Effective Python: 125 Specific Ways to Write Better Python(Effective Software Development Series)

Master the art of Python programming with 125 actionable best practices to write more efficient, readable, and maintainable code.   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 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.   Key Features of the 3rd Edition: Expanded Content: Now with 125 actionable guidelines, including 35 entirely new items. Updated Best Practices: Reflects the latest features in Python releases up to version 3.13. New Chapters: Additional chapters on how to build robust programs that achieve high performance. Advanced Topics: In-depth coverage of creating C-extension modules and interfacing with native shared libraries. Practical Examples: Realistic code examples that illustrate each best practice.

Table of Contents:
Preface     xvii Acknowledgments     xxiii About the Author     xxv   Chapter 1: Pythonic Thinking     1      Item 1: Know Which Version of Python You’re Using     1      Item 2: Follow the PEP 8 Style Guide     3      Item 3: Never Expect Python to Detect Errors at Compile Time     6      Item 4: Write Helper Functions Instead of Complex Expressions     8      Item 5: Prefer Multiple-Assignment Unpacking Over Indexing     11      Item 6: Always Surround Single-Element Tuples with Parentheses     16      Item 7: Consider Conditional Expressions for Simple Inline Logic     19      Item 8: Prevent Repetition with Assignment Expressions     24      Item 9: Consider match for Destructuring in Flow Control; Avoid When if Statements Are Sufficient     30   Chapter 2: Strings and Slicing     41      Item 10: Know the Differences Between bytes and str     41      Item 11: Prefer Interpolated F-Strings over C-Style Format Strings and str.format     47      Item 12: Understand the Difference Between  repr and str when Printing Objects     58      Item 13: Prefer Explicit String Concatenation over Implicit, Especially in Lists     62      Item 14: Know How to Slice Sequences     67      Item 15: Avoid Striding and Slicing in a Single Expression     70      Item 16: Prefer Catch-All Unpacking Over Slicing     72   Chapter 3: Loops and Iterators     77      Item 17: Prefer enumerate over range     77      Item 18: Use zip to Process Iterators in Parallel     79      Item 19: Avoid else Blocks After for and while Loops     82      Item 20: Never Use for Loop Variables After the Loop Ends     85      Item 21: Be Defensive when Iterating over Arguments     87      Item 22: Never Modify Containers While Iterating over Them; Use Copies or Caches Instead     92      Item 23: Pass Iterators to any and all for Efficient Short-Circuiting Logic     98      Item 24: Consider itertools for Working with Iterators and Generators     102   Chapter 4: Dictionaries     109      Item 25: Be Cautious when Relying on Dictionary Insertion Ordering     109      Item 26: Prefer get over in and KeyError to Handle Missing Dictionary Keys     117      Item 27: Prefer defaultdict over setdefault to Handle Missing Items in Internal State     122      Item 28: Know How to Construct Key-Dependent Default Values with __missing__     124      Item 29: Compose Classes Instead of Deeply Nesting Dictionaries, Lists, and Tuples     127   Chapter 5: Functions     135      Item 30: Know That Function Arguments Can Be Mutated     135      Item 31: Return Dedicated Result Objects Instead of Requiring Function Callers to Unpack More Than Three Variables     138      Item 32: Prefer Raising Exceptions to Returning None     142      Item 33: Know How Closures Interact with Variable Scope and nonlocal     145      Item 34: Reduce Visual Noise with Variable Positional Arguments     150      Item 35: Provide Optional Behavior with Keyword Arguments     153      Item 36: Use None and Docstrings to Specify Dynamic Default Arguments     157      Item 37: Enforce Clarity with Keyword-Only and Positional-Only Arguments     161      Item 38: Define Function Decorators with functools.wraps     166      Item 39: Prefer functools.partial over lambda Expressions for Glue Functions     169   Chapter 6: Comprehensions and Generators     173      Item 40: Use Comprehensions Instead of map and filter     173      Item 41: Avoid More Than Two Control Subexpressions in Comprehensions     176      Item 42: Reduce Repetition in Comprehensions with Assignment Expressions     178      Item 43: Consider Generators Instead of Returning Lists     182      Item 44: Consider Generator Expressions for Large List Comprehensions     184      Item 45: Compose Multiple Generators with yield from     186      Item 46: Pass Iterators into Generators as Arguments Instead of Calling the send Method     188      Item 47: Manage Iterative State Transitions with a Class Instead of the Generator throw Method     195   Chapter 7: Classes and Interfaces     201      Item 48: Accept Functions Instead of Classes for Simple Interfaces     201      Item 49: Prefer Object-Oriented Polymorphism over Functions with isinstance Checks     205      Item 50: Consider functools.singledispatch for Functional-Style Programming Instead of Object-Oriented Polymorphism     210      Item 51: Prefer dataclasses for Defining Lightweight Classes     217      Item 52: Use @classmethod Polymorphism to Construct Objects Generically     230      Item 53: Initialize Parent Classes with super     235      Item 54: Consider Composing Functionality with Mix-in Classes     240      Item 55: Prefer Public Attributes over Private Ones     245      Item 56: Prefer dataclasses for Creating Immutable Objects     250      Item 57: Inherit from collections.abc Classes for Custom Container Types     260   Chapter 8: Metaclasses and Attributes     265      Item 58: Use Plain Attributes Instead of Setter and Getter Methods     265      Item 59: Consider @property Instead of Refactoring Attributes     270      Item 60: Use Descriptors for Reusable @property Methods     274      Item 61: Use __getattr__, __getattribute__, and __setattr__ for Lazy Attributes     279      Item 62: Validate Subclasses with __init_subclass__     285      Item 63: Register Class Existence with __init_subclass__     293      Item 64: Annotate Class Attributes with __set_name__     299      Item 65: Consider Class Body Definition Order to Establish Relationships Between Attributes     303      Item 66: Prefer Class Decorators over Metaclasses for Composable Class Extensions     310   Chapter 9: Concurrency and Parallelism     319      Item 67: Use subprocess to Manage Child Processes     320      Item 68: Use Threads for Blocking I/O; Avoid for Parallelism     324      Item 69: Use Lock to Prevent Data Races in Threads     330      Item 70: Use Queue to Coordinate Work Between Threads     333      Item 71: Know How to Recognize When Concurrency Is Necessary     344      Item 72: Avoid Creating New Thread Instances for On-Demand Fan-out     349      Item 73: Understand How Using Queue for Concurrency Requires Refactoring     353      Item 74: Consider ThreadPoolExecutor When Threads Are Necessary for Concurrency     361      Item 75: Achieve Highly Concurrent I/O with Coroutines     364      Item 76: Know How to Port Threaded I/O to asyncio     368      Item 77: Mix Threads and Coroutines to Ease the Transition to asyncio     381      Item 78: Maximize Responsiveness of asyncio Event Loops with async-friendly Worker Threads     389      Item 79: Consider concurrent.futures for True Parallelism     393   Chapter 10: Robustness     399      Item 80: Take Advantage of Each Block in try/except/else/finally     399      Item 81: assert Internal Assumptions and raise Missed Expectations     404      Item 82: Consider contextlib and with Statements for Reusable try/finally Behavior     408      Item 83: Always Make try Blocks as Short as Possible     412      Item 84: Beware of Exception Variables Disappearing     414      Item 85: Beware of Catching the Exception Class     416      Item 86: Understand the Difference Between Exception and BaseException     419      Item 87: Use traceback for Enhanced Exception Reporting     424      Item 88: Consider Explicitly Chaining Exceptions to Clarify Tracebacks     428      Item 89: Always Pass Resources into Generators and Have Callers Clean Them Up Outside     436      Item 90: Never Set __debug__ to False     442      Item 91: Avoid exec and eval Unless You’re Building a Developer Tool     445   Chapter 11: Performance     447      Item 92: Profile Before Optimizing     448      Item 93: Optimize Performance-Critical Code Using timeit Microbenchmarks     453      Item 94: Know When and How to Replace Python with Another Programming Language     458      Item 95: Consider ctypes to Rapidly Integrate with Native Libraries     462      Item 96: Consider Extension Modules to Maximize Performance and Ergonomics     467      Item 97: Rely on Precompiled Bytecode and File System Caching to Improve Startup Time     475      Item 98: Lazy-Load Modules with Dynamic Imports to Reduce Startup Time     478      Item 99: Consider memoryview and bytearray for Zero-Copy Interactions with bytes     485   Chapter 12: Data Structures & Algorithms     493      Item 100: Sort by Complex Criteria Using the key Parameter     493      Item 101: Know the Difference Between sort and sorted     499      Item 102: Consider Searching Sorted Sequences with bisect     501      Item 103: Prefer deque for Producer-Consumer Queues     504      Item 104: Know How to Use heapq for Priority Queues     509      Item 105: Use datetime Instead of time for Local Clocks     519      Item 106: Use decimal When Precision Is Paramount     523      Item 107: Make pickle Serialization Maintainable with copyreg     526   Chapter 13: Testing and Debugging     533      Item 108: Verify Related Behaviors in TestCase Subclasses     533      Item 109: Prefer Integration Tests over Unit Tests     541      Item 110: Isolate Tests From Each Other with setUp, tearDown, setUpModule, and tearDownModule     547      Item 111: Use Mocks to Test Code with Complex Dependencies     550      Item 112: Encapsulate Dependencies to Facilitate Mocking and Testing     559      Item 113: Use assertAlmostEqual to Control Precision in Floating Point Tests     563      Item 114: Consider Interactive Debugging with pdb     565      Item 115: Use tracemalloc to Understand Memory Usage and Leaks     570   Chapter 14: Collaboration     575      Item 116: Know Where to Find Community-Built Modules     575      Item 117: Use Virtual Environments for Isolated and Reproducible Dependencies     576      Item 118: Write Docstrings for Every Function, Class, and Module     582      Item 119: Use Packages to Organize Modules and Provide Stable APIs     588      Item 120: Consider Module-Scoped Code to Configure Deployment Environments     593      Item 121: Define a Root Exception to Insulate Callers from APIs     595      Item 122: Know How to Break Circular Dependencies     600      Item 123: Consider warnings to Refactor and Migrate Usage     605      Item 124: Consider Static Analysis via typing to Obviate Bugs     613      Item 125: Prefer Open Source Projects for Bundling Python Programs over zipimport and zipapp     621   Index     627

About the Author :
Brett Slatkin is a Principal Software Engineer at Google in the Office of the CTO, focusing on emerging technologies. He co-founded Google Surveys, launched Google Cloud’s first product (App Engine), and co-created the PubSubHubbub protocol—all using Python. Brett has been writing Python code professionally for the past 19 years and has made numerous contributions to open-source projects.