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Effective Python:改善Python程序的90个建议 (第2版)(英文版)

Effective Python:改善Python程序的90个建议 (第2版)(英文版)

出版社:电子工业出版社出版时间:2020-05-01
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Effective Python:改善Python程序的90个建议 (第2版)(英文版) 版权信息

  • ISBN:9787121386930
  • 条形码:9787121386930 ; 978-7-121-38693-0
  • 装帧:平装-胶订
  • 册数:暂无
  • 重量:暂无
  • 所属分类:>

Effective Python:改善Python程序的90个建议 (第2版)(英文版) 本书特色

Brett Slatkin根据自己在Google公司多年开发Python基础架构所积累的经验,揭示了Python语言中一些鲜为人知的微妙特性,并给出了能够改善代码功能及运行效率的习惯用法。书中汇聚了90个优秀的实践原则、开发技巧和便捷方案,并以实用的代码范例来解释它们。通过本书,你能够了解到解决关键编程任务的实用技巧,并学会编写易于理解、便于维护且利于改进的代码。除此之外,本书第2版基本上修改了第1版中的所有条目,以反映Python实践的演变历程。

Effective Python:改善Python程序的90个建议 (第2版)(英文版) 内容简介

Brett Slatkin根据自己在Google公司多年开发Python基础架构所积累的经验,揭示了Python语言中一些鲜为人知的微妙特性,并给出了能够改善代码功能及运行效率的习惯用法。书中汇聚了90个很好的实践原则、开发技巧和便捷方案,并以实用的代码范例来解释它们。通过本书,你能够了解到解决关键编程任务的实用技巧,并学会编写易于理解、便于维护且利于改进的代码。除此之外,本书第2版基本上修改了版中的所有条目,以反映Python实践的演变历程。

Effective Python:改善Python程序的90个建议 (第2版)(英文版) 目录

Chapter 1 Pythonic Thinking 1
Item 1: Know Which Version of Python You’re Using 1
Item 2: Follow the PEP 8 Style Guide 2
Item 3: Know the Differences Between bytes and str 5
Item 4: Prefer Interpolated F-Strings Over C-style
Format Strings and str.format 11
Item 5: Write Helper Functions Instead of
Complex Expressions 21
Item 6: Prefer Multiple Assignment Unpacking
Over Indexing 24
Item 7: Prefer enumerate Over range 28
Item 8: Use zip to Process Iterators in Parallel 30
Item 9: Avoid else Blocks After for and while Loops 32
Item 10: Prevent Repetition with Assignment Expressions 35
Chapter 2 Lists and Dictionaries 43
Item 11: Know How to Slice Sequences 43
Item 12: Avoid Striding and Slicing in a Single Expression 46
Item 13: Prefer Catch-All Unpacking Over Slicing 48
Item 14: Sort by Complex Criteria Using the key Parameter 52
Item 15: Be Cautious When Relying on dict
Insertion Ordering 58
Item 16: Prefer get Over in and KeyError to
Handle Missing Dictionary Keys 65
Item 17: Prefer defaultdict Over setdefault to
Handle Missing Items in Internal State 70
Item 18: Know How to Construct Key-Dependent
Default Values with __missing__ 73
Chapter 3 Functions 77
Item 19: Never Unpack More Than Three Variables
When Functions Return Multiple Values 77
Item 20: Prefer Raising Exceptions to Returning None 80
Item 21: Know How Closures Interact with Variable Scope 83
Item 22: Reduce Visual Noise with Variable
Positional Arguments 87
Item 23: Provide Optional Behavior with Keyword Arguments 90
Item 24: Use None and Docstrings to Specify
Dynamic Default Arguments 94
Item 25: Enforce Clarity with Keyword-Only and
Positional-Only Arguments 97
Item 26: Define Function Decorators with functools.wraps 102
Chapter 4 Comprehensions and Generators 107
Item 27: Use Comprehensions Instead of map and filter 107
Item 28: Avoid More Than Two Control Subexpressions in
Comprehensions 109
Item 29: Avoid Repeated Work in Comprehensions by Using
Assignment Expressions 111
Item 30: Consider Generators Instead of Returning Lists 114
Item 31: Be Defensive When Iterating Over Arguments 117
Item 32: Consider Generator Expressions for Large List
Comprehensions 122
Item 33: Compose Multiple Generators with yield from 124
Item 34: Avoid Injecting Data into Generators with send 127
Item 35: Avoid Causing State Transitions in
Generators with throw 133
Item 36: Consider itertools for Working with Iterators
and Generators 138
Chapter 5 Classes and Interfaces 145
Item 37: Compose Classes Instead of Nesting
Many Levels of Built-in Types 145
Item 38: Accept Functions Instead of Classes for
Simple Interfaces 152
Item 39: Use @classmethod Polymorphism to
Construct Objects Generically 155
Item 40: Initialize Parent Classes with super 160
Item 41: Consider Composing Functionality
with Mix-in Classes 165
Item 42: Prefer Public Attributes Over Private Ones 170
Item 43: Inherit from collections.abc for
Custom Container Types 175
Chapter 6 Metaclasses and Attributes 181
Item 44: Use Plain Attributes Instead of Setter and
Getter Methods 181
Item 45: Consider @property Instead of
Refactoring Attributes 186
Item 46: Use Descriptors for Reusable @property Methods 190
Item 47: Use __getattr__, __getattribute__, and
__setattr__ for Lazy Attributes 195
Item 48: Validate Subclasses with __init_subclass__ 201
Item 49: Register Class Existence with __init_subclass__ 208
Item 50: Annotate Class Attributes with __set_name__ 214
Item 51: Prefer Class Decorators Over Metaclasses for
Composable Class Extensions 218
Chapter 7 Concurrency and Parallelism 225
Item 52: Use subprocess to Manage Child Processes 226
Item 53: Use Threads for Blocking I/O, Avoid for Parallelism 230
Item 54: Use Lock to Prevent Data Races in Threads 235
Item 55: Use Queue to Coordinate Work Between Threads 238
Item 56: Know How to Recognize When Concurrency
Is Necessary 248
Item 57: Avoid Creating New Thread Instances for
On-demand Fan-out 252
Item 58: Understand How Using Queue for
Concurrency Requires Refactoring 257
Item 59: Consider ThreadPoolExecutor When Threads
Are Necessary for Concurrency 264
Item 60: Achieve Highly Concurrent I/O with Coroutines 266
Item 61: Know How to Port Threaded I/O to asyncio 271
Item 62: Mix Threads and Coroutines to Ease the
Transition to asyncio 282
Item 63: Avoid Blocking the asyncio Event Loop to
Maximize Responsiveness 289
Item 64: Consider concurrent.futures for True Parallelism 292
Chapter 8 Robustness and Performance 299
Item 65: Take Advantage of Each Block in try/except
/else/finally 299
Item 66: Consider contextlib and with Statements
for Reusable try/finally Behavior 304
Item 67: Use datetime Instead of time for Local Clocks 308
Item 68: Make pickle Reliable with copyreg 312
Item 69: Use decimal When Precision Is Paramount 319
Item 70: Profile Before Optimizing 322
Item 71: Prefer deque for Producer?CConsumer Queues 326
Item 72: Consider Searching Sorted Sequences with bisect 334
Item 73: Know How to Use heapq for Priority Queues 336
Item 74: Consider memoryview and bytearray for
Zero-Copy Interactions with bytes 346
Chapter 9 Testing and Debugging 353
Item 75: Use repr Strings for Debugging Output 354
Item 76: Verify Related Behaviors in TestCase Subclasses 357
Item 77: Isolate Tests from Each Other with setUp,
tearDown, setUpModule, and tearDownModule 365
Item 78: Use Mocks to Test Code with
Complex Dependencies 367
Item 79: Encapsulate Dependencies to Facilitate
Mocking and Testing 375
Item 80: Consider Interactive Debugging with pdb 379
Item 81: Use tracemalloc to Understand Memory
Usage and Leaks 384
Chapter 10 Collaboration 389
Item 82: Know Where to Find Community-Built Modules 389
Item 83: Use Virtual Environments for Isolated and
Reproducible Dependencies 390
Item 84: Write Docstrings for Every Function,
Class, and Module 396
Item 85: Use Packages to Organize Modules and
Provide Stable APIs 401
Item 86: Consider Module-Scoped Code to
Configure Deployment Environments 406
Item 87: Define a Root Exception to Insulate
Callers from APIs 408
Item 88: Know How to Break Circular Dependencies 413
Item 89: Consider warnings to Refactor and Migrate Usage 418
Item 90: Consider Static Analysis via typing to Obviate Bugs 425
Index 435
展开全部

Effective Python:改善Python程序的90个建议 (第2版)(英文版) 作者简介

Brett Slatkin,Gooqle公司不错软件工程师。他是Google消费者调查项目的工程主管及联合创始人,曾从事Google App Engine的Python基础架构工作,并利用Python来管理众多的Google服务器。Slatkin也是PubSubHubbub协议的联合创始人,还用Python为GoogIe实现了针对该协议的系统。他拥有哥伦比亚大学计算机工程专业学士学位。
Brett Slaktin,Google首席软件工程师、Google消费者调查项目工程主管及联合创始人、PubSubHubbub 协议联合创始人。他启动了Google第一个云计算产品App Engine。十四年前,他在实习时使用Python管理了Google大量的服务器。在日常工作之余,他喜欢弹钢琴和冲浪。他也喜欢在自己的网站上发布一些编程相关的话题和文章。他拥有纽约市哥伦比亚大学计算机工程学士学位。现居旧金山。

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