精通Python：提升编程技巧与效率

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"Fluent Python是Luciano Ramalho所著的一本关于Python编程的书籍，旨在帮助中高级Python程序员深入理解和运用Python的核心语言特性和库，以提升代码效率和可读性。书中通过实践性的指导，揭示了Python可能被忽视但非常强大的特性。" 在《Fluent Python》这本书中，作者探讨了一系列关键的Python知识点： 1. **Python语法的清晰与简洁**：Python以其简洁明了的语法而闻名，书中会深入解释如何利用这一优势来编写易于理解的代码。这包括对缩进规则、函数定义、类构造以及异常处理等方面的详细讲解。 2. **数据结构的高效使用**：Python提供了丰富的内置数据结构，如列表、元组、集合和字典。书中将阐述如何选择合适的数据结构，以及如何利用它们的特性优化代码性能，例如列表推导式、字典的键值对操作等。 3. **面向对象编程**：Python是支持面向对象编程的语言，书中会详细介绍类与对象的概念，包括类的定义、继承、封装和多态性，同时讨论如何使用装饰器和上下文管理器来增强代码的功能和可维护性。 4. **函数式编程**：Python也支持函数式编程风格，书中有专门章节讨论高阶函数、闭包和生成器，这些工具可以用于简化复杂任务，如函数式编程中的map、filter和reduce函数。 5. **元编程**：元编程允许在运行时动态地创建和修改类及对象，是Python的一个强大特性。书中会讲解如何使用元类、描述符和类型系统来实现元编程，提高代码的灵活性。 6. **标准库的深度探索**：Python的标准库包含大量实用模块，如os、sys、re（正则表达式）、collections（高级数据结构）和itertools（迭代工具）。作者会带领读者深入这些库，展示它们在实际项目中的应用。 7. **并发与异步编程**：随着多核处理器的普及，Python的并发和异步编程变得尤为重要。书中可能会涵盖线程、进程、协程（如asyncio库）等概念，以及如何利用它们实现高效的并发执行。 8. **错误和调试**：良好的错误处理是编写健壮代码的关键。书中会讨论Python的异常机制，以及如何进行有效的调试，包括使用pdb等工具。 9. **性能优化**：Python虽然通常被认为是解释型语言，但通过适当的技术，如列表推导、生成器和内存管理，可以显著提高代码性能。《Fluent Python》是一本全面而深入的Python编程指南，它不仅适合有一定基础的Python开发者进一步提升技能，对于初学者来说，也能提供一个了解Python高级特性的宝贵资源。书中实例丰富，理论与实践相结合，帮助读者更好地掌握Python编程的艺术。

Descriptor Usage Tips 648

Descriptor docstring and Overriding Deletion 650

Chapter Summary 651

Further Reading 679

Afterword. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 683

A. Support Scripts. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 687

Python Jargon. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 715

Index. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 725

xiv | Table of Contents

Python package index now lists more than 60,000 libraries and many of them are in‐

credibly useful.

Who This Book Is For

This book was written for practicing Python programmers who want to become pro‐

ficient in Python 3. If you know Python 2 but are willing to migrate to Python 3.4 or

later, you should be fine. At the time of this writing, the majority of professional Python

programmers are using Python 2, so I took special care to highlight Python 3 features

that may be new to that audience.

However, Fluent Python is about making the most of Python 3.4, and I do not spell out

the fixes needed to make the code work in earlier versions. Most examples should run

in Python 2.7 with little or no changes, but in some cases, backporting would require

significant rewriting.

Having said that, I believe this book may be useful even if you must stick with Python

2.7, because the core concepts are still the same. Python 3 is not a new language, and

most differences can be learned in an afternoon. What’s New in Python 3.0 is a good

starting point. Of course, there have been changes since Python 3.0 was released in 2009,

but none as important as those in 3.0.

If you are not sure whether you know enough Python to follow along, review the topics

of the official Python Tutorial. Topics covered in the tutorial will not be explained here,

except for some features that are new in Python 3.

Who This Book Is Not For

If you are just learning Python, this book is going to be hard to follow. Not only that, if

you read it too early in your Python journey, it may give you the impression that every

Python script should leverage special methods and metaprogramming tricks. Premature

abstraction is as bad as premature optimization.

How This Book Is Organized

The core audience for this book should not have trouble jumping directly to any chapter

in this book. However, each of the six parts forms a book within the book. I conceived

the chapters within each part to be read in sequence.

I tried to emphasize using what is available before discussing how to build your own.

For example, in Part II, Chapter 2 covers sequence types that are ready to use, including

some that don’t get a lot of attention, like collections.deque. Building user-defined

sequences is only addressed in Part IV, where we also see how to leverage the abstract

base classes (ABCs) from collections.abc. Creating your own ABCs is discussed even

xvi | Preface

later in Part IV, because I believe it’s important to be comfortable using an ABC before

writing your own.

This approach has a few advantages. First, knowing what is ready to use can save you

from reinventing the wheel. We use existing collection classes more often than we im‐

plement our own, and we can give more attention to the advanced usage of available

tools by deferring the discussion on how to create new ones. We are also more likely to

inherit from existing ABCs than to create a new ABC from scratch. And finally, I believe

it is easier to understand the abstractions after you’ve seen them in action.

The downside of this strategy are the forward references scattered throughout the

chapters. I hope these will be easier to tolerate now that you know why I chose this path.

Here are the main topics in each part of the book:

Part I

A single chapter about the Python data model explaining how the special methods

(e.g., __repr__) are the key to the consistent behavior of objects of all types—in a

language that is admired for its consistency. Understanding various facets of the

data model is the subject of most of the rest of the book, but Chapter 1 provides a

high-level overview.

Part II

The chapters in this part cover the use of collection types: sequences, mappings,

and sets, as well as the str versus bytes split—the cause of much celebration among

Python 3 users and much pain for Python 2 users who have not yet migrated their

code bases. The main goals are to recall what is already available and to explain

some behavior that is sometimes surprising, like the reordering of dict keys when

we are not looking, or the caveats of locale-dependent Unicode string sorting. To

achieve these goals, the coverage is sometimes high level and wide (e.g., when many

variations of sequences and mappings are presented) and sometimes deep (e.g.,

when we dive into the hash tables underneath the dict and set types).

Part III

Here we talk about functions as first-class objects in the language: what that means,

how it affects some popular design patterns, and how to implement function dec‐

orators by leveraging closures. Also covered here is the general concept of callables

in Python, function attributes, introspection, parameter annotations, and the new

nonlocal declaration in Python 3.

Part IV

Now the focus is on building classes. In Part II, the class declaration appears in

few examples; Part IV presents many classes. Like any object-oriented (OO) lan‐

guage, Python has its particular set of features that may or may not be present in

the language in which you and I learned class-based programming. The chapters

explain how references work, what mutability really means, the lifecycle of instan‐

Preface | xvii

ces, how to build your own collections and ABCs, how to cope with multiple in‐

heritance, and how to implement operator overloading—when that makes sense.

Part V

Covered in this part are the language constructs and libraries that go beyond se‐

quential control flow with conditionals, loops, and subroutines. We start with gen‐

erators, then visit context managers and coroutines, including the challenging but

powerful new yield from syntax. Part V closes with a high-level introduction to

modern concurrency in Python with collections.futures (using threads and

processes under the covers with the help of futures) and doing event-oriented I/O

with asyncio (leveraging futures on top of coroutines and yield from).

Part VI

This part starts with a review of techniques for building classes with attributes

created dynamically to handle semi-structured data such as JSON datasets. Next,

we cover the familiar properties mechanism, before diving into how object attribute

access works at a lower level in Python using descriptors. The relationship between

functions, methods, and descriptors is explained. Throughout Part VI, the step-by-

step implementation of a field validation library uncovers subtle issues that lead to

the use of the advanced tools of the final chapter: class decorators and metaclasses.

Hands-On Approach

Often we’ll use the interactive Python console to explore the language and libraries. I

feel it is important to emphasize the power of this learning tool, particularly for those

readers who’ve had more experience with static, compiled languages that don’t provide

a read-eval-print#loop (REPL).

One of the standard Python testing packages, doctest, works by simulating console

sessions and verifying that the expressions evaluate to the responses shown. I used

doctest to check most of the code in this book, including the console listings. You don’t

need to use or even know about doctest to follow along: the key feature of doctests is

that they look like transcripts of interactive Python console sessions, so you can easily

try out the demonstrations yourself.

Sometimes I will explain what we want to accomplish by showing a doctest before the

code that makes it pass. Firmly establishing what is to be done before thinking about

how to do it helps focus our coding effort. Writing tests first is the basis of test driven

development (TDD) and I’ve also found it helpful when teaching. If you are unfamiliar

with doctest, take a look at its documentation and this book’s source code repository.

You’ll find that you can verify the correctness of most of the code in the book by typing

python3 -m doctest example_script.py in the command shell of your OS.

xviii | Preface

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鲸阮

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