# Python Language Resources

# Dr. Phillip M. Feldman

Python is a high-level, general-purpose, open-source, interpreted programming
language whose development has been a collaborative effort involving a large
community. Although sometimes described as a *scripting* language,
Python is also well-suited for large-scale applications. It's built-in support
for numerical/scientific computing is rather weak, but this was rectified by the
addition of the NumPy and SciPy packages. In the following, when I refer to
Python, I'm implicitly including such widely-used Python packages as NumPy,
SciPy, and matplotlib.

"NumPy [initially released in 2005] is an extension to the Python programming
language, adding support for large, multi-dimensional arrays and matrices, along
with a large library of high-level mathematical functions to operate on these
arrays. ... Because Python is currently implemented as an interpreter,
mathematical algorithms written in it often run slower than compiled
equivalents. NumPy seeks to address this problem for numerical algorithms by
providing multidimensional arrays and functions and operators that operate
efficiently on arrays. Thus any algorithm that can be expressed primarily as
operations on arrays and matrices can run almost as quickly as the equivalent C
code." [from
http://en.wikipedia.org/wiki/NumPy]

Python and Matlab both use dynamic typing and are higher-level than
programming languages such as C/C++ and Java, in the sense that the manipulation
of objects such as matrices and arrays requires far less code in Python and
Matlab. Python's support for numerical/scientific computing is roughly
comparable to that of Matlab, and there is in fact really no third contender in
this category. (Mathematica is an excellent tool for symbolic math, but is
severely deficient as a programming language).

## News and Miscellany

4 Oct., 2015: I've recently started digging into the *IPython Interactive
Computing and Visualization Cookbook* by Cyrille Rossant. (The snippets of
sample content at http://ipython-books.github.io/cookbook are hard to understand
without the context). Although I'm in the main opposed to computer programming
cookbooks, this is far better than most books in that genre. The author begins
each chapter with a brief introduction that combines background and motivation.
The examples are well designed and explained.

*Last update: 16 May, 2016*