I know the title sounds a little extreme but I wonder whether R is phased out by a lot of quant desks at sell side banks as well as hedge funds in favor of Python. I get the impression that with improvements in Pandas, Numpy and other Python packages functionality in Python is drastically improving in order to meaningfully mine data and model time series. I have also seen quite impressive implementations through Python to parallelize code and fan out computations to several servers/machines. I know some packages in R are capable of that too but I just sense that the current momentum favors Python.
I need to make a decision regarding architecture of a subset of my modeling framework myself and need some input what the current sentiment is by other quants.
I also have to admit that my initial reservations regarding performance via Python are mostly outdated because some of the packages make heavy use of C implementations under the hood and I have seen implementations that clearly outperform even efficiently written, compiled OOP language code.
Can you please comment on what you are using? I am not asking for opinions whether you think one is better or worse for below tasks but specifically why you use R or Python and whether you even place them in the same category to accomplish, among others, the following tasks:
- acquire, store, maintain, read, clean time series
- perform basic statistics on time series, advanced statistical models such as multivariate regression analyses,...
- performing mathematical computations (fourier transforms, PDE solver, PCA, ...)
- visualization of data (static and dynamic)
- pricing derivatives (application of pricing models such as interest rate models)
- interconnectivity (with Excel, servers, UI, ...)
- (Added Jan 2016): Ability to design, implement, and train deep learning networks.
EDIT I thought the following link might add more value though its slightly dated  (for some obscure reason that discussion was also closed...): https://softwareengineering.stackexchange.com/questions/181342/r-vs-python-for-data-analysis
You can also search for several posts on the r-bloggers website that address computational efficiency between R and Python packages. As was addressed in some of the answers, one aspect is data pruning, the preparation and setup of input data. Another part of the equation is the computational efficiency when actually performing statistical and mathematical computations.
Update (Jan 2016)
I wanted to provide an update to this question now that AI/Deep Learning networks are very actively pursued at banks and hedge funds. I have spent a good amount of time on delving into deep learning and performed experiments and worked with libraries such as Theano, Torch, and Caffe. What stood out from my own work and conversations with others was that a lot of those libraries are used via Python and that most of the researchers in this space do not use R in this particular field. Now, this still constitutes a small part of quant work being performed in financial services but I still wanted to point it out as it directly touches on the question I asked. I added this aspect of quant research to reflect current trends.