The 'Getting Started' guide has up until this point demonstrated how HoloViews objects can wrap your data and be given a rich, useful representation. All of the visualizations assumed that the data was already available in memory so that it could be used to construct the appropriate object, and all of the resulting visualizations can be viewed in static HTML pages, no longer requiring Python when users interact with them.
In many important scenarios, the assumption that the data is immediately available in memory does not hold. The data of interest may exist on some remote server, making it unavailable locally until it is fetched. In other situations, the data may exist on the local disk, but be too large to fit into memory. Perhaps the data doesn't even exist yet: it may be the result of some computation yet to be performed or the outcome of some live process with the corresponding measurement not yet made.
All these scenarios are examples of live data that can be made available to HoloViews using the appropriate Python process. In this section, we will see how HoloViews allows you to build visualizations that can update dynamically to newly available data and that can respond to live user interaction.
A computational process¶
Let us start by importing NumPy and HoloViews and loading the Bokeh extension:
import holoviews as hv from holoviews import opts import numpy as np hv.extension('bokeh')