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

Numba is a tool that compiles fast, specialized versions of Python functions at runtime. In this chapter, we learned how to compile, inspect, and analyze functions compiled by Numba. We also learned how to implement fast NumPy universal functions that are useful in a wide array of numerical applications. Finally, we implemented more complex data structures using the nb.jitclass decorator. Overall, Numba is built to accelerate numeric loops that are common in scientific computing. As we have seen, Numba works seamlessly with the popular NumPy library.

Tools such as PyPy allow us to run Python programs unchanged to obtain significant speed improvements. We demonstrated how to set up PyPy, and we assessed the performance improvements on our particle simulator application. We have also seen that, unlike Numba, PyPy doesn't operate on a function level but instead seeks to implement a more efficient interpreter for a whole Python program.

We also, briefly, described the...