Book Image

Python for Geeks

By : Muhammad Asif
Book Image

Python for Geeks

By: Muhammad Asif

Overview of this book

Python is a multipurpose language that can be used for multiple use cases. Python for Geeks will teach you how to advance in your career with the help of expert tips and tricks. You'll start by exploring the different ways of using Python optimally, both from the design and implementation point of view. Next, you'll understand the life cycle of a large-scale Python project. As you advance, you'll focus on different ways of creating an elegant design by modularizing a Python project and learn best practices and design patterns for using Python. You'll also discover how to scale out Python beyond a single thread and how to implement multiprocessing and multithreading in Python. In addition to this, you'll understand how you can not only use Python to deploy on a single machine but also use clusters in private as well as in public cloud computing environments. You'll then explore data processing techniques, focus on reusable, scalable data pipelines, and learn how to use these advanced techniques for network automation, serverless functions, and machine learning. Finally, you'll focus on strategizing web development design using the techniques and best practices covered in the book. By the end of this Python book, you'll be able to do some serious Python programming for large-scale complex projects.
Table of Contents (20 chapters)
1
Section 1: Python, beyond the Basics
5
Section 2: Advanced Programming Concepts
9
Section 3: Scaling beyond a Single Thread
13
Section 4: Using Python for Web, Cloud, and Network Use Cases

Going beyond a single CPU – implementing multiprocessing

We have seen the complexity of multithreaded programming and its limitations. The question is whether the complexity of multithreading is worth the effort. It may be worth it for I/O-related tasks but not for general application use cases, especially when an alternative approach exists. The alternative approach is to use multiprocessing because separate Python processes are not constrained by the GIL and execution can happen in parallel. This is especially beneficial when applications run on multicore processors and involve intensive CPU-demanding tasks. In reality, the use of multiprocessing is the only option in Python's built-in libraries to utilize multiple processor cores.

Graphics Processing Units (GPUs) provide a greater number of cores than regular CPUs and are considered more suitable for data processing tasks, especially when executing them in parallel. The only caveat is that in order to execute a data...