Bokeh, and Matplotlib to display the results:

HoloViews explicitly makes the distinction between data and plotting options , which allows annotating the data with semantic metadata before deciding how to visualize the data. It also allows rendering the same object using different plotting libraries, such as Bokeh or Matplotlib.


In the annotating your data section , hv.extension('bokeh') was used at the start to load and activate the bokeh plotting extension. In this notebook, we will also briefly use matplotlib that will be loaded, but not yet activated, by listing it second:

In [1]:
import pandas as pd
import holoviews as hv
hv.extension('bokeh', 'matplotlib')