seaborn

My biggest problem with ggplot is that it's slow, unbearably slow. It also enforces all data to be realized into a single dataframe, which is true only for small (as is: fits in memory) datasets. Very often, to produce specialized plots, I have to send data to the canvas in chunks by performing pre-processing myself. ggplot really doesn't work in this scenario. Combined with the general slowness, it forces me to use alternatives quite frequently. It's a bummer, really, because I'd like my plots to have a consistent visual style, and doing that across different plotting packages is an issue. I very often resort to gnuplot when it comes to huge datasets and/or incremental plotting. The same is true also in python (matplotlib is also very slow, independently of the backend). But at least, if you use seaborn (https://github.com/mwaskom/seabor

For stats plotting in python: https://github.com/mwaskom/seaborn https://github.com/yhat/ggplot For stats plotting and web apps in python: https://github.com/bokeh/bokeh For calling r libraries in python: https://pypi.python.org/pypi/rpy2 For out of core datasets in python: https://github.com/blaze/dask https://github.com/blaze/blaze

MPL's documentation is a catastrophe, and the API design is indeed abysmal. The plots, however, are only as ugly as you like them to be. Here's a great auxiliary package with much more sensible defaults: https://github.com/mwaskom/seaborn