Analytics and visualization often go hand-in-hand. One of the great things about notebooks such as IPython/Jupyter is that they provide a single interface to numerous data analysis technologies that often can be used together. So, using Brunel within notebooks is a very natural fit. For example, I can use a wide variety of python libraries to cleanse, shape and analyze data–and then use Brunel to visualize those results.
Additionally, coming up with a good visualization is a highly iterative process: Try something, look at the results and refine until done. So, again, the notebook metaphor of having live code execution near the results is extremely convenient. Lastly, since notebook cells containing output can themselves be interactive, direct manipulation techniques such as brushing/linking, filtering and selection are also available.
To try this out, we have provided an integration of Brunel for Python 3 that runs in IPython/Jupyter notebooks. Details on how to install and get started are on the PyPI site. The video above gives a very small taste of the kinds of things that are possible.