vincent

I recently built a simple site using Vincent, which is a Python visualization library using D3 & Vega. Generating the graph was only a dozen lines of code or so for me. Sometimes you don't need the full power of the underlying library, you just want to get something up and running quickly for a prototype. There's always a tradeoff of control when going to a higher abstraction layer. https://github.com/wrobstory/vincent

There is also Seaborn: http://stanford.edu/~mwaskom/software/seaborn/index.html and Vincent: https://github.com/wrobstory/vincent Also the code examples given on AstroML to work well for figuring out how to make publication quality figures in Matplotlib: http://www.astroml.org/book_figures/

There's also Vincent[1], which has some mapping capabilities and is built on top of Vega (a "visualization grammar" for d3js). [1]: https://github.com/wrobstory/vincent

Slightly off topic but the matplotlib interface always annoyed the hell out of me. The pyplot wrapper meant that there are effectively two ways to do any visualisation,which breaks python "zen" pretty badly ("There should be one-- and preferably only one --obvious way to do it"). Visualisation is one area where R with ggplot really eats pythons lunch.If anybody has the inclination I would recommend looking into https://github.com/wrobstory/vincent and https://github.com/ContinuumIO/Bokeh Vincent is a wrapper for a wrapper(vega!) for d3 and works nicely even though its only on version 0.2. Bokeh seems less "user friendly" and finished but it has good funding and Peter Wang who really understands scientific computing behind it so I would bet on it for the long run.

python has a ton of in browser plotting mechanisms (including matplotlib inside iPython notebook files), and you can always use vincent which is a wrapper around vega to create d3 plots (svg html elements, which can be styled with CSS). Here is the repo/tutorial for plotting with vincent https://github.com/wrobstory/vincent [Note that vincent works great if you are using pandas for your data analysis.]