Book Image

Clean Code in Python

By : Mariano Anaya
2 (1)
Book Image

Clean Code in Python

2 (1)
By: Mariano Anaya

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.
Table of Contents (12 chapters)

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...