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

The asyncio framework

At this point, we have seen how concurrency works and how to use callbacks and futures. We can now move on and learn how to use the asyncio package, which has been present in the standard Python library since version 3.4. We will also explore the async/await syntax to deal with asynchronous programming in a very natural way.

As a first example, we will see how to retrieve and execute a simple callback using asyncio. The asyncio loop can be retrieved by calling the asyncio.get_event_loop() function. We can schedule a callback for execution using loop.call_later, which takes a delay in seconds and a callback. We can also use the loop.stop method to halt the loop and exit the program. To start processing the scheduled call, it is necessary to start the loop, which can be done using loop.run_forever. The following example in example7.py demonstrates the usage of these basic methods by scheduling a callback that will print a message and halt the loop:

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