# Numerical Python: Scientific Computing and Data Science Applications

The first chapter in this book introduces general principles for scientific computing and the main development environments that are available for work with computing in Python: the focus is on IPython and its interactive Python prompt, the excellent Jupyter Notebook application, and the Spyder IDE.

In Chapter 2, an introduction to the NumPy library is given, and here we also discuss more generally array-based computing and its virtues. In Chapter 3, we turn our attention to symbolic computing – which in many respects complements array-based computing – using the SymPy library. In Chapter 4, we cover plotting and visualization using the Matplotlib library. Together, Chapters 2 to 4 provide the basic computational tools that will be used for domain-specific problems throughout the rest of the book: numerics, symbolics, and visualization.

In Chapter 5, the topic of study is equation solving, which we explore with both numerical and symbolic methods, using the SciPy and SymPy libraries. In Chapter 6, we explore optimization, which is a natural extension of equation solving. Here we mainly work with the SciPy library and briefly with the cvxopt library. Chapter 7 deals with interpolation, which is another basic mathematical method with many applications of its own, and important roles in higher-level algorithms and methods. In Chapter 8, we

cover numerical and symbolic integration. Chapters 5 to 8 cover core computational techniques that are pervasive in all types of computational work. Most of the methods from these chapters are found in the SciPy library.

In Chapter 9, we proceed to cover ordinary differential equations. Chapter 10 is a detour into sparse matrices and graph methods, which helps prepare the field for the following chapter. In Chapter 11, we discuss partial differential equations, which conceptually are closely related to ordinary differential equations, but require a different set of techniques that necessitates the introduction of sparse matrices, the topic of

Chapter 10.