Does it seem like your Python projects are getting bigger and bigger? Are you feeling the pain as your codebase expands and gets tougher to debug and maintain? Python is an easy language to learn and use, but that also means systems can quickly grow beyond comprehension. Thankfully, Python has features to help developers overcome maintainability woes.
In this practical book, author Patrick Viafore shows you how to use Python’s type system to the max. You’ll look at user-defined types, such as classes and enums, and Python’s type hinting system. You’ll also learn how to make Python extensible and how to use a comprehensive testing strategy as a safety net. With these tips and techniques, you’ll write clearer and more maintainable code.
- Learn why types are essential in modern development ecosystems
- Understand how type choices such as classes, dictionaries, and enums reflect specific intents
- Make Python extensible for the future without adding bloat
- Use popular Python tools to increase the safety and robustness of your codebase
- Evaluate current code to detect common maintainability gotchas
- Build a safety net around your codebase with linters and tests
Preface Who Should Read This Book About This Book Conventions Used in This Book Using Code Examples O’Reilly Online Learning How to Contact Us Acknowledgments 1. Introduction to Robust Python Robustness Why Does Robustness Matter? What’s Your Intent? Asynchronous Communication Examples of Intent in Python Collections Iteration Law of Least Surprise Closing Thoughts I. Annotating Your Code with Types 2. Introduction to Python Types What’s in a Type? Mechanical Representation Semantic Representation Typing Systems Strong Versus Weak Dynamic Versus Static Duck Typing Closing Thoughts 3. Type Annotations What Are Type Annotations? Benefits of Type Annotations Autocomplete Typecheckers Exercise: Spot the Bug When to Use Type Annotations Closing Thoughts 4. Constraining Types Optional Type Union Types Product and Sum Types Literal Types Annotated Types NewType Final Types Closing Thoughts 5. Collection Types Annotating Collections Homogeneous Versus Heterogeneous Collections TypedDict Creating New Collections Generics Modifying Existing Types As Easy as ABC Closing Thoughts 6. Customizing Your Typechecker Configuring Your Typechecker Configuring mypy Mypy Reporting Speeding Up mypy Alternative Typecheckers Pyre Pyright Closing Thoughts 7. Adopting Typechecking Practically Trade-offs Breaking Even Earlier Find Your Pain Points Target Code Strategically Lean on Your Tooling Closing Thoughts II. Defining Your Own Types 8. User-Defined Types: Enums User-Defined Types Enumerations Enum When Not to Use Advanced Usage Automatic Values Flags Integer Conversion Unique Closing Thoughts 9. User-Defined Types: Data Classes Data Classes in Action Usage String Conversion Equality Relational Comparison Immutability Comparison to Other Types Data Classes Versus Dictionaries Data Classes Versus TypedDict Data Classes Versus namedtuple Closing Thoughts 10. User-Defined Types: Classes Class Anatomy Constructors Invariants Avoiding Broken Invariants Why Are Invariants Beneficial? Communicating Invariants Consuming Your Class What About Maintainers? Encapsulation and Maintaining Invariants Encapsul-what, Now? Protecting Data Access Operations Closing Thoughts 11. Defining Your Interfaces Natural Interface Design Thinking Like a User Natural Interactions Natural Interfaces in Action Magic Methods Context Managers Closing Thoughts 12. Subtyping Inheritance Substitutability Design Considerations Composition Closing Thoughts 13. Protocols Tension Between Typing Systems Leave the Type Blank or Use Any Use a Union Use Inheritance Use Mixins Protocols Defining a Protocol Advanced Usage Composite Protocols Runtime Checkable Protocols Modules Satisfying Protocols Closing Thoughts 14. Runtime Checking With pydantic Dynamic Configuration pydantic Validators Validation Versus Parsing Closing Thoughts III. Extensible Python 15. Extensibility What Is Extensibility? The Redesign Open-Closed Principle Detecting OCP Violations Drawbacks Closing Thoughts 16. Dependencies Relationships Types of Dependencies Physical Dependencies Logical Dependencies Temporal Dependencies Visualizing Your Dependencies Visualizing Packages Visualizing Imports Visualizing Function Calls Interpreting Your Dependency Graph Closing Thoughts 17. Composability Composability Policy Versus Mechanisms Composing on a Smaller Scale Composing Functions Composing Algorithms Closing Thoughts 18. Event-Driven Architecture How It Works Drawbacks Simple Events Using a Message Broker The Observer Pattern Streaming Events Closing Thoughts 19. Pluggable Python The Template Method Pattern The Strategy Pattern Plug-in Architectures Closing Thoughts IV. Building a Safety Net 20. Static Analysis Linting Writing Your Own Pylint Plug-in Breaking Down the Plug-in Other Static Analyzers Complexity Checkers Security Analysis Closing Thoughts 21. Testing Strategy Defining Your Test Strategy What Is a Test? Reducing Test Cost AAA Testing Closing Thoughts 22. Acceptance Testing Behavior-Driven Development The Gherkin Language Executable Specifications Additional behave Features Parameterized Steps Table-Driven Requirements Step Matching Customizing the Test Life Cycle Using Tags to Selectively Run Tests Report Generation Closing Thoughts 23. Property-Based Testing Property-Based Testing with Hypothesis The Magic of Hypothesis Contrast with Traditional Tests Getting the Most Out of Hypothesis Hypothesis Strategies Generating Algorithms Closing Thoughts 24. Mutation Testing What Is Mutation Testing? Mutation Testing with mutmut Fixing Mutants Mutation Testing Reports Adopting Mutation Testing The Fallacy of Coverage (and Other Metrics) Closing Thoughts Index
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