Preface

The Python programming language has unique strengths and charms that can be hard to grasp. Many programmers familiar with other languages often approach Python from a limited mindset instead of embracing its full expressivity. Some programmers go too far in the other direction, overusing Python features that can cause big problems later.

This second edition book (to be published by Pearson Addison-Wesley in mid-November 2019) provides insight into the Pythonic way of writing programs: the best way to use Python. It builds on a fundamental understanding of the language that I assume you already have. Novice programmers will learn the best practices of Python’s capabilities. Experienced programmers will learn how to embrace the strangeness of a new tool with confidence.

My goal is to prepare you to make a big impact with Python.

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What This Book Covers

Each chapter in Effective Python: Second Edition contains a broad but related set of items. Feel free to jump between all 90 items and follow your interest. 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.

This second edition is focused exclusively on Python 3, up to and including version 3.8. It contains 30+ completely new items with additional best practices. Most of the original items from the first edition have been revised and included, but many have undergone substantial updates. For some items my advice has completely changed between the two editions of the book due best practices evolving as Python has matured.

If, for whatever reason, you’re still primarily using Python 2, despite its end-of-life on January 1st, 2020, the previous edition of the book (and its translations) may be more useful to you.

Chapter 1: Pythonic Thinking

The Python community has come to use the adjective Pythonic to describe code that follows a particular style. The idioms of Python have emerged over time through experience using the language and working with others. This chapter covers the best way to do the most common things in Python.

1. Know Which Version of Python You’re Using
2. Follow the PEP 8 Style Guide
3. Know the Differences Between bytes and str
4. Prefer Interpolated F-Strings Over C-style Format Strings and str.format
5. Write Helper Functions Instead of Complex Expressions
6. Prefer Multiple Assignment Unpacking Over Indexing
7. Prefer enumerate Over range
8. Use zip to Process Iterators in Parallel
9. Avoid else Blocks After for and while Loops
10. Prevent Repetition with Assignment Expressions

Chapter 2: Lists and Dictionaries

In Python, the most common way to organize information is in a sequence of values stored in a list. A list‘s natural complement is the dict that stores lookup keys mapped to corresponding values. This chapter covers how to build programs with these versatile building blocks.

11. Know How to Slice Sequences
12. Avoid Striding and Slicing in a Single Expression
13. Prefer Catch-All Unpacking Over Slicing
14. Sort by Complex Criteria Using the key Parameter
15. Be Cautious When Relying on dict Insertion Ordering
16. Prefer get Over in and KeyError to Handle Missing Dictionary Keys
17. Prefer defaultdict Over setdefault to Handle Missing Items in Internal State
18. Know How to Construct Key-Dependent Default Values with __missing__

Chapter 3: Functions

Functions in Python have a variety of extra features that make a programmer’s life easier. Some are similar to capabilities in other programming languages, but many are unique to Python. This chapter covers how to use functions to clarify intention, promote reuse, and reduce bugs.

19. Never Unpack More Than Three Variables When Functions Return Multiple Values
20. Prefer Raising Exceptions to Returning None
21. Know How Closures Interact with Variable Scope
22. Reduce Visual Noise with Variable Positional Arguments
23. Provide Optional Behavior with Keyword Arguments
24. Use None and Docstrings to Specify Dynamic Default Arguments
25. Enforce Clarity with Keyword-Only and Position-Only Arguments
26. Define Function Decorators with functools.wraps

Chapter 4: Comprehensions and Generators

Python has special syntax for quickly iterating through lists, dictionaries, and sets to generate derivative data structures. It also allows for a stream of iterable values to be incrementally returned by a function. This chapter covers how these features can provide better performance, reduced memory usage, and improved readability.

27. Use Comprehensions Instead of map and filter
28. Avoid More Than Two Control Subexpressions in Comprehensions
29. Avoid Repeated Work in Comprehensions by Using Assignment Expressions
30. Consider Generators Instead of Returning Lists
31. Be Defensive When Iterating Over Arguments
32. Consider Generator Expressions for Large List Comprehensions
33. Compose Multiple Generators with yield from
34. Avoid Injecting Data into Generators with send
35. Avoid Causing State Transitions in Generators with throw
36. Consider itertools for Working with Iterators and Generators

Chapter 5: Classes and Interfaces

Python is an object-oriented language. Getting things done in Python often requires writing new classes and defining how they interact through their interfaces and hierarchies. This chapter covers how to use classes to express your intended behaviors with objects.

37. Compose Classes Instead of Nesting Many Levels of Built-in Types
38. Accept Functions Instead of Classes for Simple Interfaces
39. Use @classmethod Polymorphism to Construct Objects Generically
40. Initialize Parent Classes with super
41. Consider Composing Functionality with Mix-in Classes
42. Prefer Public Attributes Over Private Ones
43. Inherit from collections.abc for Custom Container Types

Chapter 6: Metaclasses and Attributes

Metaclasses and dynamic attributes are powerful Python features. However, they also enable you to implement extremely bizarre and unexpected behaviors. This chapter covers the common idioms for using these mechanisms to ensure that you follow the rule of least surprise.

44. Use Plain Attributes Instead of Setter and Getter Methods
45. Consider @property Instead of Refactoring Attributes
46. Use Descriptors for Reusable @property Methods
47. Use __getattr__, __getattribute__, and __setattr__ for Lazy Attributes
48. Validate Subclasses with __init_subclass__
49. Register Class Existence with __init_subclass__
50. Annotate Class Attributes with __set_name__
51. Prefer Class Decorators Over Metaclasses for Composable Class Extensions

Chapter 7: Concurrency and Parallelism

Python makes it easy to write concurrent programs that do many different things seemingly at the same time. Python can also be used to do parallel work through system calls, subprocesses, and C extensions. This chapter covers how to best utilize Python in these subtly different situations.

52. Use subprocess to Manage Child Processes
53. Use Threads for Blocking I/O, Avoid for Parallelism
54. Use Lock to Prevent Data Races in Threads
55. Use Queue to Coordinate Work Between Threads
56. Know How to Recognize When Concurrency Is Necessary
57. Avoid Creating New Thread Instances for On-demand Fan-out
58. Understand How Using Queue for Concurrency Requires Refactoring
59. Consider ThreadPoolExecutor When Threads Are Necessary for Concurrency
60. Achieve Highly Concurrent I/O with Coroutines
61. Know How to Port Threaded I/O to asyncio
62. Mix Threads and Coroutines to Ease the Transition to asyncio
63. Avoid Blocking the asyncio Event Loop to Maximize Responsiveness
64. Consider concurrent.futures for True Parallelism

Chapter 8: Robustness and Performance

Python has built-in features and modules that aid in hardening your programs so they are dependable. Python also includes tools to help you achieve higher performance with minimal effort. This chapter covers how to use Python to optimize your programs to maximize their reliability and efficiency in production.

65. Take Advantage of Each Block in try/except/else/finally
66. Consider contextlib and with Statements for Reusable try/finally Behavior
67. Use datetime Instead of time for Local Clocks
68. Make pickle Reliable with copyreg
69. Use decimal When Precision Is Paramount
70. Profile Before Optimizing
71. Prefer deque for Producer–Consumer Queues
72. Consider Searching Sorted Sequences with bisect
73. Know How to Use heapq for Priority Queues
74. Consider memoryview and bytearray for Zero-Copy Interactions with bytes

Chapter 9: Testing and Debugging

You should always test your code, regardless of what language it’s written in. However, Python’s dynamic features can increase the risk of runtime errors in unique ways. Luckily, they also make it easier to write tests and diagnose malfunctioning programs. This chapter covers Python’s built-in tools for testing and debugging.

75. Use repr Strings for Debugging Output
76. Verify Related Behaviors in TestCase Subclasses
77. Isolate Tests from Each Other with setUp, tearDown, setUpModule, and tearDownModule
78. Use Mocks to Test Code with Complex Dependencies
79. Encapsulate Dependencies to Facilitate Mocking and Testing
80. Consider Interactive Debugging with pdb
81. Use tracemalloc to Understand Memory Usage and Leaks

Chapter 10: Collaboration

Collaborating on Python programs requires you to be deliberate about how you write your code. Even if you’re working alone, you’ll want to understand how to use modules written by others. This chapter covers the standard tools and best practices that enable people to work together on Python programs.

82. Know Where to Find Community-Built Modules
83. Use Virtual Environments for Isolated and Reproducible Dependencies
84. Write Docstrings for Every Function, Class, and Module
85. Use Packages to Organize Modules and Provide Stable APIs
86. Consider Module-Scoped Code to Configure Deployment Environments
87. Define a Root Exception to Insulate Callers from APIs
88. Know How to Break Circular Dependencies
89. Consider warnings to Refactor and Migrate Usage
90. Consider Static Analysis via typing to Obviate Bugs

Brett Slatkin

Brett Slatkin is a principal software engineer at Google. He is the technical co-founder of Google Surveys, the co-creator of the PubSubHubbub protocol, and he launched Google’s first cloud computing product (App Engine). Fourteen years ago, he cut his teeth using Python to manage Google’s enormous fleet of servers.

Outside of his day job, he likes to play piano and surf (both poorly). He also enjoys writing about programming-related topics on his personal website. He earned his B.S. in computer engineering from Columbia University in the City of New York. He lives in San Francisco.