Python编程中的《CleanCode》：软件工程原则实践指南

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"《Clean Code in Python》是一本专为软件工程原则在Python编程实践中的应用而撰写的书籍。它旨在填补当前市场上关于软件工程理论和Python技术之间知识的空白，特别关注如何对遗留代码库进行重构，以提高代码质量与可维护性。作者Mariano Anaya以BIRMINGHAM-MUMBAI为背景，通过这本书探讨了如何遵循专业的编程规范和最佳实践，以创建出高效、清晰且易于理解的Python代码。该书版权由Packt Publishing持有，并强调所有内容未经许可不得复制或传播，除非用于学术引用。尽管作者和出版社已经尽力确保信息的准确性，但书中提供的信息不承担任何形式的保证，无论是明示的还是暗示的。用户在使用本书内容时应自行判断其适用性和风险。《Clean Code in Python》的出版过程由多位专业编辑参与，包括Merint Mathew作为委托编辑，Denim Pinto负责项目收购，以及Priyanka Sawant担任内容开发编辑。这些人员的协作确保了书籍不仅内容丰富，而且符合行业标准，为读者提供了一套系统的方法来提升Python代码的清晰度和可读性，帮助开发者在实际项目中实施和应用软件工程的最佳实践。无论是初次接触Python的开发者，还是希望优化现有代码库的专业人士，这本书都是一个重要的学习资源和参考指南。"

Preface

[ 3 ]

What this book covers

Chapter 1, Introduction, Code Formatting, and Tools, is an introduction to the main tools you

need to set up a development environment in Python. We cover the basics a Python

developer is recommended to know to start working with the language, as well as some

guidelines for maintaining readable code in the project, such as tools for static analysis,

documentation, type checking, and code formatting.

Chapter 2, Pythonic Code, looks at the first idioms in Python, which we will continue to use

in the following chapters. We cover the particular features of Python, how they should be

used, and we start building knowledge around the idea that Pythonic code is in general

much better quality code.

Chapter 3, General Traits of Good Code, reviews general principles of software engineering

that focus on writing maintainable code. We explore the idea and apply the concepts with

the tools in the language.

Chapter 4, The SOLID Principles, covers a set of design principles for object-oriented

software design. This acronym is part of the language or jargon of software engineering,

and we see how each one of them can be applied to Python. Arguably not all of them are

entirely applicable due to the nature of the language.

Chapter 5, Using Decorators to Improve Our Code, looks at one of the greatest features of

Python. After understanding how to create decorators (for functions and classes), we put

them in action for reusing code, separating responsibilities, and creating more granular

functions.

Chapter 6, Getting More Out of Our Objects with Descriptors, explores descriptors in Python,

which take object-oriented design to a new level. While this is a feature more related to

frameworks and tools, we can see how to improve the readability of our code with

descriptors, and also reuse code.

Chapter 7, Using Generators, shows that generators are probably the best feature of Python.

The fact that iteration is a core component of Python could make us think that it leads to a

new programming paradigm. By using generators and iterators in general, we can think

about the way we write our programs. With the lessons learned from generators, we go

further and learn about coroutines in Python, and the basics of asynchronous

programming.

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