Depends on what you're referring to by "statistical process platforms". Python has been quite popular in the machine learning communities for several years, wit

Depends on what you're referring to by "statistical process platforms". Python has been quite popular in the machine learning communities for several years, with some pure Python libraries (e.g. nltk, scikits.learn), and many wrappers to non-Python libraries (Orange, Shogun, and I'm sure some others). One issue at the moment is that there's an R package for just about anything you would want to do. But the issue with R is its software development tools (which are not very good) and its ability to integrate with other systems. As far as mainstream frequentist statistics in Python, there's still a ways to go. scikits.statsmodels has made a lot of headway the last couple years in implementing standard linear (regression) modeling classes and econometrics tools, with some time series analysis and other things thrown in (I'm actually actively working on adding time series analysis functionality to that library). My general view is that given how friendly a language Python is for scientific programmers and the vibrancy of the community (in terms of new tools being built, etc.), Python could become a permanent fixture for statistical computing. Add on top of that things like integrated distributed computing inside IPython and projects like PyCUDA (which could make high performance statistical computing much more accessible), it's looking very good. But there's a slight chicken-and-egg problem at the moment. I should also point out that R is sort of a frankenstein of half-baked object oriented ideas and remnants of the Splus era. So if you want to design extensible classes for statistical models, it can be quite challenging.