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Python resources for statistical computing

Statistical computing in Python
A collection of resources to assist statistical computing with Python, with a special emphasis on astrostatistics, compiled by Tom Loredo at Cornell.
NumPy/Scipy
Important libraries for scientific and numerical data analysis. See the Cookbook and the Example List for reference, as well as John Cook's Distributions in Scipy.
Statistical computing in Python
A collection of resources to assist statistical computing with Python, with a special emphasis on astrostatistics, compiled by Tom Loredo at Cornell.  These include:
  1. pandas Library for working with tabular data, time series, panel data with many built-in functions for data summaries, grouping/aggregation, pivoting. Also with a statistics/econometrics library.
  2. larry  al functions for labeled arrays not present in NumPy.
  3. python-statlib  Combined scattered statistics libraries for basic and descriptive statistics if you're not using NumPy or pandas.
  4. statsmodels Statistical modeling: Linear models, GLMs, among others.
  5. scikits Statistical and scientific computing packages -- notably smoothing, optimization and machine learning.
  6. PyMC  For Bayesian, MCMC and hierarchical modeling. 
  7. PyMix Mixture models.
  8. Theano For high performance computing and deep learning.