# Interactivity¶

The previous notebook showed all the steps required to get a Datashader rendering of your dataset, yielding raster images displayed using Jupyter's "rich display" support. However, these bare images do not show the data ranges or axis labels, making them difficult to interpret. Moreover, they are only static images, and datasets often need to be explored at multiple scales, which is much easier to do in an interactive program.

To get axes and interactivity, the images generated by Datashader need to be embedded into a plot using an external library like Matplotlib or Bokeh. As we illustrate below, the most convenient way to make Datashader plots using these libraries is via the HoloViews high-level data-science API. Plotly can also be used with Datashader, and native Datashader support for Matplotlib has been sketched but is not yet released.

In this notebook, we will first look at Datashader's native Bokeh support, because it uses the same API introduced in the previous examples. We'll start with the same example from the previous notebook:

import holoviews as hv