Overview

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.

Full Product Details

Author:   Brett Slatkin
Publisher:   Pearson Education (US)
Imprint:   Addison Wesley
Edition:   3rd edition
Dimensions:   Width: 17.70cm , Height: 3.20cm , Length: 23.10cm
Weight:   1.214kg
ISBN:  

9780138172183

ISBN 10:   0138172188
Pages:   672
Publication Date:   26 February 2025
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   Available To Order  
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Table of Contents

Chapter 1: Pythonic Thinking Item   1: Know Which Version of Python You’re Using Item   2: Follow the PEP 8 Style Guide Item   3: Never Expect Python to Detect Errors at Compile Time Item   4: Write Helper Functions Instead of Complex Expressions Item   5: Prefer Multiple Assignment Unpacking Over Indexing Item   6: Always Surround Single-Element Tuples with Parentheses Item   7: Consider Conditional Expressions for Simple Inline if Statements Item   8: Prevent Repetition with Assignment Expressions Item   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 str Item  11: Prefer Interpolated F-Strings Over C-style Format Strings and str.format Item  12: Understand the Difference Between  repr and str When Printing Objects Item  13: Prefer Explicit String Concatenation Over Implicit, Especially In Lists Item  14: Know How to Slice Sequences Item  15: Avoid Striding and Slicing in a Single Expression Item  16: Prefer Catch-All Unpacking Over Slicing Chapter 3: Loops and Iterators Item  17: Prefer enumerate Over range Item  18: Use zip to Process Iterators in Parallel Item  19: Avoid else Blocks After for and while Loops Item  20: Never Use for Loop Variables After the Loop Ends Item  21: Be Defensive When Iterating Over Arguments Item  22: Never Modify Containers While Iterating Over Them, Use Copies or Caches Instead Item  23: Pass Iterators to any and all for Efficient Short-Circuiting Logic Item  24: Consider itertools for Working with Iterators and Generators Chapter 4: Dictionaries Item  25: Be Cautious When Relying on Dictionary Insertion Ordering Item  26: Prefer get Over in and KeyError to Handle Missing Dictionary Keys Item  27: Prefer defaultdict Over setdefault to Handle Missing Items in Internal State Item  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 Mutated Item  31: Return Dedicated Result Objects Instead of Requiring Function Callers to Unpack More Than Three Variables Item  32: Prefer Raising Exceptions to Returning None Item  33: Know How Closures Interact with Variable Scope and nonlocal Item  34: Reduce Visual Noise with Variable Positional Arguments Item  35: Provide Optional Behavior with Keyword Arguments Item  36: Use None and Docstrings to Specify Dynamic Default Arguments Item  37: Enforce Clarity with Keyword-Only and Position-Only Arguments Item  38: Define Function Decorators with functools.wraps Item  39: Prefer functools.partial Over lambda Expressions For Glue Functions Chapter 6: Comprehensions and Generators Item  40: Use Comprehensions Instead of map and filter Item  41: Avoid More Than Two Control Subexpressions in Comprehensions Item  42: Reduce Repetition in Comprehensions with Assignment Expressions Item  43: Consider Generators Instead of Returning Lists Item  44: Consider Generator Expressions for Large List Comprehensions Item  45: Compose Multiple Generators with yield from Item  46: Pass Iterators into Generators as Arguments Instead of Calling the send Method Item  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 Interfaces Item  49: Prefer Object-Oriented Programming Over isinstance Checks Item  50: Consider functools.singledispatch for Functional-Style Programming Instead of Object-Oriented Polymorphism Item  51: Prefer dataclasses For Defining Light-weight Classes Item  52: Use @classmethod Polymorphism to Construct Objects Generically Item  53: Initialize Parent Classes with super Item  54: Consider Composing Functionality with Mix-in Classes Item  55: Prefer Public Attributes Over Private Ones Item  56: Prefer dataclasses for Creating Immutable Objects Item  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 Methods Item  59: Consider @property Instead of Refactoring Attributes Item  60: Use Descriptors for Reusable @property Methods Item  61: Use __getattr__, __getattribute__, and __setattr__ for Lazy Attributes Item  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 Sequential Relationships Between Attributes Item  66: Prefer Class Decorators Over Metaclasses for Composable Class Extensions Chapter 9: Concurrency and Parallelism Item  67: Use subprocess to Manage Child Processes Item  68: Use Threads for Blocking I/O, Avoid for Parallelism Item  69: Use Lock to Prevent Data Races in Threads Item  70: Use Queue to Coordinate Work Between Threads Item  71: Know How to Recognize When Concurrency Is Necessary Item  72: Avoid Creating New Thread Instances for On-demand Fan-out Item  73: Understand How Using Queue for Concurrency Requires Refactoring Item  74: Consider ThreadPoolExecutor When Threads Are Necessary for Concurrency Item  75: Achieve Highly Concurrent I/O with Coroutines Item  76: Know How to Port Threaded I/O to asyncio Item  77: Mix Threads and Coroutines to Ease the Transition to asyncio Item  78: Maximize Responsiveness of asyncio Event Loops with async-friendly Worker Threads Item  79: Consider concurrent.futures for True Parallelism Chapter 10: Robustness Item  80: Take Advantage of Each Block in try/except/else/finally Item  81: assert Internal Assumptions, raise Missed Expectations Item  82: Consider contextlib and with Statements for Reusable try/finally Behavior Item  83: Always Make try Blocks as Short as Possible Item  84: Beware of Exception Variables Disappearing Item  85: Beware of Catching the Exception Class Item  86: Understand the Difference Between Exception and BaseException Item  87: Use traceback for Enhanced Exception Reporting Item  88: Consider Explicitly Chaining Exceptions to Clarify Tracebacks Item  89: Always Pass Resources into Generators and Have Callers Clean Them Up Outside Item  90: Never Set __debug__ to False Item  91: Avoid exec and eval Unless Youre Building a Developer Tool Chapter 11: Performance Item  92: Profile Before Optimizing Item  93: Optimize Performance-Critical Code Using timeit Microbenchmarks Item  94: Know When and How to Replace Python with Another Programming Language Item  95: Consider ctypes to Rapidly Integrate with Native Libraries Item  96: Consider Extension Modules to Maximize Capabilities and Ergonomics Item  97: Rely on Precompiled Bytecode and File System Caching to Improve Startup Time Item  98: Lazy-load Modules with Dynamic Imports to Reduce Startup Time Item  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 Parameter Item 101: Know the Difference Between sort and sorted Item 102: Consider Searching Sorted Sequences with bisect Item 103: Know How to Use heapq for Priority Queues Item 104: Prefer deque for Producer-Consumer Queues Item 105: Use datetime Instead of time for Local Clocks Item 106: Use decimal When Precision is Paramount Item 107: Make pickle Reliable with copyreg Chapter 13: Testing and Debugging Item 108: Verify Related Behaviors in TestCase Subclasses Item 109: Prefer Integration Tests Over Unit Tests Item 110: Isolate Tests From Each Other with setUp, tearDown, setUpModule, and tearDownModule Item 111: Use Mocks to Test Code with Complex Dependencies Item 112: Encapsulate Dependencies to Facilitate Mocking and Testing Item 113: Use assertAlmostEqual to Control Precision in Floating Point Tests Item 114: Consider Interactive Debugging with pdb Item 115: Use tracemalloc to Understand Memory Usage and Leaks Chapter 14: Collaboration Item 116: Know Where to Find Community-Built Modules Item 117: Use Virtual Environments for Isolated and Reproducible Dependencies Item 118: Write Docstrings for Every Function, Class, and Module Item 119: Use Packages to Organize Modules and Provide Stable APIs Item 120: Consider Module-Scoped Code to Configure Deployment Environments Item 121: Define a Root Exception to Insulate Callers from APIs Item 122: Know How to Break Circular Dependencies Item 123: Consider warnings to Refactor and Migrate Usage Item 124: Consider Static Analysis via typing to Obviate Bugs Item 125: Prefer Open Source Projects for Bundling Python Programs Over zipimport and zipapp

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Author Information

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.

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