Book Image

Clean Code in Python - Second Edition

By : Mariano Anaya

Book Image

Clean Code in Python - Second Edition

By: Mariano Anaya

Overview of this book

Experienced professionals in every field face several instances of disorganization, poor readability, and testability due to unstructured code. With updated code and revised content aligned to the new features of Python 3.9, this second edition of Clean Code in Python will provide you with all the tools you need to overcome these obstacles and manage your projects successfully. The book begins by describing the basic elements of writing clean code and how it plays a key role in Python programming. You will learn about writing efficient and readable code using the Python standard library and best practices for software design. The book discusses 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 problems by implementing software design patterns in your code. In the concluding chapter, we break down a monolithic application into a microservices-based one starting from the code as the basis for a solid platform. By the end of this clean code book, you will be proficient in applying industry-approved coding practices to design clean, sustainable, and readable real-world Python code.

Preface

Who this book is for?

What this book covers

To get the most out of this book

Introduction, Code Formatting, and Tools

Introduction, Code Formatting, and Tools

Code formatting

Free Chapter

Pythonic Code

Indexes and slices

Context managers

Comprehensions and assignment expressions

Properties, attributes, and different types of methods for objects

Caveats in Python

A brief introduction to asynchronous code

General Traits of Good Code

General Traits of Good Code

Design by contract

Defensive programming

Separation of concerns

Acronyms to live by

Inheritance in Python

Arguments in functions and methods

Final remarks on good practices for software design

The SOLID Principles

The SOLID Principles

The single responsibility principle

The open/closed principle

Liskov's substitution principle

Interface segregation

Dependency inversion

Using Decorators to Improve Our Code

Using Decorators to Improve Our Code

What are decorators in Python?

More advanced decorators

Good uses for decorators

Effective decorators – avoiding common mistakes

Decorators and clean code

Getting More Out of Our Objects with Descriptors

Getting More Out of Our Objects with Descriptors

A first look at descriptors

Types of descriptors

Descriptors in action

Analysis of descriptors

Final remarks about descriptors

Generators, Iterators, and Asynchronous Programming

Generators, Iterators, and Asynchronous Programming

Technical requirements

Creating generators

Iterating idiomatically

Asynchronous programming

Unit Testing and Refactoring

Unit Testing and Refactoring

Design principles and unit testing

Tools for testing

More about testing

Common Design Patterns

Common Design Patterns

Design pattern considerations in Python

Design patterns in action

The null object pattern

Final thoughts about design patterns

Clean Architecture

Clean Architecture

From clean code to clean architecture

Software components

Summing it all up

Other Books You May Enjoy

Other Books You May Enjoy

Index

Customer Reviews

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Who this book is for?

This book is suitable for all software engineering practitioners who are interested in software design or learning more about Python. It is assumed that the reader is already familiar with the principles of object-oriented software design and has experience writing code.

It will appeal to team leads, software architects and senior software engineers who want to learn good Python coding techniques to create projects from scratch or work on their legacy systems to save costs and improve efficiency.

The book is organized in such a way that the content is in increasing order of complexity. The first chapters cover the basics of Python, which is a good way to learn the main idioms, functions, and utilities available in the language. The idea is not just to solve some problems with Python, but to do so in an idiomatic way.

Experienced programmers will also benefit from the topics in this book, as some sections cover advanced topics in Python, such as decorators, descriptors, and an introduction to asynchronous programming. It will help the reader discover more about Python because some of the cases are analyzed from the internals of the language itself.

Scientists using Python for data processing can also benefit from the content of this book, and to that end, several parts of the book are dedicated to setting up projects from the ground up, in terms of tools, configuration of environments, and good practices to release software.

It is worth emphasizing the word "practitioners" in the first sentence of this section. This is a book that takes a pragmatic approach. Examples are limited to what the case study requires but are also intended to resemble the context of a real software project. It is not an academic book, and as such the definitions made, the remarks made, and the recommendations are to be taken with caution. The reader is expected to examine these recommendations critically and pragmatically rather than dogmatically. After all, practicality beats purity.