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.

Embedding Datashader with HoloViews#

HoloViews (1.7 and later) is a high-level data analysis and visualization library that makes it simple to generate interactive Datashader-based plots. Here’s an illustration of how this all fits together when using HoloViews+Bokeh:


HoloViews offers a data-centered approach for analysis, where the same tool can be used with small data (anything that fits in a web browser’s memory, which can be visualized with Bokeh directly), and large data (which is first sent through Datashader to make it tractable) and with several different plotting frontends. A developer willing to do more programming can do all the same things separately, using Bokeh, Matplotlib, and Datashader’s APIs directly, but with HoloViews it is much simpler to explore and analyze data. Of course, the previous notebook showed that you can also use datashader without either any plotting library at all (the light gray pathways above), but then you wouldn’t have interactivity, axes, and so on.

Most of this notebook will focus on HoloViews+Bokeh to support full interactive plots in web browsers, but HoloViews+Plotly works similarly for interactive plots, and we will also briefly illustrate the non-interactive HoloViews+Matplotlib approach, followed by a non-HoloViews Matplotlib approach at the end. Let’s start by importing some parts of HoloViews and setting some defaults:

import holoviews as hv
import holoviews.operation.datashader as hd
hd.shade.cmap=["lightblue", "darkblue"]
hv.extension("bokeh", "matplotlib")