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.

1. http://statsmodels.sourceforge.net/

2. http://pandas.sourceforge.net/

3. http://rpy.sourceforge.net/rpy2.html

4. http://blog.wesmckinney.com/

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. :)

I'm also partial to R, but Python does have stats support via NumPy and (especially) SciPy: http://docs.scipy.org/doc/scipy/reference/stats.html

When I was at the Broad, at least, there was heavy use of both R and Python among the statistically savvy.

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#.

Here's a directory of stats/ML-related Python libraries: http://aliquote.org/memos/2011/02/07/python-for-statistical-...

Raw level: numpy

More high-level statistical functionality: scipy.stats (built on top of numpy)

Even more high-level: scikit.statsmodels (built on top of scipy)