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

Autorentext

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.

Inhalt

• Preface
• Chapter 1 Pythonic Thinking
• Chapter 2 Lists and Dictionaries
• Chapter 3 Functions
• Chapter 4 Comprehensions and Generators
• Chapter 5 Classes and Interfaces
• Chapter 6 Metaclasses and Attributes
• Chapter 7 Concurrency and Parallelism
• Chapter 8 Robustness and Performance
• Chapter 9 Testing and Debugging
• Chapter 10 Collaboration
• Index