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Clean Code in Python

Clean Code in Python - Second Edition

By : Anaya
4.6 (34)
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Clean Code in Python

Clean Code in Python

4.6 (34)
By: 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.
Table of Contents (13 chapters)
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11
Other Books You May Enjoy
12
Index

Documentation

This section is about documenting code in Python, from within the code. Good code is self-explanatory but is also well-documented. It is a good idea to explain what it is supposed to do (not how).

One important distinction: documenting code is not the same as adding comments to it. This section intends to explore docstrings and annotations because they're the tools in Python used to document code. That said, parenthetically, I will briefly touch on the subject of code comments, just to establish some points that will make a clearer distinction.

Code documentation is important in Python, because being dynamically typed, it might be easy to get lost in the values of variables or objects across functions and methods. For this reason, stating this information will make it easier for future readers of the code.

There is another reason that specifically relates to annotations. They can also help in running some automatic checks, such as type hinting...

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Clean Code in Python
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