R or Python - as statistical bench

There is a nice discussion about choosing R or Python as your statistical bench. You could also find much good Python resources.

I just grab some here:
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• NumPy/Scipy You probably know about these already. But let me point out the Cookbookwhere you can read about many statistical facilities already available and the Example Listwhich is a great reference for functions (including data manipulation and other operations). Another handy reference is John Cook's Distributions in Scipy.
• pandas This is a really nice library for working with statistical data -- tabular data, time series,panel data. Includes many builtin functions for data summaries, grouping/aggregation,pivoting. Also has a statistics/econometrics library.
• larry Labeled array that plays nice with NumPy. Provides statistical functions not present in NumPy and good for data manipulation.
• python-statlib A fairly recent effort which combined a number of scattered statistics libraries. Useful for basic and descriptive statistics if you're not using NumPy or pandas.
• statsmodels Statistical modeling: Linear models, GLMs, among others.
• scikits Statistical and scientific computing packages -- notably smoothing, optimization and machine learning.
• PyMC For your Bayesian/MCMC/hierarchical modeling needs. Highly recommended.
• PyMix Mixture models.
  If speed becomes a problem, consider Theano -- used with good success by the deep learningpeople.
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  • matplotlib for beautiful, publication quality graphics.
  • IPython for an enhanced, interactive Python console. Importantly, IPython provides a powerfulframework for interactive, parallel computing in Python.
  • Cython for easily writing C extensions in Python. This package lets you take a chunk ofcomputationally intensive Python code and easily convert it to a C extension. You'll then beable to load the C extension like any other Python module but the code will run very fast sinceit is in C.
  • PyIMSL Studio for a collection of hundreds of mathemaical and statistical algorithms that are thoroughly documented and supported. You can call the exact same algorithms from Python and C, with nearly the same API and you'll get the same results. Full disclosure: I work on this product, but I also use it a lot.
  • xlrd for reading in Excel files easily.
  If you want a more MATLAB-like interactive IDE/console, check out Spyder, or the PyDev plugin for Eclipse.
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  I know there's also rpy, where python can call R functions. This can be useful, but if you're "just"doing statistics then I would use R.
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  The following StackOverflow discussions might be useful
  • R versus Python
  • SciPy versus R
  • Psychology researcher choosing between R, Python, and Matlab
  
  
  Other useful packages specifically for data structures include:

  • pydataframe replicates a data.frame and can be used with rpy. Allows you to use R-likefiltering and operations.
  • pyTables Uses the fast hdf5 data type underneath, been around for ages
  • h5py Also hdf5, but specifically aimed at interoperating with numpy

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  What you are looking for is called Sage: http://www.sagemath.org/

  It is an excellent online interface to a well-built combination of Python tools for mathematics.

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  I like the http://enthought.com/ python distribution. It's commercial, yet free for academicpurposes and, as far as I know, completely open-source. As I'm working with a lot of students,before using enthought it was sometimes troublesome for them to install numpy, scipy, ipythonetc. Enthought provides an installer for Windows, Linux and Mac.

  Two other packages worth mentioning:

  1. ipython (comes already with enthought) great advanced shell. a good intro is on showmedohttp://showmedo.com/videotutorials/series?name=PythonIPythonSeries
  2. nltk - the natural language toolkit http://www.nltk.org/ great package in case you want to do some statistics /machine learning on any corpus