Update, March 2014: there are some major changes and refactorings in mpld3 version 0.1. Because of this, some of the code below will not work with the current release: please see the mpld3 documentation for more information.

I've spent a lot of time recently attempting to push the boundaries of tools for interactive data exploration within the IPython notebook. I have worked on animations, including an HTML5 embedding and a Javascript Viewer. I have worked on javascript/python kernel interaction and static javascript widgets. But I would say that the holy grail of interactive data visualization in the IPython notebook is, as I've mentioned previously, a truly interactive in-browser matplotlib display.

There are many people pushing in this direction in the Python world. Bokeh and Plotly are new visualization packages which have built Python APIs from scratch. The demos are beautiful and impressive, and the APIs are clean and intuitive. But, because matplotlib is so well-established in the Python world, it would be nice to be able to continue using it even in the age of browser-based visualization. To this end, some of the matplotlib core devs have been working on a WebGL viewer for matplotlib figures. I've seen a working demo, and it's very cool, but last I heard it still has a long way to go.

I've been wondering for a while whether it might be possible to create a solution using D3.js. D3 (short for data-driven documents) is a framework which facilitates the easy creation and manipulation of groups of HTML objects. Combined with the native SVG support of modern web browsers, it provides an extremely powerful and flexible low-level interface to creating interactive graphics on the web. I've long wondered what it would take to write a D3 backend or frontend for matplotlib, but I'd never experimented with the idea. It was a couple weeks ago at Seattle Beer && Code meetup that I chatted with some expert Javascript hackers who pointed out where I might start.