Advanced Python Programming: Accelerate your Python programs using proven techniques and design patterns, 2nd Edition
Author: Quan Nguyen
Publisher Finelybook 出版社：Packt Publishing; 2nd ed. edition (March 25, 2022)
pages 页数：606 pages
Write fast, robust, and highly reusable applications using Python’s internal optimization, state-of-the-art performance-benchmarking tools, and cutting-edge libraries
Benchmark, profile, and accelerate Python programs using optimization tools
Scale applications to multiple processors with concurrent programming
Make applications robust and reusable using effective design patterns
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 Author: 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.
Author: 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.
What you will learn
Write efficient numerical code with NumPy, pandas, and Xarray
Use Cython and Numba to achieve native performance
Find bottlenecks in your Python code using profilers
Optimize your machine learning models with JAX
Implement multithreaded, multiprocessing, and asynchronous programs
Solve common problems in concurrent programming, such as deadlocks
Tackle architecture challenges with design patterns