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Clean Code in Python

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Preface

Getting Started with Clean Code in Python

Introduction to Clean Code, Code Formatting, and Tools

An Introduction to Clean Code

Code Formatting

Documentation: Code Comments

Documentation: Docstrings

Documentation: Annotations

Tooling for Type Consistency and General Validations

Tooling for Automatic Formatting

Quiz on Introduction to Clean Code, Code Formatting, and Tools

Pythonic Code

Indexes and Slices Context Managers Comprehensions and Assignment Expressions Properties, Attributes, and Different Types of Methods for Objects Creating Classes with a More Compact Syntax Iterable Objects Other Properties, Attributes, and Methods Summary of Magic Methods Caveats in Python Quiz on Pythonic Code

General Traits of Good Code

Design by Contract Defensive Programming Best Practices for Exception Handling Using Assertions in Python Separation of Concerns Acronyms to Live By Inheritance in Python Multiple Inheritance in Python Arguments in Functions and Methods Variable Number of Arguments in Python More on How Function Arguments Work in Python Optimizing the Number of Arguments in Functions Final Remarks on Good Practices for Software Design Quiz on General Traits of Good Code

The SOLID Principles

The Single Responsibility Principle The Open/Closed Principle Liskov's Substitution Principle Integration Segregation Principle Dependency Inversion Principle Quiz on the SOLID Principles Challenge: The Single Responsibility Principle Solution: The Single Responsibility Principle

Using Decorators to Improve Our Code

What Are Decorators in Python?Function Decorators Class Decorators More Advanced Decorators Decorators with Default Values Decorators for Coroutines Extended Syntax for Decorators Good Uses for Decorators Effective Decorators—Avoiding Common Mistakes Dealing with Side Effects in Decorators Creating Decorators That Will Always Work Decorators and Clean Code The DRY Principle with Decorators Decorators and Separation of Concerns Analysis of Good Decorators Quiz on Using Decorators to Improve Our Code Challenge: Creating Function Decorators Solution: Creating Function Decorators

Getting More Out of Our Objects with Descriptors

A First Look at Descriptors Get Method of the Descriptor Protocol Set Method of the Descriptor Protocol Delete Method of the Descriptor Protocol Set Name Method of the Descriptor Protocol Types of Descriptors An Application of Descriptors Different Forms of Implementing Descriptors More Considerations for Using Descriptors Analysis of Descriptors Final Remarks About Descriptors Quiz on Getting More Out of Our Objects with Descriptors Challenge: Creating Descriptors Solution: Creating Descriptors

Generators, Iterators, and Asynchronous Programming

What are Generators?Generator Expressions Iterating Idiomatically Itertools Simplifying Code through Iterators The Iterator Pattern in Python Coroutines More Advanced Coroutines Further Uses of the yield from Construction Asynchronous Programming Magic Asynchronous Methods Asynchronous Iteration and Asynchronous Generators Quiz on Generators, Iterators, and Asynchronous Programming Challenge: Asynchronous Programming Solution: Asynchronous Programming

Unit Testing and Refactoring

Design Principles and Unit Testing Forms of Automated Testing Unit Testing and Agile Software Development Unit Testing and Software Design Getting Started with Testing The unittest Module Parameterised Tests Using unittest The pytest Framework Parameterized Tests Using pytest Fixtures in pytest Code Coverage Mock Objects Using Mock Objects Refactoring More about Testing Common Themes in Testing Test-Driven Development Quiz on Unit Testing and Refactoring

Common Design Patterns

Design Patterns Considerations in Python Design Patterns in Action Creational Patterns Shared State The Borg Pattern Adapter Composite Decorator Facade Chain of Responsibility The Template Method Command State The Null Object Pattern Final Thoughts about Design Patterns Quiz on Common Design Patterns

Clean Architecture

From Clean Code to Clean Architecture Separation of Concerns Monolithic Applications and Microservices Abstractions Software Components Packages Managing Dependencies Docker Containers Use Case The Service Analysis of the Architectural Traits Quiz on Clean Architecture

Conclusion

An Overview of the Course Ending Notes

Documentation: Annotations

Learn how annotations are used for type hinting in Python.

We'll cover the following

Do annotations replace docstrings?

The basic idea of annotations is that they hint to the readers of the code what to expect as values of arguments in functions. The use of the word hint is not casual; annotations enable type hinting.

Annotations let us specify the expected type of some variables that have been defined. It is actually not only about the types, but any kind of metadata that can help us get a better idea of what that variable actually represents.

Consider this example:

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