Difference between revisions of "Python Installation and Basic Programming"

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(→‎Python: added Fernando Perez's nice list of python resources, and the astronomy tutorial)
(→‎Links: removed unnecessary pipe in link)
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** [http://sites.google.com/site/pythonbootcamp/links Useful links] to Python resources
 
** [http://sites.google.com/site/pythonbootcamp/links Useful links] to Python resources
 
* [http://fperez.org/py4science/starter_kit.html Python for Scientific Computing] has a list of resources on various topics relevant to a scientist getting started with Python
 
* [http://fperez.org/py4science/starter_kit.html Python for Scientific Computing] has a list of resources on various topics relevant to a scientist getting started with Python
* [http://www.scipy.org/Additional_Documentation/Astronomy_Tutorial?action=show |Scipy astronomy tutorial] covers reading in and analyzing data in scipy, mostly focusing on FITS files, but with some other useful information for those with data in other formats
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* [http://www.scipy.org/Additional_Documentation/Astronomy_Tutorial?action=show Scipy astronomy tutorial] covers reading in and analyzing data in scipy, mostly focusing on FITS files, but with some other useful information for those with data in other formats
 
* A [http://code.google.com/edu/languages/google-python-class/index.html Google Class on Python]
 
* A [http://code.google.com/edu/languages/google-python-class/index.html Google Class on Python]
 
* The [http://docs.python.org/tutorial/ Python Tutorial]
 
* The [http://docs.python.org/tutorial/ Python Tutorial]

Revision as of 13:45, 22 September 2011

Here we will assemble resources for learning Python, and for getting it and other programming-related software installed on your computer.

For a scientific programmer in Python, the absolute basics you need to have installed are:

  • Python 2.X (note that 3.X exists and is maturing, but a lot of scientific code and packages are not yet ported)
  • NumPy: a package for fast numerical array processing
  • Matplotlib/Pylab: a package for generating publication-quality plots
  • GIT: a revision-control program for keeping tabs on the changes you make to your code. Not just for python.

Python

Topical Videos

Links

GIT

Please see the Revision Control page,