# What this book covers

Chapter 1, *Introduction to SciPy*, shows the benefits of using the combination of Python, NumPy, SciPy, and matplotlib as a programming environment for scientific purposes. You will learn how to install, test, and explore the environments, use them for quick computations, and figure out a few good ways to search for help. A brief introduction on how to open the companion IPython Notebooks that comes with this book is also presented.

Chapter 2, *Working with the NumPy Array As a First Step to SciPy*, explores in depth the creation and basic manipulation of the object array used by SciPy, as an overview of the NumPy libraries.

Chapter 3, *SciPy for Linear Algebra*, covers applications of SciPy to applications with large matrices, including solving systems or computation of eigenvalues and eigenvectors.

Chapter 4, *SciPy for Numerical Analysis*, is without a doubt one of the most interesting chapters in this book. It covers with great detail the definition and manipulation of functions (one or several variables), the extraction of their roots, extreme values (optimization), computation of derivatives, integration, interpolation, regression, and applications to the solution of ordinary differential equations.

Chapter 5, *SciPy for Signal Processing*, explores construction, acquisition, quality improvement, compression, and feature extraction of signals (in any dimension). It is covered with beautiful and interesting examples from the field of image processing.

Chapter 6, *SciPy for Data Mining*, covers applications of SciPy for collection, organization, analysis, and interpretation of data, with examples taken from statistics and clustering.

Chapter 7, *SciPy for Computational Geometry*, explores the construction of triangulation of points, convex hulls, Voronoi diagrams, and applications, including the solving of the two dimensional Laplace Equation via the Finite Element Method in a rectangular grid. At this point in the book, it will be possible to combine techniques from all the previous chapters to show state-of-the-art research performed with ease with SciPy, and we will explore a few good examples from Material Science and Experimental Physics.

Chapter 8, *Interaction with Other Languages*, introduces one of the main strengths of SciPy—the ability to interact with other languages such as C/C++, Fortran, R, and MATLAB/Octave.