Thanks for all the interest in my talk - I will fix up the issues in this repo so you can run it locally if you want tomorrow.

https://us.pycon.org/2015/schedule/presentation/369/

Interactive data visualization libraries are mostly a JavaScript stronghold. The new Python library, Bokeh, provides a simple, clean way to make more shiny things. Although it comes from the data science community, it has a lot to offer web developers. For a visualization you might have built in d3.js, I'll show how to build it in Bokeh, how to test it, and how to hook it into your web app.

As a web developer, I find myself being asked to make increasing numbers of data visualizations, interactive infographics, and more. d3.js is great, as are many other js toolkits that are out there. But if I can write more Python and less JavaScript... well, that makes me happy!

Bokeh is a new Python library for interactive visualization. Its origins are in the data science community, but it has a lot to offer web developers.

In this talk I'll discuss using Bokeh with a web framework (in this case, Django):

• I will walk through building an interactive visualizations in Bokeh to display your data
• How to unit test your visualization
• How to display your plot on the web and within your templates, including a number of pitfalls I have encountered.

I will not be covering real-time or high-volume analytics, or any statistical processing. This is an introduction to Bokeh's core, focused on the needs of an average web developer.