Python's NumPy, SciPy, Pandas, Matplotlib, SciKits, and StatsModels are very formidable, and have most of the good stuff R has, plus Python itself has a lot more good stuff (from Boost Python to really basic stuff like argparse), minus some horrible stuff that R has (such as the affinity for global functions like `rm()` which seem to be named like Unix tools but which do other things, or the `c()` function which is impossible to Google for, or the abysmal default error reporting, or the use of dots in variable names).

But R has some things going for it. There are some algorithms and tools which exist in R but nowhere in Python (this set seems to both shrink and grow over time as both languages add more stuff). R's overly-terse syntax for some things is annoying for maintainers of R code, but R hackers enjoy it because they tend to be all about banging out piles of stuff quickly.

R also comes with a lot of stuff included that in the Python world would fall under many different umbrellas (see the several names I mentioned at the beginning--those are just some of the basics). Whether it's true or not, R users perceive Python as being relatively balkanized, with that long list of packages just to get started, and with the Python 2 vs. 3 divide which has plagued it for years and will continue for a while still.