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- 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:
- 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.
- larry al functions for labeled arrays not present in NumPy.
- python-statlib Combined scattered statistics libraries 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 Bayesian, MCMC and hierarchical modeling.
- PyMix Mixture models.
- Theano For high performance computing and deep learning.