Python has more consistent stats, time series and programming syntax (for the packages it does have), and better bayesian inference package (pymc 3 > stan).
Python is also better than R for ad hoc statistical modeling and algorithim development (you can write python code on the order of C fast with numba) , general programming, scraping, natural language processing, agent based modeling etc.
Python is also better for GIS, optimization, symbolic math and larger datasets with blaze and dask and pyspark.
R right now is a bit better for visualization, reporting and exploratory data analysis (I think this will change soon though with Bokeh and blaze) and has many more esoteric stats packages.
With statsmodels, pymc3, pandas and scikitlearn etc you can probably do 98% of everything you need in python without needing to dip into the more esoteric packages of R (with some exceptions). For everything else, you can call R packages with Rpy2. With this you get all the advantages of working in one language (not spread too thin) and the advantages that python offers while leveraging R's wealth of packages.
The latter is a bit more difficult through python, but not as hard as trying to remember the syntax of and gluing together a two language workflow.
That is why I chose python... Also I can write excel addins with python (xlwings) instead of using VBA
Genuine question, why do you find Pymc3 better than stan? Pymc3 is still in alpha, while Stan is stable since many years. They both implements the same algorithm (NUTS), so is it just the nicer syntax or is it anything else?
I've made minimal use of Stan, and not really used Pymc3, but from a quick look, it seems Pymc3 is a bit more integrated than RStan. In RStan, you end up writing Stan code as an alien, wrapping the foreign syntax in quotes, and then shoveling the code as a string into Stan. There isn't really an R grammar for Stan as near as I can tell.
Aside from the written in python making it more natural and extensible and amenable to messing around with the models, pymc 3 can sample directly from discrete parameters and STAN cannot. Pymc3 has more sampling routines.
Python is also better than R for ad hoc statistical modeling and algorithim development (you can write python code on the order of C fast with numba) , general programming, scraping, natural language processing, agent based modeling etc.
Python is also better for GIS, optimization, symbolic math and larger datasets with blaze and dask and pyspark.
R right now is a bit better for visualization, reporting and exploratory data analysis (I think this will change soon though with Bokeh and blaze) and has many more esoteric stats packages.
With statsmodels, pymc3, pandas and scikitlearn etc you can probably do 98% of everything you need in python without needing to dip into the more esoteric packages of R (with some exceptions). For everything else, you can call R packages with Rpy2. With this you get all the advantages of working in one language (not spread too thin) and the advantages that python offers while leveraging R's wealth of packages.
The latter is a bit more difficult through python, but not as hard as trying to remember the syntax of and gluing together a two language workflow.
That is why I chose python... Also I can write excel addins with python (xlwings) instead of using VBA