# 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, using either Bokeh or Plotly. HoloViews encapsulates the Datashader pipeline in a way that lets you combine interactive datashaded plots easily with other plots without having to write explicit callbacks or event-processing code.

In this notebook, we will first look at the HoloViews API, then at Datashader’s new native Matplotlib support.

import holoviews as hv