Seaborn: a high-level Python interface for drawing statistical graphics

Seaborn is fantastic--it produces high-quality plots with a minimum of code. The aesthetics are great and easily tuned.

Check out the gallery for examples: https://web.stanford.edu/~mwaskom/software/seaborn/examples/...

tl;dr:

I'm new to Seaborn and matplotlib in general, but Seaborn is a wrapper on top of matplotlib, and from what I can tell, was borne partly out of frustration with how hard it is to get matplotlib graphics to look decent out-of-the-box. Which makes it, in one sense, kind of like what ggplot2 was to R's standard plotting tools.

However, Seaborn has a more object-oriented API, among other things:

http://stanford.edu/~mwaskom/software/seaborn/introduction.h...

> Seaborn’s goals are similar to those of R’s ggplot, but it takes a different approach with an imperative and object-oriented style that tries to make it straightforward to construct sophisticated plots. If matplotlib “tries to make easy things easy and hard things possible”, seaborn aims to make a well-defined set of hard things easy too.

There already is an attempt to port ggplot over to Python, and its authors' opinion is that its API should look like R's ggplot2, which means the syntax is not Pythonic: http://ggplot.yhathq.com/

While we're talking about stats and Python could I convince someone here to implement a fast medcouple for statsmodels? I can't do it myself because I read R's GPL'ed code in order to understand the algorithm. Using my understanding, I wrote the following high-level description of it:

https://en.wikipedia.org/wiki/Medcouple

This should be taken as the design spec of a clean-room reverse engineering, so that we can have a free, fast and non-copylefted implementation. It's not that I have a problem with copyleft (in fact, I prefer it), but I really want statsmodels to fix their implementation, and they're GPL-phobic.

Since Seaborn has boxplots, implementing an adjusted boxplot seems relevant.

edit: Oh, one more thing. I'd love any feedback on how to improve the "design spec", in case I wasn't able to make it clear enough.

Also matplotlib recently added stylesheets

http://matplotlib.org/users/style_sheets.html

which gets you ~75% of the way there to ggplot style plots. I've found a number of edge cases where the plots don't turn out right when using the ggplot sheet. BUT the important part is you can set your own default plotting style with a single line of code and keep everything nice and pythonic (well kind of pythonic since you're using matplotlib...)

There is also http://ggplot.yhathq.com/ which is buggy for complex graphs but rapidly getting better - indispensable for me.