88% python + 10% calling R from python + 2% porting R code to python.

edit: that being said, ggplot is still better than anything on the python side of things.

I wish I had a good answer to that, but I don't. Python is taking a good shot at it. But it doesn't match R by any means.

seaborn, bokeh, pandas' built in methods, and plotly fill in the ggplot gap for me.

So perhaps we ought be working on building solutions that bridge the Python-R gap? Because as someone who has effectively left Python, I'd love to learn from solutions Python users find effective. After all, it's not like one language is the sole proprietor of useful approaches (;

I'm not saying you're wrong, but this sounds exactly like the arguments made supporting Perl, which is an interesting comparison.

It is an interesting comparison for sure, but things are different for two reasons:

1) Software and related services will grow immensely over the next decade because of the "cloud". Some of the growth will be same old, same old application logic software, but most of it will be data analysis software.

2) The language of choice for data analysis software is and will be R due to the biggest software companies (both #1 and #2, among many others) standing firmly behind it along with universities incorporating it in its curriculum, ditching SPSS and SAS.

Well, both are pretty great languages (not without issues, but what is?), and there are a lot of similarities -- "more than one way to do it" of course, and CRAN and CPAN have much in common (I do wish CRAN took a few lessons from CPAN though).

If you have the time, could you please shoot me an email with the lessons you think CRAN could learn from CPAN? Thanks!

have we talked about CPAN testers?