The following list of Python resources was taken from the SciPy website, where additional resources for scientifc computing can be found.

General Python Resources

  • Python.org: official website for the Python language. It includes links to the current documentation and tutorials, downloads for many platforms, the Python mailing lists and newsgroups, and much more
  • Python Package Index (PyPI): the official Python.org package index (the Python standard distribution system, distutils, includes support for automatically registering packages with PyPI).
  • PyCode: Another collection of python packages and resources.
  • The Python Cookbook: a community-driven collection of code snippets for many tasks.
  • The O'Reilly Python Devcenter: O'Reilly is widely regarded as one of the best computing book publishers, and they maintain a resource center devoted to Python. This includes both their publications and articles on Python-related topics.
  • The Python Learning Foundation: a large collection of Python links.
  • Enthought Python Distribution: Python distribution that includes a lot of additional scientific modules
  • Python(x,y): Python distribution that includes a lot of additional scientific modules

Programming tools

  • Eclipse IDE (integrated development environment)

Some generic Python/programming tutorials

  • The standard Python docs : this contains the official documentation and tutorials which ship with the language.
  • Dive Into Python: an online (print version available) complete book on the Python language, aimed at experienced programmers, covering topics from introductory to fairly advanced.
  • Learning to Program: beginner's tutorial.
  • How to Think Like a Computer Scientist: another beginner's tutorial.
  • Many more: an external collection with over 100 tutorials.

And some more specifically geared towards scientific computing

  • The SciPy documentation page has a number of important links on using SciPy, especially a (slightly outdated, but still useful) PDF tutorial for the SciPy library.
  • The user guide for the new NumPy system. Numeric and Numarray have been unified into the new NumPy environment. This document covers all the details of the new system, and was written by its lead developer.
  • A tutorial focused on interactive data analysis for astronomy, but of generic utility to most scientific users. Developed at the STSCI, available for free download including all data files necessary to run the examples.
  • Konrad Hinsen's Python page: contains a number of introductions and tutorials to Python, geared towards the needs of scientists.
  • Jacek Generowicz's Python Courses.
  • Python Scripting for Computational Science: not free, this is a Springer book.
  • Python/Matlab/Octave/Scilab/R/Gnuplot/IDL/Axiom cross-reference by Vidar Gundersen.
  • Software Carpentry is an open source course on basic software development skills for people with backgrounds in science, engineering, and medicine.
  • A tutorial for SciPy by Dave Kuhlman (dkuhlman@rexx.com).
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