SciPy and NumPy

Chapter 1. Introduction

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.

1.1 Why SciPy and NumPy?

The basic operations used in scientific programming include arrays, matrices, integration, differential equation solvers, statistics, and much more. Python, by default, does not have any of these functionalities built in, except for some basic mathematical operations that can only deal with a variable and not an array or matrix. NumPy and SciPy are two powerful Python packages, however, that ...