Book Image

Python Object-Oriented Programming - Fourth Edition

By : Steven F. Lott, Dusty Phillips
2 (2)
Book Image

Python Object-Oriented Programming - Fourth Edition

2 (2)
By: Steven F. Lott, Dusty Phillips

Overview of this book

Object-oriented programming (OOP) is a popular design paradigm in which data and behaviors are encapsulated in such a way that they can be manipulated together. Python Object-Oriented Programming, Fourth Edition dives deep into the various aspects of OOP, Python as an OOP language, common and advanced design patterns, and hands-on data manipulation and testing of more complex OOP systems. These concepts are consolidated by open-ended exercises, as well as a real-world case study at the end of every chapter, newly written for this edition. All example code is now compatible with Python 3.9+ syntax and has been updated with type hints for ease of learning. Steven and Dusty provide a comprehensive, illustrative tour of important OOP concepts, such as inheritance, composition, and polymorphism, and explain how they work together with Python’s classes and data structures to facilitate good design. In addition, the book also features an in-depth look at Python’s exception handling and how functional programming intersects with OOP. Two very powerful automated testing systems, unittest and pytest, are introduced. The final chapter provides a detailed discussion of Python's concurrent programming ecosystem. By the end of the book, you will have a thorough understanding of how to think about and apply object-oriented principles using Python syntax and be able to confidently create robust and reliable programs.
Table of Contents (17 chapters)
Other Books You May Enjoy


We've looked closely at a variety of topics related to concurrent processing in Python:

  • Threads have an advantage of simplicity for many cases. This has to be balanced against the GIL interfering with compute-intensive multi-threading.
  • Multiprocessing has an advantage of making full use of all cores of a processor. This has to be balanced against interprocess communication costs. If shared memory is used, there is the complication of encoding and accessing the shared objects.
  • The concurrent.futures module defines an abstraction – the future – that can minimize the differences in application programming used for accessing threads or processes. This makes it easy to switch and see which approach is fastest.
  • The async/await features of the Python language are supported by the AsyncIO package. Because these are coroutines, there isn't true parallel processing; control switches among the coroutines allow a single thread to interleave...