Numeric Extensions

Finally, although Python core numeric types offer plenty of power for most applications, there is a large library of third-party open source extensions available to address more focused needs. Because numeric programming is a popular domain for Python, you’ll find a wealth of advanced tools.

For example, if you need to do serious number crunching, an optional extension for Python called NumPy (Numeric Python) provides advanced numeric programming tools, such as a matrix data type, vector processing, and sophisticated computation libraries. Hardcore scientific programming groups at places like Los Alamos and NASA use Python with NumPy to implement the sorts of tasks they previously coded in C++, FORTRAN, or Matlab. The combination of Python and NumPy is often compared to a free, more flexible version of Matlab—you get NumPy’s performance, plus the Python language and its libraries.

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Because it’s so advanced, we won’t talk further about NumPy in this book. You can find additional support for advanced numeric programming in Python, including graphics and plotting tools, statistics libraries, and the popular SciPy package at Python’s PyPI site, or by searching the Web. Also note that NumPy is currently an optional extension; it doesn’t come with Python and must be installed separately.