Book Image

Advanced Python Programming - Second Edition

By : Quan Nguyen
Book Image

Advanced Python Programming - Second Edition

By: Quan Nguyen

Overview of this book

Python's powerful capabilities for implementing robust and efficient programs make it one of the most sought-after programming languages. In this book, you'll explore the tools that allow you to improve performance and take your Python programs to the next level. This book starts by examining the built-in as well as external libraries that streamline tasks in the development cycle, such as benchmarking, profiling, and optimizing. You'll then get to grips with using specialized tools such as dedicated libraries and compilers to increase your performance at number-crunching tasks, including training machine learning models. The book covers concurrency, a major solution to making programs more efficient and scalable, and various concurrent programming techniques such as multithreading, multiprocessing, and asynchronous programming. You'll also understand the common problems that cause undesirable behavior in concurrent programs. Finally, you'll work with a wide range of design patterns, including creational, structural, and behavioral patterns that enable you to tackle complex design and architecture challenges, making your programs more robust and maintainable. By the end of the book, you'll be exposed to a wide range of advanced functionalities in Python and be equipped with the practical knowledge needed to apply them to your use cases.
Table of Contents (32 chapters)
1
Section 1: Python-Native and Specialized Optimization
8
Section 2: Concurrency and Parallelism
18
Section 3: Design Patterns in Python

Summary

While the GIL in Python offers a simple and intuitive solution to one of the more difficult problems in the language, it also raises a number of problems of its own, concerning the ability to run multiple threads in a Python program to process CPU-bound tasks. Multiple attempts have been made to remove the GIL from the main implementation of Python, but none has been able to achieve it while maintaining the effectiveness of processing non-CPU-bound tasks, which are affected by the GIL.

Overall, we have discussed practical methods that make working with the GIL easier. We have also learned that while it possesses considerable notoriety among the Python community, the GIL only affects a certain portion of the ecosystem. This should better inform our opinion about the GIL.

In the last four chapters, we discussed some of the most well-known and common problems in concurrent programming in Python. For the remainder of the book, we will be looking at a different topic—...