Description

Effective Python will help students harness the full power of Python to write exceptionally robust, efficient, maintainable, and well-performing code. Utilizing the concise, scenario-driven style pioneered in Scott Meyers's best-selling Effective C++, Brett Slatkin brings together 53 Python best practices, tips, shortcuts, and realistic code examples from expert programmers. Each section contains specific, actionable guidelines organized into items, each with carefully worded advice supported by detailed technical arguments and illuminating examples.

Features

• Covers Python algorithms, objects, concurrency, collaboration, built-in modules, and much more
• Addresses both Python 3 and Python 2
• Guides students to a far deeper understanding of the Python language, so they know why its unique idioms and rules of thumb make sense
• Follows the enormously popular 'Effective' format proven in Scott Meyers' classic Effective C++

Table of Contents

Preface xiii

Acknowledgments xvii

About the Author xix

Chapter 1: Pythonic Thinking 1

Item 1: Know Which Version of Python You’re Using 1

Item 2: Follow the PEP 8 Style Guide 2

Item 3: Know the Differences Between bytes, str, and unicode 5

Item 4: Write Helper Functions Instead of Complex Expressions 8

Item 5: Know How to Slice Sequences 10

Item 6: Avoid Using start, end, and stride in a Single Slice 13

Item 7: Use List Comprehensions Instead of map and filter 15

Item 8: Avoid More Than Two Expressions in List Comprehensions 16

Item 9: Consider Generator Expressions for Large Comprehensions 18

Item 10: Prefer enumerate Over range 20

Item 11: Use zip to Process Iterators in Parallel 21

Item 12: Avoid else Blocks After for and while Loops 23

Item 13: Take Advantage of Each Block in try/except/else/finally 26

Chapter 2: Functions 29

Item 14: Prefer Exceptions to Returning None 29

Item 15: Know How Closures Interact with Variable Scope 31

Item 16: Consider Generators Instead of Returning Lists 36

Item 17: Be Defensive When Iterating Over Arguments 38

Item 18: Reduce Visual Noise with Variable Positional Arguments 43

Item 19: Provide Optional Behavior with Keyword Arguments 45

Item 20: Use None and Docstrings to Specify Dynamic Default Arguments 48

Item 21: Enforce Clarity with Keyword-Only Arguments 51

Chapter 3: Classes and Inheritance 55

Item 22: Prefer Helper Classes Over Bookkeeping with Dictionaries and Tuples 55

Item 23: Accept Functions for Simple Interfaces Instead of Classes 61

Item 24: Use @classmethod Polymorphism to Construct Objects Generically 64

Item 25: Initialize Parent Classes with super 69

Item 26: Use Multiple Inheritance Only for Mix-in Utility Classes 73

Item 27: Prefer Public Attributes Over Private Ones 78

Item 28: Inherit from collections.abc for Custom Container Types 83

Chapter 4: Metaclasses and Attributes 87

Item 29: Use Plain Attributes Instead of Get and Set Methods 87

Item 30: Consider @property Instead of Refactoring Attributes 91

Item 31: Use Descriptors for Reusable @property Methods 95

Item 32: Use __getattr__, __getattribute__, and __setattr__ for Lazy Attributes 100

Item 33: Validate Subclasses with Metaclasses 105

Item 34: Register Class Existence with Metaclasses 108

Item 35: Annotate Class Attributes with Metaclasses 112

Chapter 5: Concurrency and Parallelism 117

Item 36: Use subprocess to Manage Child Processes 118

Item 37: Use Threads for Blocking I/O, Avoid for Parallelism 122

Item 38: Use Lock to Prevent Data Races in Threads 126

Item 39: Use Queue to Coordinate Work Between Threads 129

Item 40: Consider Coroutines to Run Many Functions Concurrently 136

Item 41: Consider concurrent.futures for True Parallelism 145

Chapter 6: Built-in Modules 151

Item 42: Define Function Decorators with functools.wraps 151

Item 43: Consider contextlib and with Statements for Reusable try/finally Behavior 153

Item 44: Make pickle Reliable with copyreg 157

Item 45: Use datetime Instead of time for Local Clocks 162

Item 46: Use Built-in Algorithms and Data Structures 166

Item 47: Use decimal When Precision Is Paramount 171

Item 48: Know Where to Find Community-Built Modules 173

Chapter 7: Collaboration 175

Item 49: Write Docstrings for Every Function, Class, and Module 175

Item 50: Use Packages to Organize Modules and Provide Stable APIs 179

Item 51: Define a Root Exception to Insulate Callers from APIs 184

Item 52: Know How to Break Circular Dependencies 187

Item 53: Use Virtual Environments for Isolated and

Reproducible Dependencies 192

Chapter 8: Production 199

Item 54: Consider Module-Scoped Code to Configure Deployment Environments 199

Item 55: Use repr Strings for Debugging Output 202

Item 56: Test Everything with unittest 204

Item 57: Consider Interactive Debugging with pdb 208

Item 58: Profile Before Optimizing 209

Item 59: Use tracemalloc to Understand Memory Usage and Leaks 214

Index 217

Author

Brett Slatkin, senior staff software engineer at Google, is engineering lead and co-founder of Google Consumer Surveys. He previously worked on Google App Engine’s Python infrastructure, leveraged Python to manage Google’s enormous server fleet, and used Python to implement Google's system for PubSubHubbub, a protocol he co-created. Slatkin holds a B.S. in computer engineering from Columbia University in the City of New York. He lives in San Francisco.

Reader Review(s)

“I’ve been programming in Python for years and thought I knew it pretty well. Thanks to this treasure trove of tips and techniques, I realize there’s so much more I could be doing with my Python code to make it faster (e.g., using built-in data structures), easier to read (e.g., enforcing keyword-only arguments), and much more Pythonic (e.g., using zip to iterate over lists in parallel).”

–Pamela Fox, educationeer, Khan Academy

 

“If I had this book when I first switched from Java to Python, it would have saved me many months of repeated code rewrites, which happened each time I realized I was doing particular things ‘non-Pythonically.’ This book collects the vast majority of basic Python ‘must-knows’ into one place, eliminating the need to stumble upon them one-by-one over the course of months or years. The scope of the book is impressive, starting with the importance of PEP8 as well as that of major Python idioms, then reaching through function, method and class design, effective standard library use, quality API design, testing, and performance measurement–this book really has it all. A fantastic introduction to what it really means to be a Python programmer for both the novice and the experienced developer.”

–Mike Bayer, creator of SQLAlchemy

 

“Effective Python will take your Python skills to the next level with clear guidelines for improving Python code style and function.”

–Leah Culver, developer advocate, Dropbox

 

“This book is an exceptionally great resource for seasoned developers in other languages who are looking to quickly pick up Python and move beyond the basic language constructs into more Pythonic code. The organization of the book is clear, concise, and easy to digest, and each item and chapter can stand on its own as a meditation on a particular topic. The book covers the breadth of language constructs in pure Python without confusing the reader with the complexities of the broader Python ecosystem. For more seasoned developers the book provides in-depth examples of language constructs they may not have previously encountered, and provides examples of less commonly used language features. It is clear that the author is exceptionally facile with Python, and he uses his professional experience to alert the reader to common subtle bugs and common failure modes. Furthermore, the book does an excellent job of pointing out subtleties between Python 2.X and Python 3.X and could serve as a refresher course as one transitions between variants of Python.”

–Katherine Scott, software lead, Tempo Automation

 

“This is a great book for both novice and experienced programmers. The code examples and explanations are well thought out and explained concisely and thoroughly.”

–C. Titus Brown, associate professor, UC Davis

 

“This is an immensely useful resource for advanced Python usage and building cleaner, more maintainable software. Anyone looking to take their Python skills to the next level would benefit from putting the book’s advice into practice.”

–Wes McKinney, creator of pandas; author of Python for Data Analysis; and software engineer at Cloudera