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Clean Code in Python

By : Anaya

3.7 (3)

Buy this Book

Clean Code in Python

3.7 (3)

By: Anaya

Buy this Book

Overview of this book

Python is currently used in many different areas such as software construction, systems administration, and data processing. In all of these areas, experienced professionals can find examples of inefficiency, problems, and other perils, as a result of bad code. After reading this book, readers will understand these problems, and more importantly, how to correct them. The book begins by describing the basic elements of writing clean code and how it plays an important role in Python programming. You will learn about writing efficient and readable code using the Python standard library and best practices for software design. You will learn to implement the SOLID principles in Python and use decorators to improve your code. The book delves more deeply into object oriented programming in Python and shows you how to use objects with descriptors and generators. It will also show you the design principles of software testing and how to resolve software problems by implementing design patterns in your code. In the final chapter we break down a monolithic application to a microservice one, starting from the code as the basis for a solid platform. By the end of the book, you will be proficient in applying industry approved coding practices to design clean, sustainable and readable Python code.

Preface

Who this book is for

What this book covers

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Free Chapter

Introduction, Code Formatting, and Tools

The meaning of clean code

The importance of having clean code

Docstrings and annotations

Summary

Pythonic Code

Indexes and slices

Context managers

Properties, attributes, and different types of methods for objects

Iterable objects

Container objects

Dynamic attributes for objects

Callable objects

Summary of magic methods

Caveats in Python

Summary

References

General Traits of Good Code

Design by contract

Defensive programming

Separation of concerns

Acronyms to live by

Composition and inheritance

Arguments in functions and methods

Final remarks on good practices for software design

Summary

References

The SOLID Principles

Single responsibility principle

The open/closed principle

Liskov's substitution principle

Interface segregation

Dependency inversion

Summary

References

Using Decorators to Improve Our Code

What are decorators in Python?

Effective decorators – avoiding common mistakes

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

Types of descriptors

Descriptors in action

Analysis of descriptors

Summary

References

Using Generators

Technical requirements

Creating generators

Iterating idiomatically

Coroutines

Asynchronous programming

Summary

References

Unit Testing and Refactoring

Design principles and unit testing

Frameworks and tools for testing

Refactoring

More about unit testing

A brief introduction to test-driven development

Summary

References

Common Design Patterns

Considerations for design patterns in Python

Design patterns in action

The null object pattern

Final thoughts about design patterns

Summary

References

Clean Architecture

From clean code to clean architecture

Software components

Use case

Summary

References

Summing it all up

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A brief introduction to test-driven development

There are entire books dedicated only to TDD, so it would not be realistic to try and cover this topic comprehensively in this book. However, it's such an important topic that it has to be mentioned.

The idea behind TDD is that tests should be written before production code in a way that the production code is only written to respond to tests that are failing due to that missing implementation of the functionality.

There are multiple reasons why we would like to write the tests first and then the code. From a pragmatic point of view, we would be covering our production code quite accurately. Since all of the production code was written to respond to a unit test, it would be highly unlikely that there are tests missing for functionality (that doesn't mean that there is 100% of coverage of course, but at least all main functions...