Python for Data Analysis: The Landscape of Tutorials by Abhijit Dasgupta.
From the post:
Python has been one of the premier general scripting languages, and a major web development language. Numerical and data analysis and scientific programming developed through the packages Numpy and Scipy, which, along with the visualization package Matplotlib formed the basis for an open-source alternative to Matlab. Numpy provided array objects, cross-language integration, linear algebra and other functionalities. Scipy adds to this and provides optimization, linear algebra, optimization, statistics and basic image analysis capabilities. Matplotlib provides sophisticated 2-D and basic 3-D graphics capabilities with Matlab-like syntax.
Further recent development has resulted in a rather complete stack for data manipulation and analysis, that includes Sympy for symbolic mathematics, pandas for data structures and analysis, and IPython as an enhanced console and HTML notebook that also facilitates parallel computation.
An even richer data analysis ecosystem is quickly evolving in Python, led by Enthought and Continuum Analytics and several other independent and associated efforts. We have described this ecosystem here. [“ecosystem” and “here” are two distinct links.]
A very impressive listing of tutorials on Python packages for data analysis.