book

Effective Python: 125 Specific Ways to Write Better Python, 3rd Edition

Name: Effective Python: 125 Specific Ways to Write Better Python, 3rd Edition
Author: Brett Slatkin
ISBN: 9780138172398

by Brett Slatkin

November 2024

Intermediate to advanced

672 pages

21h 1m

English

Addison-Wesley Professional

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Includes

Sandbox

Cover Page
About This eBook
Halftitle Page
Title Page
Copyright Page
Dedication Page
Contents at a Glance
Contents
Preface
What This Book CoversConventions Used in This Book
Acknowledgments

About the Author
Chapter 1. Pythonic Thinking
Item 1: Know Which Version of Python You’re UsingItem 2: Follow the PEP 8 Style GuideItem 3: Never Expect Python to Detect Errors at Compile TimeItem 4: Write Helper Functions Instead of Complex ExpressionsItem 5: Prefer Multiple-Assignment Unpacking over IndexingItem 6: Always Surround Single-Element Tuples with ParenthesesItem 7: Consider Conditional Expressions for Simple Inline LogicItem 8: Prevent Repetition with Assignment ExpressionsItem 9: Consider match for Destructuring in Flow Control; Avoid When if Statements Are Sufficient
Chapter 2. Strings and Slicing
Item 10: Know the Differences Between bytes and strItem 11: Prefer Interpolated F-Strings over C-Style Format Strings and str.formatItem 12: Understand the Difference Between repr and str when Printing ObjectsItem 13: Prefer Explicit String Concatenation over Implicit, Especially in ListsItem 14: Know How to Slice SequencesItem 15: Avoid Striding and Slicing in a Single ExpressionItem 16: Prefer Catch-All Unpacking over Slicing
Chapter 3. Loops and Iterators
Item 17: Prefer enumerate over rangeItem 18: Use zip to Process Iterators in ParallelItem 19: Avoid else Blocks After for and while LoopsItem 20: Never Use for Loop Variables After the Loop EndsItem 21: Be Defensive when Iterating over ArgumentsItem 22: Never Modify Containers While Iterating over Them; Use Copies or Caches InsteadItem 23: Pass Iterators to any and all for Efficient Short-Circuiting LogicItem 24: Consider itertools for Working with Iterators and Generators
Chapter 4. Dictionaries
Item 25: Be Cautious when Relying on Dictionary Insertion OrderingItem 26: Prefer get over in and KeyError to Handle Missing Dictionary KeysItem 27: Prefer defaultdict over setdefault to Handle Missing Items in Internal StateItem 28: Know How to Construct Key-Dependent Default Values with __missing__Item 29: Compose Classes Instead of Deeply Nesting Dictionaries, Lists, and Tuples
Chapter 5. Functions
Item 30: Know That Function Arguments Can Be MutatedItem 31: Return Dedicated Result Objects Instead of Requiring Function Callers to Unpack More Than Three VariablesItem 32: Prefer Raising Exceptions to Returning NoneItem 33: Know How Closures Interact with Variable Scope and nonlocalItem 34: Reduce Visual Noise with Variable Positional ArgumentsItem 35: Provide Optional Behavior with Keyword ArgumentsItem 36: Use None and Docstrings to Specify Dynamic Default ArgumentsItem 37: Enforce Clarity with Keyword-Only and Positional-Only ArgumentsItem 38: Define Function Decorators with functools.wrapsItem 39: Prefer functools.partial over lambda Expressions for Glue Functions
Chapter 6. Comprehensions and Generators
Item 40: Use Comprehensions Instead of map and filterItem 41: Avoid More Than Two Control Subexpressions in ComprehensionsItem 42: Reduce Repetition in Comprehensions with Assignment ExpressionsItem 43: Consider Generators Instead of Returning ListsItem 44: Consider Generator Expressions for Large List ComprehensionsItem 45: Compose Multiple Generators with yield fromItem 46: Pass Iterators into Generators as Arguments Instead of Calling the send MethodItem 47: Manage Iterative State Transitions with a Class Instead of the Generator throw Method
Chapter 7. Classes and Interfaces
Item 48: Accept Functions Instead of Classes for Simple InterfacesItem 49: Prefer Object-Oriented Polymorphism over Functions with isinstance ChecksItem 50: Consider functools.singledispatch for Functional-Style Programming Instead of Object-Oriented PolymorphismItem 51: Prefer dataclasses for Defining Lightweight ClassesItem 52: Use @classmethod Polymorphism to Construct Objects GenericallyItem 53: Initialize Parent Classes with superItem 54: Consider Composing Functionality with Mix-in ClassesItem 55: Prefer Public Attributes over Private OnesItem 56: Prefer dataclasses for Creating Immutable ObjectsItem 57: Inherit from collections.abc Classes for Custom Container Types
Chapter 8. Metaclasses and Attributes
Item 58: Use Plain Attributes Instead of Setter and Getter MethodsItem 59: Consider @property Instead of Refactoring AttributesItem 60: Use Descriptors for Reusable @property MethodsItem 61: Use __getattr__, __getattribute__, and __setattr__ for Lazy AttributesItem 62: Validate Subclasses with __init_subclass__Item 63: Register Class Existence with __init_subclass__Item 64: Annotate Class Attributes with __set_name__Item 65: Consider Class Body Definition Order to Establish Relationships Between AttributesItem 66: Prefer Class Decorators over Metaclasses for Composable Class Extensions
Chapter 9. Concurrency and Parallelism
Item 67: Use subprocess to Manage Child ProcessesItem 68: Use Threads for Blocking I/O; Avoid for ParallelismItem 69: Use Lock to Prevent Data Races in ThreadsItem 70: Use Queue to Coordinate Work Between ThreadsItem 71: Know How to Recognize When Concurrency Is NecessaryItem 72: Avoid Creating New Thread Instances for On-Demand Fan-outItem 73: Understand How Using Queue for Concurrency Requires RefactoringItem 74: Consider ThreadPoolExecutor When Threads Are Necessary for ConcurrencyItem 75: Achieve Highly Concurrent I/O with CoroutinesItem 76: Know How to Port Threaded I/O to asyncioItem 77: Mix Threads and Coroutines to Ease the Transition to asyncioItem 78: Maximize Responsiveness of asyncio Event Loops with async-Friendly Worker ThreadsItem 79: Consider concurrent.futures for True Parallelism
Chapter 10. Robustness
Item 80: Take Advantage of Each Block in try/except/else/finallyItem 81: assert Internal Assumptions and raise Missed ExpectationsItem 82: Consider contextlib and with Statements for Reusable try/finally BehaviorItem 83: Always Make try Blocks as Short as PossibleItem 84: Beware of Exception Variables DisappearingItem 85: Beware of Catching the Exception ClassItem 86: Understand the Difference Between Exception and BaseExceptionItem 87: Use traceback for Enhanced Exception ReportingItem 88: Consider Explicitly Chaining Exceptions to Clarify TracebacksItem 89: Always Pass Resources into Generators and Have Callers Clean Them Up OutsideItem 90: Never Set __debug__ to FalseItem 91: Avoid exec and eval Unless You’re Building a Developer Tool
Chapter 11. Performance
Item 92: Profile Before OptimizingItem 93: Optimize Performance-Critical Code Using timeit MicrobenchmarksItem 94: Know When and How to Replace Python with Another Programming LanguageItem 95: Consider ctypes to Rapidly Integrate with Native LibrariesItem 96: Consider Extension Modules to Maximize Performance and ErgonomicsItem 97: Rely on Precompiled Bytecode and File System Caching to Improve Startup TimeItem 98: Lazy-Load Modules with Dynamic Imports to Reduce Startup TimeItem 99: Consider memoryview and bytearray for Zero-Copy Interactions with bytes
Chapter 12. Data Structures and Algorithms
Item 100: Sort by Complex Criteria Using the key ParameterItem 101: Know the Difference Between sort and sortedItem 102: Consider Searching Sorted Sequences with bisectItem 103: Prefer deque for Producer–Consumer QueuesItem 104: Know How to Use heapq for Priority QueuesItem 105: Use datetime Instead of time for Local ClocksItem 106: Use decimal when Precision Is ParamountItem 107: Make pickle Serialization Maintainable with copyreg
Chapter 13. Testing and Debugging
Item 108: Verify Related Behaviors in TestCase SubclassesItem 109: Prefer Integration Tests over Unit TestsItem 110: Isolate Tests from Each Other with setUp, tearDown, setUpModule, and tearDownModuleItem 111: Use Mocks to Test Code with Complex DependenciesItem 112: Encapsulate Dependencies to Facilitate Mocking and TestingItem 113: Use assertAlmostEqual to Control Precision in Floating Point TestsItem 114: Consider Interactive Debugging with pdbItem 115: Use tracemalloc to Understand Memory Usage and Leaks
Chapter 14. Collaboration
Item 116: Know Where to Find Community-Built ModulesItem 117: Use Virtual Environments for Isolated and Reproducible DependenciesItem 118: Write Docstrings for Every Function, Class, and ModuleItem 119: Use Packages to Organize Modules and Provide Stable APIsItem 120: Consider Module-Scoped Code to Configure Deployment EnvironmentsItem 121: Define a Root Exception to Insulate Callers from APIsItem 122: Know How to Break Circular DependenciesItem 123: Consider warnings to Refactor and Migrate UsageItem 124: Consider Static Analysis via typing to Obviate BugsItem 125: Prefer Open Source Projects for Bundling Python Programs over zipimport and zipapp
Index
Code Snippets

Content preview from Effective Python: 125 Specific Ways to Write Better Python, 3rd Edition

3 Loops and Iterators

Programs often need to process through sequential data—of fixed or dynamic length. As a primarily imperative programming language, Python makes it easy to implement sequential processing using loops. This is the general pattern: On each pass through a loop, read data—stored in variables, lists, dictionaries, and so on—and carry out corresponding state modifications or I/O operations. Loops in Python feel natural and capable for the most common tasks involving built-in data types, container types, and user-defined classes.

Python also supports iterators, which enable a more functional-style approach to processing arbitrary streams of data. Instead of directly interacting with how the sequential data is stored, you can use ...

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Effective Python: 90 Specific Ways to Write Better Python, 2nd Edition

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Effective Python: 125 Specific Ways to Write Better Python, 3rd Edition

by Brett Slatkin

3

Loops and Iterators

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