Python 3精华速查：2.7/3.5/3.6指南

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《Python in a Nutshell: A Desktop Quick Reference》第三版（ISBN: 144939292X, 2017）是一本针对Python编程语言的实用指南，旨在为经验丰富的程序员以及从其他编程语言转向Python的人提供快速参考。该书特别关注了Python 3.5、2.7以及3.6的一些亮点，涵盖了广泛的应用领域，包括Web和网络编程、XML处理、数据库交互以及高性能数值计算。本书的核心内容包括Python语法介绍，以及面向对象的Python实践。读者可以学习到如何有效地利用Python的标准库模块，如os、sys、datetime等，这些模块提供了丰富的功能，使得文件和文本操作、持久化数据存储以及数据库连接变得简单。此外，书中还深入讲解了并发执行、事件驱动编程和客户端网络协议模块，这对于理解和应用Python在网络应用中的角色至关重要。在高级主题部分，作者探讨了Python扩展模块的开发与管理，以及如何使用像pip这样的工具打包和分发扩展模块、模块和应用程序。对于希望在科学计算、数据分析或机器学习等领域发挥Python威力的读者，这部分内容尤其有价值。值得注意的是，作者团队由Alex Martelli、Anna Ravenscroft和Steve Holden组成，他们均为Python领域的专家。书中版权信息表明，所有权利归三位作者所有，并且此版本于2017年四月出版，经过多次修订，确保内容紧跟Python语言的发展步伐。《Python in a Nutshell》第三版是一本既适合初学者巩固基础知识，又适合经验丰富的开发者查阅和参考的专业书籍，无论是在设计、原型制作、测试，还是部署和维护过程中，都能提供强大的实用指导。无论是对Python语言特性有深入了解，还是希望拓展其在特定应用场景中的使用，这本书都是不可或缺的参考资料。

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Acknowledgments

Thanks to our thorough and conscientious reviewers and editor! All errors that

remain are entirely our own.

Many thanks to our editor, Dawn Schanafelt, for gently guiding, nudging, and push‐

ing us along to get this done! Also, thanks to Kim Cofer, our excellent copyeditor,

whose eye for detail caught not just grammatical issues, but also some technical

errors.

Thanks to our hard-working tech reviewers: Brett Cannon, Luciano Ramalho, and

Victoria Wong, who read through every explanation and example in the book’s

draft. Without them, this book would not have been nearly as clear or accurate.

Thanks as well to our several unofficial reviewers: Tim Golden, for his invaluable

help with Python for Windows coverage; Nick Coghlan and Donald Stufft, for their

help untangling the intricacies of Python packaging; Łukasz Langa, for his copious

feedback on asynchronous programming; Peter Norvig, for his precious, detailed

feedback and advice about the entire book; Daniel Pyrathon, for his review of the

chapter on HTTP serving frameworks; and George Gillan, for reporting many typos

and unclear explanations in Early Release editions of this book.

And finally, thanks to Google for its useful Hangouts app and other G Suite online

collaboration tools, without which our intense communication and coordination

among authors in different continents would have been far less easy and efficient.

xiv | Preface

opment and maintenance, particularly in large projects, where many developers

cooperate and often maintain code originally written by others.

Python is simple, but not simplistic. It adheres to the idea that, if a language behaves

a certain way in some contexts, it should ideally work similarly in all contexts.

Python also follows the principle that a language should not have “convenient”

shortcuts, special cases, ad hoc exceptions, overly subtle distinctions, or mysterious

and tricky under-the-covers optimizations. A good language, like any other well-

designed artifact, must balance such general principles with taste, common sense,

and a high degree of practicality.

Python is a general-purpose programming language: Python’s traits are useful in

just about any area of software development. There is no area where Python cannot

be part of a great solution. “Part” is an important word here; while many developers

find that Python fills all of their needs, it does not have to stand alone: Python pro‐

grams can cooperate with a variety of other software components, making it an

ideal language for gluing together components written in other languages. One of

the language’s design goals was that it should “play well with others.”

Python is a very high-level language (VHLL). This means that Python uses a higher

level of abstraction, conceptually further from the underlying machine, than do

classic compiled languages such as C, C++, and Fortran, which are traditionally

called “high-level languages.” Python is simpler, faster to process (both for human

brains and for programmatic tools), and more regular than classic high-level lan‐

guages. This enables high programmer productivity and makes Python an attractive

development tool. Good compilers for classic compiled languages can generate

binary machine code that runs faster than Python code. However, in most cases, the

performance of Python-coded applications is sufficient. When it isn’t, apply the

optimization techniques covered in “Optimization” on page 477 to improve your

program’s performance while keeping the benefit of high productivity.

Somewhat newer languages such as Java and C# are slightly higher-level than classic

ones such as C and Fortran, and share some characteristics of classic languages

(such as the need to use declarations) as well as some of VHLLs like Python (such as

the use of portable bytecode as the compilation target in typical implementations,

and garbage collection to relieve programmers from the need to manage memory).

If you find you are more productive with Java or C# than with C or Fortran, try

Python (possibly in the Jython or IronPython implementations, covered in “Python

Implementations” on page 5) to become even more productive.

In terms of language level, Python is comparable to other powerful VHLLs like

JavaScript, Ruby, and Perl. The advantages of simplicity and regularity, however,

remain on Python’s side.

Python is an object-oriented programming language, but lets you develop code

using both object-oriented and procedural styles, and a touch of functional pro‐

gramming, too, mixing and matching as your application requires. Python’s object-

oriented features are conceptually similar to those of C++, but simpler to use.

4 | Chapter 1: Introduction to Python

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