Summary

Python is a versatile and powerful language, but leveraging its full potential requires more than just knowing the syntax. Effective Python: 125 Specific Ways to Write Better Python, 3rd Edition is your comprehensive guide to mastering Python’s unique strengths and avoiding its hidden pitfalls. This updated edition (published by Pearson Addison-Wesley in November, 2024) builds on the acclaimed second edition, expanding from 90 to 125 best practices that are essential for writing high-quality Python code.

Drawing on years of experience at Google, Brett Slatkin offers clear, concise, and practical advice for both new and experienced Python developers. Each item in the book provides insight into the “Pythonic” way of programming, helping you understand how to write code that is not only effective but also elegant and maintainable. Whether you’re building web applications, analyzing data, writing automation scripts, or training AI models, this book will equip you with the skills to make a significant impact using Python.

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What This Book Covers

Each chapter in Effective Python contains a broad but related set of items. Each item contains concise and specific guidance explaining how you can write Python programs more effectively. Items include advice on what to do, what to avoid, how to strike the right balance, and why this is the best choice. Items reference each other to make it easier to fill in the gaps as you read. Feel free to jump between items and follow your interest.

This third edition covers the language up through Python version 3.13. This book includes 35 completely new items compared to the second edition, two new chapters focused on robustness and performance, and in-depth coverage of creating C-extension modules and interfacing with native shared libraries. Most of the items from the second edition have been revised and included, but many have undergone substantial updates. For some items, my advice has completely changed (compared to the second edition) due to best practices evolving as Python has matured.

Table of Contents

Chapter 1: Pythonic Thinking

1. Know Which Version of Python You’re Using
2. Follow the PEP 8 Style Guide
3. Never Expect Python to Detect Errors at Compile Time
4. Write Helper Functions Instead of Complex Expressions
5. Prefer Multiple-Assignment Unpacking over Indexing
6. Always Surround Single-Element Tuples with Parentheses
7. Consider Conditional Expressions for Simple Inline Logic
8. Prevent Repetition with Assignment Expressions
9. Consider match for Destructuring in Flow Control; Avoid When if Statements Are Sufficient

Chapter 2: Strings and Slicing

10. Know the Differences Between bytes and str
11. Prefer Interpolated F-Strings over C-Style Format Strings and str.format
12. Understand the Difference Between repr and str when Printing Objects
13. Prefer Explicit String Concatenation over Implicit, Especially in Lists
14. Know How to Slice Sequences
15. Avoid Striding and Slicing in a Single Expression
16. Prefer Catch-All Unpacking over Slicing

Chapter 3: Loops and Iterators

17. Prefer enumerate over range
18. Use zip to Process Iterators in Parallel
19. Avoid else Blocks After for and while Loops
20. Never Use for Loop Variables After the Loop Ends
21. Be Defensive when Iterating over Arguments
22. Never Modify Containers While Iterating over Them; Use Copies or Caches Instead
23. Pass Iterators to any and all for Efficient Short-Circuiting Logic
24. Consider itertools for Working with Iterators and Generators

Chapter 4: Dictionaries

25. Be Cautious when Relying on Dictionary Insertion Ordering
26. Prefer get over in and KeyError to Handle Missing Dictionary Keys
27. Prefer defaultdict over setdefault to Handle Missing Items in Internal State
28. Know How to Construct Key-Dependent Default Values with __missing__
29. Compose Classes Instead of Deeply Nesting Dictionaries, Lists, and Tuples

Chapter 5: Functions

30. Know That Function Arguments Can Be Mutated
31. Return Dedicated Result Objects Instead of Requiring Function Callers to Unpack More Than Three Variables
32. Prefer Raising Exceptions to Returning None
33. Know How Closures Interact with Variable Scope and nonlocal
34. Reduce Visual Noise with Variable Positional Arguments
35. Provide Optional Behavior with Keyword Arguments
36. Use None and Docstrings to Specify Dynamic Default Arguments
37. Enforce Clarity with Keyword-Only and Positional-Only Arguments
38. Define Function Decorators with functools.wraps
39. Prefer functools.partial over lambda Expressions for Glue Functions

Chapter 6: Comprehensions and Generators

40. Use Comprehensions Instead of map and filter
41. Avoid More Than Two Control Subexpressions in Comprehensions
42. Reduce Repetition in Comprehensions with Assignment Expressions
43. Consider Generators Instead of Returning Lists
44. Consider Generator Expressions for Large List Comprehensions
45. Compose Multiple Generators with yield from
46. Pass Iterators into Generators as Arguments Instead of Calling the send Method
47. Manage Iterative State Transitions with a Class Instead of the Generator throw Method

Chapter 7: Classes and Interfaces

48. Accept Functions Instead of Classes for Simple Interfaces
49. Prefer Object-Oriented Polymorphism over Functions with isinstance Checks
50. Consider functools.singledispatch for Functional-Style Programming Instead of Object-Oriented Polymorphism
51. Prefer dataclasses for Defining Lightweight Classes
52. Use @classmethod Polymorphism to Construct Objects Generically
53. Initialize Parent Classes with super
54. Consider Composing Functionality with Mix-in Classes
55. Prefer Public Attributes over Private Ones
56. Prefer dataclasses for Creating Immutable Objects
57. Inherit from collections.abc Classes for Custom Container Types

Chapter 8: Metaclasses and Attributes

58. Use Plain Attributes Instead of Setter and Getter Methods
59. Consider @property Instead of Refactoring Attributes
60. Use Descriptors for Reusable @property Methods
61. Use __getattr__, __getattribute__, and __setattr__ for Lazy Attributes
62. Validate Subclasses with __init_subclass__
63. Register Class Existence with __init_subclass__
64. Annotate Class Attributes with __set_name__
65. Consider Class Body Definition Order to Establish Relationships Between Attributes
66. Prefer Class Decorators over Metaclasses for Composable Class Extensions

Chapter 9: Concurrency and Parallelism

67. Use subprocess to Manage Child Processes
68. Use Threads for Blocking I/O; Avoid for Parallelism
69. Use Lock to Prevent Data Races in Threads
70. Use Queue to Coordinate Work Between Threads
71. Know How to Recognize When Concurrency Is Necessary
72. Avoid Creating New Thread Instances for On-demand Fan-out
73. Understand How Using Queue for Concurrency Requires Refactoring
74. Consider ThreadPoolExecutor When Threads Are Necessary for Concurrency
75. Achieve Highly Concurrent I/O with Coroutines
76. Know How to Port Threaded I/O to asyncio
77. Mix Threads and Coroutines to Ease the Transition to asyncio
78. Maximize Responsiveness of asyncio Event Loops with async-friendly Worker Threads
79. Consider concurrent.futures for True Parallelism

Chapter 10: Robustness

80. Take Advantage of Each Block in try/ except/ else/ finally
81. assert Internal Assumptions and raise Missed Expectations
82. Consider contextlib and with Statements for Reusable try/ finally Behavior
83. Always Make try Blocks as Short as Possible
84. Beware of Exception Variables Disappearing
85. Beware of Catching the Exception Class
86. Understand the Difference Between Exception and BaseException
87. Use traceback for Enhanced Exception Reporting
88. Consider Explicitly Chaining Exceptions to Clarify Tracebacks
89. Always Pass Resources into Generators and Have Callers Clean Them Up Outside
90. Never Set __debug__ to False
91. Avoid exec and eval Unless You’re Building a Developer Tool

Chapter 11: Performance

92. Profile Before Optimizing
93. Optimize Performance-Critical Code Using timeit Microbenchmarks
94. Know When and How to Replace Python with Another Programming Language
95. Consider ctypes to Rapidly Integrate with Native Libraries
96. Consider Extension Modules to Maximize Performance and Ergonomics
97. Rely on Precompiled Bytecode and File System Caching to Improve Startup Time
98. Lazy-Load Modules with Dynamic Imports to Reduce Startup Time
99. Consider memoryview and bytearray for Zero-Copy Interactions with bytes

Chapter 12: Data Structures and Algorithms

100. Sort by Complex Criteria Using the key Parameter
101. Know the Difference Between sort and sorted
102. Consider Searching Sorted Sequences with bisect
103. Prefer deque for Producer-Consumer Queues
104. Know How to Use heapq for Priority Queues
105. Use datetime Instead of time for Local Clocks
106. Use decimal When Precision Is Paramount
107. Make pickle Serialization Maintainable with copyreg

Chapter 13: Testing and Debugging

108. Verify Related Behaviors in TestCase Subclasses
109. Prefer Integration Tests over Unit Tests
110. Isolate Tests From Each Other with setUp, tearDown, setUpModule, and tearDownModule
111. Use Mocks to Test Code with Complex Dependencies
112. Encapsulate Dependencies to Facilitate Mocking and Testing
113. Use assertAlmostEqual to Control Precision in Floating Point Tests
114. Consider Interactive Debugging with pdb
115. Use tracemalloc to Understand Memory Usage and Leaks

Chapter 14: Collaboration

116. Know Where to Find Community-Built Modules
117. Use Virtual Environments for Isolated and Reproducible Dependencies
118. Write Docstrings for Every Function, Class, and Module
119. Use Packages to Organize Modules and Provide Stable APIs
120. Consider Module-Scoped Code to Configure Deployment Environments
121. Define a Root Exception to Insulate Callers from APIs
122. Know How to Break Circular Dependencies
123. Consider warnings to Refactor and Migrate Usage
124. Consider Static Analysis via typing to Obviate Bugs
125. Prefer Open Source Projects for Bundling Python Programs over zipimport and zipapp

Previous editions

If, for whatever reason, you’re still primarily using Python 2, despite its end-of-life in April, 2020, the first edition of the book will be more useful to you. For older versions of Python 3, the second edition of this book might be useful.

Brett Slatkin has been programming with Python professionally for the past 19 years. He currently works as a principal software engineer in the Office of the CTO at Google, developing technology strategies and rapid prototypes.

His prior experience includes founding Google Surveys, an internal startup for collecting machine learning and market research data sets; launching Google App Engine, the company’s first cloud computing product; scaling Google’s A/B experimentation products to billions of users; co-creating PubSubHubbub, the W3C standard for real-time RSS feeds; and making various contributions to open source projects.

Brett earned a bachelor’s degree in computer engineering from Columbia University in the City of New York. Outside of his day job, he enjoys playing piano, surfing, and spending time with his family. He lives in California.