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Advanced Python Programming

Advanced Python Programming - Second Edition

By : Quan Nguyen
4.6 (14)
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Advanced Python Programming

Advanced Python Programming

4.6 (14)
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)
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1
Section 1: Python-Native and Specialized Optimization
8
Section 2: Concurrency and Parallelism
18
Section 3: Design Patterns in Python

Good concurrent image processing practices

So far, you have most likely realized that image processing is quite an involved process and that implementing concurrent and parallel programming in an image processing application can add more complexity to our work. There are, however, good practices that will guide us in the right direction while developing our image processing applications. The following sections discuss some of the most common practices that we should keep in mind.

Choosing the correct way (out of many)

We have hinted at this practice briefly when we learned about thresholding. How an image processing application handles and processes its image data heavily depends on the problems it is supposed to solve, and what kind of data will be fed to it. Therefore, there is significant variability when it comes to choosing specific parameters when processing your image.

For example, as we saw earlier, there are various ways to threshold an image, and each will result...

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Advanced Python Programming
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