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

Summary

In this chapter, we discussed different options of concurrent programming in Python using the standard libraries. We started with multithreading with an introduction to the core concepts of concurrent programming. We introduced the challenges with multithreading, such as the GIL, which allows only one thread at a time to access Python objects. The concepts of locking and synchronization were explored with practical examples of Python code. We also discussed the types of task that multithreaded programming is more effective for using a case study.

We studied how to achieve concurrency using multiple processes in Python. With multiprocessing programming, we learned how to share data between processes using shared memory and the server process, and also how to exchange objects safely between processes using the Queue object and the Pipe object. In the end, we built the same case study as we did for the multithreading example, but using processes instead. Then, we introduced...