Python数值计算实战：工业界应用的实用技巧

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《数值Python：工业实践中的实用技术指南》（Numerical Python: A Practical Techniques Approach for Industry）是由罗伯特·约翰松所著的一本专业书籍，它针对IT行业中的从业者，如数据科学家、工程师、金融工程师、科学家、商务管理者等，提供了一套全面的指导。本书将重点讲解如何利用Python及其标准库（如NumPy、SciPy、SymPy、Matplotlib、Pandas等）进行数值计算和数学建模，以应用于诸如大数据、云计算、金融工程、商业管理等多个领域。作者在书中强调了Python的流行性，其简洁易懂的语法使其在现代计算工作中尤其适合快速开发和探索性编程。通过学习本书，读者能够掌握一系列关键技能，包括： 1. NumPy：学习如何使用向量和矩阵，这是处理多维数据的基础。 2. SymPy：了解符号计算，能够解决线性和非线性方程，以及高级数学问题。 3. Matplotlib：学会用作数据可视化工具，有效地呈现分析结果。 4. SciPy：掌握优化、插值和积分等通用计算方法，以及特定领域的解算问题，如常微分方程和偏微分方程。 5. Pandas：用于数据处理与分析，执行统计任务和解决与数据相关的业务问题。 6. Statsmodels 和 scikit-learn：学习统计建模和机器学习技术，提升数据分析能力。 7. 文件I/O：学会使用HDF5和其他常见格式来管理和读写数值数据。 8. 代码优化：通过Numba和Cython等工具提高Python代码的性能。本书共分为19章，内容涵盖了从基础的Python编程到高级的信号处理和数据输入输出等方方面面。此外，还包括一个附录，指导读者如何安装所需的软件包和工具。《数值Python》是一本实践导向的书籍，适合希望提升自己在工业界应用数值计算技能的专业人士，无论是为了现在的工作需求，还是为了未来的职业发展，都能从中受益匪浅。通过阅读本书，读者不仅能获得丰富的实例和案例，还能深入理解并掌握如何在实际工作中灵活运用这些技术。

■ Contents

xvi

JSON�� 445

Serialization �� 449

Summary �� 451

Further Reading �� 479

Index �� 481

Introduction

Scientific and numerical computing is a booming field in research, engineering, and analytics. The

revolution in the computer industry over the last several decades has provided new and powerful tools for

computational practitioners. This has enabled computational undertakings of previously unprecedented

scale and complexity. Entire fields and industries have sprung up as a result. This development is still on

going, and it is creating new opportunities as hardware, software, and algorithms keep improving. Ultimately

the enabling technology for this movement is the powerful computing hardware that has been developed in

recent decades. However, for a computational practitioner, the software environment used for computational

work is as important as, if not more important than, the hardware on which the computations are carried

out. This book is about one popular and fast growing environment for numerical computing: the Python

programming language and its vibrant ecosystem of libraries and extensions for computational work.

Computing is an interdisciplinary activity that requires experience and expertise in both theoretical

and practical subjects: a firm understanding of mathematics and scientific thinking is a fundamental

requirement for effective computational work. Equally important is solid training in computer programming

and computer science. The role of this book is to bridge these two subjects by introducing how scientific

computing can be done using the Python programming language and the computing environment that

has appeared around this language. In this book the reader is assumed to have some previous training in

mathematics and numerical methods, and basic knowledge about Python programming. The focus of the

book is to give a practical introduction to computational problem solving with Python. Brief introductions to

the theory of the covered topics are given in each chapter, to introduce notation and remind readers of the

basic methods and algorithms. However, this book is not a self-consistent treatment of numerical methods.

To assist readers that are not previously familiar with some of the topics of this book, references for further

reading are given at the end of each chapter. Likewise, readers without experience in Python programming

will probably find it useful to read this book together with a book that focus on the Python programming

language itself.

How This Book is Organized

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 and its excellent 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

xxi

■ IntroduCtIon

xxii

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.

Starting with Chapter 12, we make a change of direction and begin exploring data analysis and statistics.

In Chapter 12 we introduce the Pandas library and its excellent data analysis framework. In Chapter 13, we

cover basic statistical analysis and methods from the SciPy stats package. In Chapter 14, we move on to

statistical modeling, using the statsmodels library. In Chapter 15, the theme of statistics and data analysis

is continued with a discussion of machine learning, using the scikit-learn library. In Chapter 16, we wrap

up the statistics-related chapters with a discussion of Bayesian statistics and the PyMC library. Together,

Chapters 12 to 16 provide an introduction to the broad field of statistics and data analytics: a field that has

been developing rapidly within and outside of the scientific Python community in recent years.

In Chapter 17 we briefly return to a core subject in scientific computing: signal processing. In

Chapter 18, we discuss data input and output, and several methods for reading and writing numerical data

to files, which is a basic topic that is required for most types of computational work. In Chapter 19, the final

regular chapter in this book, two methods for speeding up Python code are introduced, using the Numba

and Cython libraries.

The appendix covers the installation of the software used in this book. To install the required software

(mostly Python libraries), we use the conda package manager. Conda can also be used to create virtual

and isolated Python environments, which is an important topic for creating stable and reproducible

computational environments. The appendix also discusses how to work with such environments using the

conda package manager.

Source Code Listings

Each chapter in this book has an accompanying IPython notebook that contains the chapter’s source code

listings. These notebooks, and the data files required to run them, can be downloaded from the Source Code

page on the Apress web site, at www.apress.com/9781484205549.

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