Eli Bressert - SciPy and NumPy: An Overview for Developers

I would like to thank Meghan Blanchette and Julie Steele, my current and previous editors, for their patience, help, and expertise. This book wouldnt have materialized without their assistance. The tips, warnings, and package tools discussed in the book were much improved thanks to the two book reviewers: Tom Aldcroft and Sarah Kendrew. Colleagues and friends that have helped discuss certain aspects of this book and bolstered my drive to get it done are Leonardo Testi, Nate Bastian, Diederik Kruijssen, Joao Alves, Thomas Robitaille, and Farida Khatchadourian. A big thanks goes to my wife and son, Judith van Raalten and Taj Bressert, for their help and inspiration, and willingness to deal with me being huddled away behind the computer for endless hours.

Python is a powerful programming language when considering portability, flexibility, syntax, style, and extendability. The language was written by Guido van Rossum with clean syntax built in. To define a function or initiate a loop, indentation is used instead of brackets. The result is profound: a Python programmer can look at any given uncommented Python code and quickly understand its inner workings and purpose.

Compiled languages like Fortran and C are natively much faster than Python, but not necessarily so when Python is bound to them. Using packages like Cython enables Python to interface with C code and pass information from the C program to Python and vice versa through memory. This allows Python to be on par with the faster languages when necessary and to use legacy code (e.g., FFTW ). The combination of Python with fast computation has attracted scientists and others in large numbers. Two packages in particular are the powerhouses of scientific Python: NumPy and SciPy. Additionally, these two packages makes integrating legacy code easy.