Effective Python: 59 Specific Ways to Write Better Python (Effective Software Development Series)

Each item in Slatkin s "Effective Python" teaches a self-contained lesson with its own source code. This makes the book random-access: Items are easy to browse and study in whatever order the reader needs. I will be recommending "Effective Python" to students as an admirably compact source of mainstream advice on a very broad range of topics for the intermediate Python programmer. " Brandon Rhodes, software engineer at Dropbox and chair of PyCon 2016-2017" It s easy to start coding 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. " 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 59 Python best practices, tips, and shortcuts, and explains them with realistic code examples. 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 learn the best way to accomplish key tasks, so you can write code that s easier to understand, maintain, and improve. Key features include Actionable guidelines for all major areas of Python 3.x and 2.x development, with detailed explanations and examples Best practices for writing functions that clarify intention, promote reuse, and avoid bugs Coverage of how to accurately express behaviors with classes and objects Guidance on how to avoid pitfalls with metaclasses and dynamic attributes More efficient approaches to concurrency and parallelism Better techniques and idioms for using Python s built-in modules Tools and best practices for collaborative development Solutions for debugging, testing, and optimization in order to improve quality and performance " About This eBook Title Page Copyright Page Praise for Effective Python Dedication Page Contents Preface What This Book Covers Conventions Used in This Book Where to Get the Code and Errata 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, str, and unicode Item 4: Write Helper Functions Instead of Complex Expressions Item 5: Know How to Slice Sequences Item 6: Avoid Using start, end, and stride in a Single Slice Item 7: Use List Comprehensions Instead of map and filter Item 8: Avoid More Than Two Expressions in List Comprehensions Item 9: Consider Generator Expressions for Large Comprehensions Item 10: Prefer enumerate Over range Item 11: Use zip to Process Iterators in Parallel Item 12: Avoid else Blocks After for and while Loops Item 13: Take Advantage of Each Block in try/except/else/finally 2. Functions Item 14: Prefer Exceptions to Returning None Item 15: Know How Closures Interact with Variable Scope Item 16: Consider Generators Instead of Returning Lists Item 17: Be Defensive When Iterating Over Arguments Item 18: Reduce Visual Noise with Variable Positional Arguments Item 19: Provide Optional Behavior with Keyword Arguments Item 20: Use None and Docstrings to Specify Dynamic Default Arguments Item 21: Enforce Clarity with Keyword-Only Arguments 3. Classes and Inheritance Item 22: Prefer Helper Classes Over Bookkeeping with Dictionaries and Tuples Item 23: Accept Functions for Simple Interfaces Instead of Classes Item 24: Use @classmethod Polymorphism to Construct Objects Generically Item 25: Initialize Parent Classes with super Item 26: Use Multiple Inheritance Only for Mix-in Utility Classes Item 27: Prefer Public Attributes Over Private Ones Item 28: Inherit from collections.abc for Custom Container Types 4. Metaclasses and Attributes Item 29: Use Plain Attributes Instead of Get and Set Methods Item 30: Consider @property Instead of Refactoring Attributes Item 31: Use Descriptors for Reusable @property Methods Item 32: Use __getattr__, __getattribute__, and __setattr__ for Lazy Attributes Item 33: Validate Subclasses with Metaclasses Item 34: Register Class Existence with Metaclasses Item 35: Annotate Class Attributes with Metaclasses 5. Concurrency and Parallelism Item 36: Use subprocess to Manage Child Processes Item 37: Use Threads for Blocking I/O, Avoid for Parallelism Item 38: Use Lock to Prevent Data Races in Threads Item 39: Use Queue to Coordinate Work Between Threads Item 40: Consider Coroutines to Run Many Functions Concurrently Item 41: Consider concurrent.futures for True Parallelism 6. Built-in Modules Item 42: Define Function Decorators with functools.wraps Item 43: Consider contextlib and with Statements for Reusable try/finally Behavior Item 44: Make pickle Reliable with copyreg Item 45: Use datetime Instead of time for Local Clocks Item 46: Use Built-in Algorithms and Data Structures Item 47: Use decimal When Precision Is Paramount Item 48: Know Where to Find Community-Built Modules 7. Collaboration Item 49: Write Docstrings for Every Function, Class, and Module Item 50: Use Packages to Organize Modules and Provide Stable APIs Item 51: Define a Root Exception to Insulate Callers from APIs Item 52: Know How to Break Circular Dependencies Item 53: Use Virtual Environments for Isolated and Reproducible Dependencies 8. Production Item 54: Consider Module-Scoped Code to Configure Deployment Environments Item 55: Use repr Strings for Debugging Output Item 56: Test Everything with unittest Item 57: Consider Interactive Debugging with pdb Item 58: Profile Before Optimizing Item 59: Use tracemalloc to Understand Memory Usage and Leaks Index Code Snippets It's easy to start writing code with Python: that's why the language is so immensely popular. However, Python has unique strengths, charms, and expressivity that can be hard to grasp at first -- as well as hidden pitfalls that can easily trip you up if you aren't aware of them. Effective Python will help you 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. Through realistic examples, Slatkin uncovers little-known Python quirks, intricacies, and idioms that powerfully impact code behavior and performance. You'll learn how to choose the most efficient and effective way to accomplish key tasks when multiple options exist, and how to write code that's easier to understand, maintain, and improve. Drawing on his deep understanding of Python's capabilities, Slatkin offers practical advice for each major area of development with both Python 3.x and Python 2.x. Coverage includes: * Algorithms * Objects * Concurrency * Collaboration * Built-in modules * Production techniques * And more Each section contains specific, actionable guidelines organized into items, each with carefully worded advice supported by detailed technical arguments and illuminating examples. Using Effective Python, you can systematically improve all the Python code you write: not by blindly following rules or mimicking incomprehensible idioms, but by gaining a deep understanding of the technical reasons why they make sense It's easy to start writing code with Python: that's why the language is so immensely popular. However, Python has unique strengths, charms, and expressivity that can be hard to grasp at first -- as well as hidden pitfalls that can easily trip you up if you aren't aware of them. Effective Python will help you 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 59 Python best practices, tips, shortcuts, and realistic code examples from expert programmers.Through realistic examples, Slatkin uncovers little-known Python quirks, intricacies, and idioms that powerfully impact code behavior and performance. You'll learn how to choose the most efficient and effective way to accomplish key tasks when multiple options exist, and how to write code that's easier to understand, maintain, and improve. The Python programming language has unique strengths and charms that can be hard to grasp. Many programmers familiar with other languages often approach Python from a limited mindset instead of embracing its full expressivity. Some programmers go too far in the other direction, overusing Python features that can cause big problems later. Effective Python provides insight into the Pythonic way of writing programs: the best way to use Python. It builds on a fundamental understanding of the language that I assume you already have. Novice programmers will learn the best practices of Python’s capabilities. Experienced programmers will learn how to embrace the strangeness of a new tool with confidence. -[(Source)][1] [1]: http://www.effectivepython.com/