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Clean Code in Python

Clean Code in Python

By : Mariano Anaya
3.7 (3)
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Clean Code in Python

Clean Code in Python

3.7 (3)
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)
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The open/closed principle

The open/closed principle (OCP) states that a module should be both open and closed (but with respect to different aspects).

When designing a class, for instance, we should carefully encapsulate the logic so that it has good maintenance, meaning that we will want it to be open to extension but closed for modification.

What this means in simple terms is that, of course, we want our code to be extensible, to adapt to new requirements, or changes in the domain problem. This means that, when something new appears on the domain problem, we only want to add new things to our model, not change anything existing that is closed to modification.

If, for some reason, when something new has to be added, we found ourselves modifying the code, then that logic is probably poorly designed. Ideally, when requirements change, we want to just have to extend the module with...

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Clean Code in Python
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