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

An overview of the threading module

There are a lot of choices when it comes to implementing multithreaded programs in Python. One of the most common ways to work with threads in Python is through the threading module. Before we dive into the module's usage and its syntax, first, let's explore the thread model, which was previously the main thread-based development module in Python.

The thread module in Python 2

Before the threading module became popular, the primary thread-based development module was thread. If you are using an older version of Python 2, it is possible to use the module as it is. However, according to the module documentation page, the thread module was, in fact, renamed _thread in Python 3.

For readers that have been working with the thread module to build multithreaded applications and are looking to port their code from Python 2 to Python 3, the 2to3 tool might be a solution. The 2to3 tool handles most of the detectable incompatibilities between the different versions...