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
Copyright
About Packt
Contributors
Preface
Index

The asyncio framework in action


As you have seen, asyncio provides a simple and intuitive way to implement the framework of an asynchronous program with Python's asynchronous programming keywords. With that, let's consider the process of applying the framework provided to a synchronous application in Python, and convert it to an asynchronous one.

Asynchronously counting down

Let's take a look at theChapter17/example1.pyfile, as follows:

# Chapter17/example1.py

import time

def count_down(name, delay):
    indents = (ord(name) - ord('A')) * '\t'

    n = 3
    while n:
        time.sleep(delay)

        duration = time.perf_counter() - start
        print('-' * 40)
        print('%.4f \t%s%s = %i' % (duration, indents, name, n))

        n -= 1

start = time.perf_counter()

count_down('A', 1)
count_down('B', 0.8)
count_down('C', 0.5)

print('-' * 40)
print('Done.')

The goal of this example is to illustrate the asynchronous nature of overlapping the processing and waiting time of independent...