O'Reilly《Python in a Nutshell 第2版》：经典Python学习指南

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"《Python in a Nutshell》第二版，是O'Reilly出版社的经典之作，属于其动物系列之一，专为Python编程语言的学习者设计。本书由Alex Martelli编著，于2006年和2003年分别出版了第一版和第二版。它旨在为读者提供简洁而全面的Python技术指南，帮助读者快速掌握这门强大的脚本语言。作为一本实用手册，书中深入浅出地讲解了Python的基础知识、核心概念以及高级特性。从Python的语法结构、数据类型、控制流程、函数、模块到异常处理、面向对象编程，甚至包括了一些高级主题如网络编程、数据库操作和元编程等。作者以清晰的逻辑和实例驱动的方式，确保读者能够在实践中理解并熟练运用Python。《Python in a Nutshell》特别适合那些希望快速熟悉Python或需要参考手册来解决日常编程问题的专业人士。此外，对于初学者来说，这本书也是学习Python的良好起点，它不仅提供了基础知识，还为进阶学习奠定了坚实的基础。值得一提的是，本书的版权信息强调了所有权利归O'Reilly Media Inc.所有，并且指出在线版本也在safari.oreilly.com上可供访问。印刷版本的出版历史显示，该书在2003年首次发行，而后在2006年进行了修订更新。书中涉及的编辑、生产编辑、校对人员以及封面设计师等都为确保内容质量和专业呈现做出了贡献。《Python in a Nutshell》第二版是一本不可或缺的Python技术参考资料，无论是对于初学者还是经验丰富的开发者，都能从中获益匪浅。通过阅读和实践，读者可以全面提升Python编程技能，适应不断发展的IT行业需求。"

xiv

Preface

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Acknowledgments

My heartfelt thanks to everybody who helped me out on this book, both in the

first edition and in its current second edition. Many Python beginners, practitio-

ners, and experts have read drafts of parts of the book and have offered feedback

to help me make the book clear, precise, accurate, and readable. Out of all of

them, for the quality and quantity of their feedback and other help, I must single

out for special thanks my colleagues at Google, especially Neal Norwitz and

Mohsin Ahmed.

The first edition received indispensable help from Python experts in specific areas

(Aahz on threading, Itamar Shtull-Trauring on Twisted, Mike Orr on Cheetah,

Eric Jones and Paul Dubois on Numeric, and Tim Peters on threading, testing,

and optimization), a wonderful group of technical reviewers (Fred Drake, Magnus

Lie Hetland, Steve Holden, and Sue Giller), and the book’s editor, Paula

Ferguson. The second edition benefited from the efforts of editors Jonathan

Gennick and Mary O’Brien, and technical reviewers Ryan Alexander, Jeffery

Collins, and Mary Gardiner. I owe special thanks to the wonderful folks in the

O’Reilly Tools Group, who (both directly and personally, and through the helpful

tools they developed) helped me through several difficult technical problems.

As always, even though they’re back in my native Italy and my career with Google

has taken me to California, my thoughts go to my family: my children Flavia and

Lucio, my sister Elisabetta, and my father Lanfranco.

But the one, incredible individual to which my heart gushes out in gratitude, and

more than gratitude, is my wife, Anna Martelli Ravenscroft, my co-author in the

second edition of the Python Cookbook, a fellow Python Software Foundation

member, and the harshest, most wonderful technical reviewer any author could

possibly dream of. Besides her innumerable direct contributions to this book,

Anna managed to create for me, out of thin air, enough peace, quiet, and free time

over the last year (despite my wonderful and challenging responsibilities as Uber

Tech Lead for Google) to make this book possible. Truly, this is her book at least

as much as it is mine.

www.it-ebooks.info

Chapter 1: Introduction to Python

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 distinc-

tions, or mysterious and tricky under-the-covers optimizations. A good language,

like any other designed artifact, must balance such general principles with taste,

common sense, and a high degree of practicality.

Python is a general-purpose programming language, so Python’s traits are useful

in just about any area of software development. There is no area where Python

cannot be part of an optimal solution. “Part” is an important word here; while

many developers find that Python fills all of their needs, Python does not have to

stand alone. Python programs can easily cooperate with a variety of other soft-

ware components, making it an ideal language for gluing together components

written in other languages.

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

higher level of abstraction, conceptually farther from the underlying machine,

than do classic compiled languages such as C, C++, and Fortran, which are tradi-

tionally called high-level languages. Python is also simpler, faster to process, and

more regular than classic high-level languages. This affords high programmer

productivity and makes Python an attractive development tool. Good compilers

for classic compiled languages can often generate binary machine code that runs

much faster than Python code. However, in most cases, the performance of

Python-coded applications proves sufficient. When it doesn’t, you can apply the

optimization techniques covered in “Optimization” on page 474 to enhance your

program’s performance while keeping the benefits of high programming

productivity.

Newer languages such as Java and C# are slightly higher-level (farther from the

machine) 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) and become

even more productive.

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

Perl or Ruby. The advantages of simplicity and regularity, however, remain on

Python’s side.

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

using both object-oriented and traditional procedural styles, and a touch of the

functional programming style, too, mixing and matching as your application

requires. Python’s object-oriented features are like those of C++, although they

are much simpler to use.

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