Book Image

Python Object-Oriented Programming - Fourth Edition

By : Steven F. Lott, Dusty Phillips
2 (1)
Book Image

Python Object-Oriented Programming - Fourth Edition

2 (1)
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)
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Generator functions

Generator functions embody the essential features of a generator expression, which is the generalization of a comprehension. The generator function syntax looks even less object-oriented than anything we've seen, but we'll discover that once again, it is a syntax shortcut to create a kind of iterator object. It helps us build processing following the standard iterator-filter-mapping pattern.

Let's take the log file example a little further. If we want to decompose the log into columns, we'll have to do a more significant transformation as part of the mapping step. This will involve a regular expression to find the timestamp, the severity word, and the message as a whole. We'll look at a number of solutions to this problem to show how generators and generator functions can be applied to create the objects we want.

Here's a version, avoiding generator expressions entirely:

import csv
import re
from pathlib import Path