Effective Python

Description

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.

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.

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

Additional information

Series	Effective Software Development Series
Imprint	Addison-Wesley Professional
Format	gen
ISBN-13	09780138172183
ISBN-10	0138172188
Author	Brett Slatkin
Subjects	professional, higher education, Employability, IT Professional, Y-AL PROGRAMMING LANG