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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 i

by elemeno 15y ago

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.

carbocation 15y ago
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 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.
- elemeno 15y ago
  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#.
gammarator 15y ago
Here's a directory of stats/ML-related Python libraries: http://aliquote.org/memos/2011/02/07/python-for-statistical-computing http://aliquote.org/memos/2011/02/07/python-for-statistical-...
pash 15y ago
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/ http://statsmodels.sourceforge.net/ 2. http://pandas.sourceforge.net/ http://pandas.sourceforge.net/ 3. http://rpy.sourceforge.net/rpy2.html http://rpy.sourceforge.net/rpy2.html 4. http://blog.wesmckinney.com/ http://blog.wesmckinney.com/
rd108 15y ago
Check out enthought http://www.enthought.com 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. :)
Radim 15y ago
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)

by elemeno 15y ago

carbocation 15y ago

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 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.

elemeno 15y ago
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#.

gammarator 15y ago

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

pash 15y ago

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/ http://statsmodels.sourceforge.net/ 2. http://pandas.sourceforge.net/ http://pandas.sourceforge.net/ 3. http://rpy.sourceforge.net/rpy2.html http://rpy.sourceforge.net/rpy2.html 4. http://blog.wesmckinney.com/ http://blog.wesmckinney.com/

rd108 15y ago

Check out enthought http://www.enthought.com 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. :)

Radim 15y ago

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)