Since the comment from the new account mentioning it may be modded away, I'll say (as a recent user, not affiliated with them in any way) that Rodeo is a fairly robust integrated analysis environment, a more immediate kind of Jupyter notebook. They recently added a native dataframe viz, too.

http://blog.yhat.com/posts/rodeo-2.0.6-dataframes.html

Rodeo is a good start:

http://blog.yhat.com/posts/rodeo-native.html

I am with you on this. While I feel like I should eventually move fully into python for data, with it being a more multipurpose language. I can never never seem to make the transition because python does not have a rstudio equivalent.

I have tried yhat's rodeo and also yhat's port of ggplot for python but it just isn't there yet in my opinion.

For python you have something similar, though not quite as good with https://github.com/spyder-ide (Spyder).

I use both heavily in my daily work.

This really strongly depends on your industry/focus, and what your end goal is. Some industries are completely dominated (library/community wise) by one or the other. I used to do finance work and strongly preferred python, so I tried hard to use it. (This was also before/right when pandas was out.) I was constantly plagued by needing some fitting routine e.g. for a vector GARCH model, and there was a package in R just sitting there. I was able to get very good mileage out of the RPy2 interface, though.

On the flip side, I've done a fair swath of work in the machine learning arena, and in particular the deep learning topics before it was called 'deep learning', and it was nearly hopeless to use anything except Python or MATLAB (both strongly tied in with C++/CUDA libaries). I think this is still largely the case.

As others have mentioned here, for me the biggest pull away from R was that it's not general purpose. Hard to ship someone R code. Hard to throw a web framework in front of your code. Hard to build a rich desktop GUI on top of it. I know you can do most of those things in R, but last time I dealt with it there was a massive ravine in usability/maturity. I'd also be insincere if I didn't admit that the pervasive R coding style just drove.me.fucking.crazy. That and I found that while there was a mindbogglingly large pile of libraries, documentations was usually very lacking.

/rant

There's s lot more options in Python for data viz other than matplotlib. Pandas has several built ins as well, so in many cases simple plots on existing data frames can be a one liner.