Use both and you'll see. Python can do modeling, can do exploratory work and is great in production. R is brilliant at exploratory work and modeling. Having a core in functional programming helps too.
It's like every line I wrote there was some catch or trick I had to know just to get it to work. Pretty frustrating.
I've seriously got something like a 100 textbooks on R on my system, intro to stats with R, machine learning with R, questionnaire analysis with R, bayesian stats with R... There are Coursera classes, the amazing r-bloggers.com, etc. For someone who is simultaneously learning statistics and a tool, this is invaluable.
For Python, there's still very little. There's Wes' book, which is mostly about pandas and a lot of finance/time series stuff... I haven't seen a single "intro to stats in social sciences with Python"... There must be one book out there that is open source, which could just be rewritten with Python examples? (The most useful thing I've seen is people reworking examples from Machine Learning for Hackers, or one of the Coursera R courses, in Python).
I agree it's frustrating. I've been working with R on and off for more than five years and almost exclusively for 1-2 years, and I still need google every ten minutes to remember some esoteric command that is perfect for the problem at hand. In my experience this is still quicker and less error-prone than python, which often requires you to effectively roll out your own solutions for small data processing needs.
I still struggle sometimes to write nice looking code in R, but I've known people who are pretty good at managing it. If you have to inherit an R program from someone else though, it's almost guaranteed to be a nightmare to comprehend.