Book Image

Advanced Python Programming - Second Edition

By : Quan Nguyen
Book Image

Advanced Python Programming - Second Edition

By: Quan Nguyen

Overview of this book

Python's powerful capabilities for implementing robust and efficient programs make it one of the most sought-after programming languages. In this book, you'll explore the tools that allow you to improve performance and take your Python programs to the next level. This book starts by examining the built-in as well as external libraries that streamline tasks in the development cycle, such as benchmarking, profiling, and optimizing. You'll then get to grips with using specialized tools such as dedicated libraries and compilers to increase your performance at number-crunching tasks, including training machine learning models. The book covers concurrency, a major solution to making programs more efficient and scalable, and various concurrent programming techniques such as multithreading, multiprocessing, and asynchronous programming. You'll also understand the common problems that cause undesirable behavior in concurrent programs. Finally, you'll work with a wide range of design patterns, including creational, structural, and behavioral patterns that enable you to tackle complex design and architecture challenges, making your programs more robust and maintainable. By the end of the book, you'll be exposed to a wide range of advanced functionalities in Python and be equipped with the practical knowledge needed to apply them to your use cases.
Table of Contents (32 chapters)
1
Section 1: Python-Native and Specialized Optimization
8
Section 2: Concurrency and Parallelism
18
Section 3: Design Patterns in Python

Introducing the decorator pattern

As Python developers, we can write decorators in a Pythonic way (meaning using the language's features), thanks to the built-in decorator feature (https://docs.python.org/3/reference/compound_stmts.html#function). What exactly is this feature? A Python decorator is a callable (function, method, or class) that gets a function object, func_in, as input and returns another function object, func_out. It is a commonly used technique for extending the behavior of a function, method, or class.

But this feature should not be completely new to you. We have already seen how to use the built-in property decorator, which makes a method appear as a variable in both Chapter 16, The Factory Pattern, and Chapter 17, The Builder Pattern. There are also several other useful built-in decorators in Python. In the Implementation section of this chapter, we will learn how to implement and use our own decorators.

Note that there is no one-to-one relationship between...