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Index
Clean Code in Python Title Page Copyright and Credits Clean Code in Python Dedication Packt Upsell Why subscribe? PacktPub.com Contributors About the author About the reviewer Packt is searching for authors like you Table of Contents Preface Who this book is for What this book covers To get the most out of this book Download the example code files Conventions used Get in touch Reviews Introduction, Code Formatting, and Tools The meaning of clean code The importance of having clean code The role of code formatting in clean code Adhering to a coding style guide on your project Docstrings and annotations Docstrings Annotations  Do annotations replace docstrings? Configuring the tools for enforcing basic quality gates Type hinting with Mypy Checking the code with Pylint Setup for automatic checks Summary Pythonic Code Indexes and slices  Creating your own sequences Context managers Implementing context managers Properties, attributes, and different types of methods for objects Underscores in Python Properties Iterable objects Creating iterable objects Creating sequences Container objects Dynamic attributes for objects Callable objects Summary of magic methods Caveats in Python Mutable default arguments Extending built-in types Summary References General Traits of Good Code Design by contract Preconditions Postconditions Pythonic contracts Design by contract – conclusions Defensive programming Error handling Value substitution Exception handling Handle exceptions at the right level of abstraction Do not expose tracebacks Avoid empty except blocks Include the original exception Using assertions in Python Separation of concerns Cohesion and coupling Acronyms to live by DRY/OAOO YAGNI KIS EAFP/LBYL Composition and inheritance When inheritance is a good decision Anti-patterns for inheritance Multiple inheritance in Python Method Resolution Order (MRO) Mixins Arguments in functions and methods How function arguments work in Python How arguments are copied to functions Variable number of arguments The number of arguments in functions Function arguments and coupling Compact function signatures that take too many arguments Final remarks on good practices for software design Orthogonality in software Structuring the code Summary References The SOLID Principles Single responsibility principle A class with too many responsibilities Distributing responsibilities The open/closed principle Example of maintainability perils for not following the open/closed principle Refactoring the events system for extensibility Extending the events system Final thoughts about the OCP Liskov's substitution principle Detecting LSP issues with tools Detecting incorrect datatypes in method signatures with Mypy Detecting incompatible signatures with Pylint More subtle cases of LSP violations Remarks on the LSP Interface segregation An interface that provides too much The smaller the interface, the better How small should an interface be? Dependency inversion A case of rigid dependencies Inverting the dependencies Summary References Using Decorators to Improve Our Code What are decorators in Python? Decorate functions Decorate classes Other types of decorator Passing arguments to decorators Decorators with nested functions Decorator objects Good uses for decorators Transforming parameters Tracing code Effective decorators – avoiding common mistakes Preserving data about the original wrapped object Dealing with side-effects in decorators Incorrect handling of side-effects in a decorator Requiring decorators with side-effects Creating decorators that will always work The DRY principle with decorators Decorators and separation of concerns Analyzing good decorators Summary References Getting More Out of Our Objects with Descriptors A first look at descriptors The machinery behind descriptors Exploring each method of the descriptor protocol __get__(self, instance, owner) __set__(self, instance, value) __delete__(self, instance) __set_name__(self, owner, name) Types of descriptors Non-data descriptors Data descriptors Descriptors in action An application of descriptors A first attempt without using descriptors The idiomatic implementation Different forms of implementing descriptors The issue of global shared state Accessing the dictionary of the object Using weak references More considerations about descriptors Reusing code Avoiding class decorators Analysis of descriptors How Python uses descriptors internally Functions and methods Built-in decorators for methods Slots Implementing descriptors in decorators Summary References Using Generators Technical requirements Creating generators A first look at generators Generator expressions Iterating idiomatically Idioms for iteration The next() function Using a generator Itertools Simplifying code through iterators Repeated iterations Nested loops The iterator pattern in Python The interface for iteration Sequence objects as iterables Coroutines The methods of the generator interface close() throw(ex_type[, ex_value[, ex_traceback]]) send(value) More advanced coroutines Returning values in coroutines Delegating into smaller coroutines – the yield from syntax The simplest use of yield from Capturing the value returned by a sub-generator Sending and receiving data to and from a sub-generator Asynchronous programming Summary References Unit Testing and Refactoring Design principles and unit testing A note about other forms of automated testing Unit testing and agile software development Unit testing and software design Defining the boundaries of what to test Frameworks and tools for testing Frameworks and libraries for unit testing unittest Parametrized tests pytest Basic test cases with pytest Parametrized tests Fixtures Code coverage Setting up rest coverage Caveats of test coverage Mock objects A fair warning about patching and mocks Using mock objects Types of mocks A use case for test doubles Refactoring Evolving our code Production code isn't the only thing that evolves More about unit testing Property-based testing Mutation testing A brief introduction to test-driven development Summary References Common Design Patterns Considerations for design patterns in Python Design patterns in action Creational patterns Factories Singleton and shared state (monostate) Shared state The borg pattern Builder Structural patterns Adapter Composite Decorator Facade Behavioral patterns Chain of responsibility The template method Command State The null object pattern Final thoughts about design patterns The influence of patterns over the design Names in our models Summary References Clean Architecture From clean code to clean architecture Separation of concerns Abstractions Software components Packages Containers Use case The code Domain models Calling from the application Adapters The services Analysis The dependency flow Limitations Testability Intention revealing Summary References Summing it all up Other Books You May Enjoy Leave a review - let other readers know what you think
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