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Scientific Applications
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Context
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Python is frequently used for high-performance scientific applications. Python
is widely used in academia and scientific projects because it is easy to write,
and it performs really well.

Due to its high performance nature, scientific computing in python often refers
to external libraries, typically written in faster languages (like C, or FORTRAN
for matrix operations). The main libraries used are `NumPy`_ and
`SciPy`_.

Libraries
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NumPy
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`NumPy <http://numpy.scipy.org/>`_ is a low level library written in C (and
FORTRAN) for high level mathematical functions. NumPy cleverly overcomes the
problem of running slower algorithms on Python by using multidimensional arrays
and functions that operate on arrays. Any algorithm can then be expressed as a
function on arrays, allowing the algorithms to be run quickly.


NumPy is part of the SciPy project, and is released as a separate library so
people who only need the basic requirements can just use NumPy.

NumPy is compatible with Python versions 2.4 through to 2.7.2 and 3.1+.

SciPy
-----
`SciPy <http://scipy.org/>`_ is a library that uses Numpy for more mathematical
function. SciPy uses NumPy arrays as its basic data structure. SciPy comes with
modules for various commonly used tasks in scientific programing like linear
algebra, integration (calculus), ordinary differential equation solvers and
signal processing.

Enthought
---------

Installing NumPy and SciPy can be a daunting task. Which is why the
`Enthought Python distribution <http://enthought.com/>`_ was created. With
Enthought, scientific python has never been easier (one click to install about
100 scientific python packages). User beware: Enthought is not free.

Matplotlib
----------

`matplotlib <http://matplotlib.sourceforge.net/>`_ is a flexible plotting
library for creating interactive 2D and 3D plots that can also be saved as
manuscript-quality figures.  The API in many ways reflects that of `MATLAB <http://www.mathworks.com/products/matlab/>`_,
easing transition of MATLAB users to Python.  Many examples, along with the
source code to re-create them, can be browsed at the `matplotlib gallery <http://matplotlib.sourceforge.net/gallery.html>`_.

Resources
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Many people who do scientific computing are on Windows. And yet many of the
scientific computing packages are notoriously difficult to build and install.
`Christoph Gohlke <http://www.lfd.uci.edu/~gohlke/pythonlibs/>`_ however, has
compiled a list of Windows binaries for many useful Python packages. The list
of packages has grown from a mainly scientific python resource to a more
general list. It might be a good idea to check it out if you're on Windows.