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Updated and Expanded for Python 3

It’s easy to start developing programs with Python, which is why the language is so popular. However, Python’s unique strengths, charms, and expressiveness can be hard to grasp, and there are hidden pitfalls that can easily trip you up.

This second edition of Effective Python will help you master a truly “Pythonic” approach to programming, harnessing Python’s full power to write exceptionally robust and well-performing code. Using the concise, scenario-driven style pioneered in Scott Meyers’ best-selling Effective C++, Brett Slatkin brings together 90 Python best practices, tips, and shortcuts, and explains them with realistic code examples so that you can embrace Python with confidence.

Drawing on years of experience building Python infrastructure at Google, Slatkin uncovers little-known quirks and idioms that powerfully impact code behavior and performance. You’ll understand the best way to accomplish key tasks so you can write code that’s easier to understand, maintain, and improve. In addition to even more advice, this new edition substantially revises all items from the first edition to reflect how best practices have evolved.

Key features include

30 new actionable guidelines for all major areas of Python

Detailed explanations and examples of statements, expressions, and built-in types

Best practices for writing functions that clarify intention, promote reuse, and avoid bugs

Better techniques and idioms for using comprehensions and generator functions

Coverage of how to accurately express behaviors with classes and interfaces

Guidance on how to avoid pitfalls with metaclasses and dynamic attributes

More efficient and clear approaches to concurrency and parallelism

Solutions for optimizing and hardening to maximize performance and quality

Techniques and built-in modules that aid in debugging and testing

Tools and best practices for collaborative development

Effective Python will prepare growing programmers to make a big impact using Python.

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 (https://onebigfluke.com). He earned his B.S. in computer engineering from Columbia University in the City of New York. He lives in San Francisco.

Preface
Acknowledgments
About the Author
1. Pythonic Thinking
Item 1: Know Which Version of Python You’re Using
Item 2: Follow the PEP 8 Style Guide
Item 3: Know the Differences Between bytes and str
Item 4: Prefer Interpolated F-Strings Over C-style Format Strings and str.format
Item 5: Write Helper Functions Instead of Complex Expressions
Item 6: Prefer Multiple Assignment Unpacking Over Indexing
Item 7: Prefer enumerate Over range
Item 8: Use zip to Process Iterators in Parallel
Item 9: Avoid else Blocks After for and while Loops
Item 10: Prevent Repetition with Assignment Expressions
2. Lists and Dictionaries
Item 11: Know How to Slice Sequences
Item 12: Avoid Striding and Slicing in a Single Expression
Item 13: Prefer Catch-All Unpacking Over Slicing
Item 14: Sort by Complex Criteria Using the key Parameter
Item 15: Be Cautious When Relying on dict Insertion Ordering
Item 16: Prefer get Over in and KeyError to Handle Missing Dictionary Keys
Item 17: Prefer defaultdict Over setdefault to Handle Missing Items in Internal State
Item 18: Know How to Construct Key-Dependent Default Values with __missing__
3. Functions
Item 19: Never Unpack More Than Three Variables When Functions Return Multiple Values
Item 20: Prefer Raising Exceptions to Returning None
Item 21: Know How Closures Interact with Variable Scope
Item 22: Reduce Visual Noise with Variable Positional Arguments
Item 23: Provide Optional Behavior with Keyword Arguments
Item 24: Use None and Docstrings to Specify Dynamic Default Arguments
Item 25: Enforce Clarity with Keyword-Only and Positional-Only Arguments
Item 26: Define Function Decorators with functools.wraps
4. Comprehensions and Generators
Item 27: Use Comprehensions Instead of map and filter
Item 28: Avoid More Than Two Control Subexpressions in Comprehensions
Item 29: Avoid Repeated Work in Comprehensions by Using Assignment Expressions
Item 30: Consider Generators Instead of Returning Lists
Item 31: Be Defensive When Iterating Over Arguments
Item 32: Consider Generator Expressions for Large List Comprehensions
Item 33: Compose Multiple Generators with yield from
Item 34: Avoid Injecting Data into Generators with send
Item 35: Avoid Causing State Transitions in Generators with throw
Item 36: Consider itertools for Working with Iterators and Generators
5. Classes and Interfaces
Item 37: Compose Classes Instead of Nesting Many Levels of Built-in Types
Item 38: Accept Functions Instead of Classes for Simple Interfaces
Item 39: Use @classmethod Polymorphism to Construct Objects Generically
Item 40: Initialize Parent Classes with super
Item 41: Consider Composing Functionality with Mix-in Classes
Item 42: Prefer Public Attributes Over Private Ones
Item 43: Inherit from collections.abc for Custom Container Types
6. Metaclasses and Attributes
Item 44: Use Plain Attributes Instead of Setter and Getter Methods
Item 45: Consider @property Instead of Refactoring Attributes
Item 46: Use Descriptors for Reusable @property Methods
Item 47: Use __getattr__, __getattribute__, and __setattr__ for Lazy Attributes
Item 48: Validate Subclasses with __init_subclass__
Item 49: Register Class Existence with __init_subclass__
Item 50: Annotate Class Attributes with __set_name__
Item 51: Prefer Class Decorators Over Metaclasses for Composable Class Extensions
7. Concurrency and Parallelism
Item 52: Use subprocess to Manage Child Processes
Item 53: Use Threads for Blocking I/O, Avoid for Parallelism
Item 54: Use Lock to Prevent Data Races in Threads
Item 55: Use Queue to Coordinate Work Between Threads
Item 56: Know How to Recognize When Concurrency Is Necessary
Item 57: Avoid Creating New Thread Instances for On-demand Fan-out
Item 58: Understand How Using Queue for Concurrency Requires Refactoring
Item 59: Consider ThreadPoolExecutor When Threads Are Necessary for Concurrency
Item 60: Achieve Highly Concurrent I/O with Coroutines
Item 61: Know How to Port Threaded I/O to asyncio
Item 62: Mix Threads and Coroutines to Ease the Transition to asyncio
Item 63: Avoid Blocking the asyncio Event Loop to Maximize Responsiveness
Item 64: Consider concurrent.futures for True Parallelism
8. Robustness and Performance
Item 65: Take Advantage of Each Block in try/except/else/finally
Item 66: Consider contextlib and with Statements for Reusable try/finally Behavior
Item 67: Use datetime Instead of time for Local Clocks
Item 68: Make pickle Reliable with copyreg
Item 69: Use decimal When Precision Is Paramount
Item 70: Profile Before Optimizing
Item 71: Prefer deque for Producer–Consumer Queues for Producer–Consumer Queues
Item 72: Consider Searching Sorted Sequences with bisect
Item 73: Know How to Use heapq for Priority Queues
Item 74: Consider memoryview and bytearray for Zero-Copy Interactions with bytes
9. Testing and Debugging
Item 75: Use repr Strings for Debugging Output
Item 76: Verify Related Behaviors in TestCase Subclasses
Item 77: Isolate Tests from Each Other with setUp, tearDown, setUpModule, and tearDownModule
Item 78: Use Mocks to Test Code with Complex Dependencies
Item 79: Encapsulate Dependencies to Facilitate Mocking and Testing
Item 80: Consider Interactive Debugging with pdb
Item 81: Use tracemalloc to Understand Memory Usage and Leaks
10. Collaboration
Item 82: Know Where to Find Community-Built Modules
Item 83: Use Virtual Environments for Isolated and Reproducible Dependencies
Item 84: Write Docstrings for Every Function, Class, and Module
Item 85: Use Packages to Organize Modules and Provide Stable APIs
Item 86: Consider Module-Scoped Code to Configure Deployment Environments
Item 87: Define a Root Exception to Insulate Callers from APIs
Item 88: Know How to Break Circular Dependencies
Item 89: Consider warnings to Refactor and Migrate Usage
Item 90: Consider Static Analysis via typing to Obviate Bugs
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