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Mastering Object-Oriented Python

Mastering Object-Oriented Python

By : Steven F. Lott
3.8 (4)
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Mastering Object-Oriented Python

Mastering Object-Oriented Python

3.8 (4)
By: Steven F. Lott

Overview of this book

Object-oriented programming (OOP) is a relatively complex discipline to master, and it can be difficult to see how general principles apply to each language's unique features. With the help of the latest edition of Mastering Objected-Oriented Python, you'll be shown how to effectively implement OOP in Python, and even explore Python 3.x. Complete with practical examples, the book guides you through the advanced concepts of OOP in Python, and demonstrates how you can apply them to solve complex problems in OOP. You will learn how to create high-quality Python programs by exploring design alternatives and determining which design offers the best performance. Next, you'll work through special methods for handling simple object conversions and also learn about hashing and comparison of objects. As you cover later chapters, you'll discover how essential it is to locate the best algorithms and optimal data structures for developing robust solutions to programming problems with minimal computer processing. Finally, the book will assist you in leveraging various Python features by implementing object-oriented designs in your programs. By the end of this book, you will have learned a number of alternate approaches with different attributes to confidently solve programming problems in Python.
Table of Contents (25 chapters)
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Preface
Who this book is for
What this book covers
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Using a message queue to transmit objects

The multiprocessing module uses both the serialization and transmission of objects. We can use queues and pipes to serialize objects that are then transmitted to other processes. There are numerous external projects to provide sophisticated message queue processing. We'll focus on the multiprocessing queue because it's built in to Python and works nicely.

For high-performance applications, a faster message queue may be necessary. It may also be necessary to use a faster serialization technique than pickling. For this chapter, we'll focus only on the Python design issues. The multiprocessing module relies on pickle to encode objects. See Chapter 10, Serializing and Saving – JSON, YAML, Pickle, CSV, and XML, for more information. We can't provide a restricted unpickler easily; therefore, this module offers...

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