If you're using Python, what are you using for your stats libraries?
R is, generally, the go to language for stats work I've found. It's certainly used a lot in the financial world for statistical modelling, and all the libraries in it are well tested which is a big plus in my book.
Scikits.statsmodels [1] is the main statistical and econometric library for Python. It is usually used with pandas [2], which provides nice interfaces to your data, particularly for time-series. You can also call R functions with the rpy2 bridge [3]; pandas provides a higher-level API for using rpy2, though last I checked it's not fully fleshed out yet.
Wes McKinney, the developer of pandas, writes a blog [4] that provides a good look at data analysis in Python, particularly if you're in finance.
Check out enthought http://www.enthought.com for "everything and the kitchen sink". Numpy and scipy will generally have you covered, and often wrap low-level libraries written in e.g. FORTRAN and LAPACK. YMMV but I've used python exclusively in scientific computing as well as "web startup" data analysis environments. I love being able to write my data analysis and web server code in the same language. :)
Interesting - I'm not well versed on the Python stats ecosystem. It's a side effect of working in finance where every place I've worked already has large well tested libraries in C/C++/C#.
R is, generally, the go to language for stats work I've found. It's certainly used a lot in the financial world for statistical modelling, and all the libraries in it are well tested which is a big plus in my book.