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

Chapter 15

  1. C++ associates a variable with its value by simply writing the value to the memory location of the variable; Python has its variables reference point to the memory location of the values that they hold. For this reason, Python needs to maintain a reference count for every value in its memory space.
  2. To avoid race conditions and, consequently, value reference counts from being corrupted, the GIL is implemented so that only one thread can access and mutate the counts at any given time.
  3. The GIL effectively prevents multiple threads from taking advantage of the CPU and executing CPU-bound instructions at the same time. This means that if multiple threads that are meant to be executed concurrently are CPU-bound, they will be executed sequentially.
  4. There are a few ways to deal with the GIL in your Python applications; namely, implementing multiprocessing instead of multithreading and utilizing other, alternative Python interpreters.