What this book covers
Chapter 1, The Landscape of Scientific Computing – and Why Python?, introduces the basic concepts of scientific computing. It also discusses the background of Python, its guiding principle, and why using Python for scientific computing is efficient.
Chapter 2, A Deeper Dive into Scientific Workflows and the Ingredients of Scientific Computing Recipes, discusses the various concepts of mathematical and numerical analysis that are generally required to solve scientific problems. It also covers a brief introduction to the packages, toolkits, and APIs meant for performing scientific computing in the Python language.
Chapter 3, Efficiently Fabricating and Managing Scientific Data, discusses all the aspects about the underlying data of scientific applications, including the basic concepts, various operations, and the formats and software used to store data. It also presents standard datasets and techniques of preparing synthetic data.
Chapter 4, Scientific Computing APIs for Python, covers the basic concepts, features, and selected sample programs of various scientific computing APIs and toolkits, including NumPy, SciPy, and SymPy. A basic introduction to interactive computing, data analysis, and data visualization is also discussed in this chapter using IPython, matplotlib, and pandas.
Chapter 5, Performing Numerical Computing, discusses how to perform numerical computations using the NumPy and SciPy packages of Python. This chapter starts with the basics of numerical computation and covers a number of advanced concepts, such as optimization, interpolation, Fourier transformation, signal processing, linear algebra, statistics, spatial algorithms, image processing, file input/output, and others.
Chapter 6, Applying Python for Symbolic Computing, starts with the fundamentals of the Computerized Algebra System (CAS) and performing symbolic computations using SymPy. It covers a vast range of topics on CAS, from using simple expressions and basic arithmetic to advanced concepts of mathematics and physics.
Chapter 7, Data Analysis and Visualization, presents the concepts and applications of matplotlib and pandas for data analysis and visualization.
Chapter 8, Parallel and Large-scale Scientific Computing, discusses the concepts of high-performance scientific computing using IPython (which is done using MPI), the management of the Amazon EC2 cluster using StarCluster, multiprocessing, multithreading, Hadoop, and Spark.
Chapter 9, Revisiting Real-life Case Studies, illustrates several case studies of scientific computing applications, libraries, and tools developed using the Python language. Some cases studied from various engineering and science domains are presented in this chapter.
Chapter 10, Best Practices for Scientific Computing, discusses the best practices for scientific computing. It consists of the best practices for designing, coding, data management, application deployment, high-performance computing, security, data privacy, maintenance, and support. We also cover the best practices for general Python-based development.