Scientists, engineers, and quantitative data analysts face many challenges nowadays. Data scientists want to be able to perform numerical analysis on large datasets with minimal programming effort. They also want to write readable, efficient, and fast code that is as close as possible to the mathematical language they are used to. A number of accepted solutions are available in the scientific computing world.
The C, C++, and Fortran programming languages have their benefits, but they are not interactive and considered too complex by many. The common commercial alternatives, such as MATLAB, Maple, and Mathematica, provide powerful scripting languages that are even more limited than any general-purpose programming language. Other open source tools similar to MATLAB exist, such as R, GNU Octave, and Scilab. Obviously, they too lack the power of a language such as Python.
Python is a popular general-purpose programming language that is widely used in the scientific community. You can access legacy C, Fortran, or R code easily from Python. It is object-oriented and considered to be of a higher level than C or Fortran. It allows you to write readable and clean code with minimal fuss. However, it lacks an out-of-the-box MATLAB equivalent. That's where NumPy comes in. This book is about NumPy and related Python libraries, such as SciPy and matplotlib.
NumPy (short for numerical Python) is an open source Python library for scientific computing. It lets you work with arrays and matrices in a natural way. The library contains a long list of useful mathematical functions, including some functions for linear algebra, Fourier transformation, and random number generation routines. LAPACK, a linear algebra library, is used by the NumPy linear algebra module if you have it installed on your system. Otherwise, NumPy provides its own implementation. LAPACK is a well-known library, originally written in Fortran, on which MATLAB relies as well. In a way, NumPy replaces some of the functionality of MATLAB and Mathematica, allowing rapid interactive prototyping.
We will not be discussing NumPy from a developing contributor's perspective, but from more of a user's perspective. NumPy is a very active project and has a lot of contributors. Maybe, one day you will be one of them!