Book Image

Advanced Python Programming

By : Dr. Gabriele Lanaro, Quan Nguyen, Sakis Kasampalis
Book Image

Advanced Python Programming

By: Dr. Gabriele Lanaro, Quan Nguyen, Sakis Kasampalis

Overview of this book

This Learning Path shows you how to leverage the power of both native and third-party Python libraries for building robust and responsive applications. You will learn about profilers and reactive programming, concurrency and parallelism, as well as tools for making your apps quick and efficient. You will discover how to write code for parallel architectures using TensorFlow and Theano, and use a cluster of computers for large-scale computations using technologies such as Dask and PySpark. With the knowledge of how Python design patterns work, you will be able to clone objects, secure interfaces, dynamically choose algorithms, and accomplish much more in high performance computing. By the end of this Learning Path, you will have the skills and confidence to build engaging models that quickly offer efficient solutions to your problems. This Learning Path includes content from the following Packt products: • Python High Performance - Second Edition by Gabriele Lanaro • Mastering Concurrency in Python by Quan Nguyen • Mastering Python Design Patterns by Sakis Kasampalis
Table of Contents (41 chapters)
Title Page
About Packt

The asyncio framework

By now, you should have a solid foundation of how concurrency works, and how to use callbacks and futures. We can now move on and learn how to use the asyncio package present in the standard library since version 3.4. We will also explore the brand new 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 that 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 demonstrates the usage of these basic methods by scheduling a callback that will print a message and halt the loop:

    import asyncio

    loop ...