Book Image

Python 3 Object-Oriented Programming - Second Edition

By : Dusty Phillips
Book Image

Python 3 Object-Oriented Programming - Second Edition

By: Dusty Phillips

Overview of this book

Python 3 is more versatile and easier to use than ever. It runs on all major platforms in a huge array of use cases. Coding in Python minimizes development time and increases productivity in comparison to other languages. Clean, maintainable code is easy to both read and write using Python's clear, concise syntax. Object-oriented programming is a popular design paradigm in which data and behaviors are encapsulated in such a way that they can be manipulated together. Many modern programming languages utilize the powerful concepts behind object-oriented programming and Python is no exception. Starting with a detailed analysis of object-oriented analysis and design, you will use the Python programming language to clearly grasp key concepts from the object-oriented paradigm. This book fully explains classes, data encapsulation, inheritance, polymorphism, abstraction, and exceptions with an emphasis on when you can use each principle to develop well-designed software. You'll get an in-depth analysis of many common object-oriented design patterns that are more suitable to Python's unique style. This book will not just teach Python syntax, but will also build your confidence in how to program. You will also learn how to create maintainable applications by studying higher level design patterns. Following this, you'll learn the complexities of string and file manipulation, and how Python distinguishes between binary and textual data. Not one, but two very powerful automated testing systems will be introduced in the book. After you discover the joy of unit testing and just how easy it can be, you'll study higher level libraries such as database connectors and GUI toolkits and learn how they uniquely apply object-oriented principles. You'll learn how these principles will allow you to make greater use of key members of the Python eco-system such as Django and Kivy. This new edition includes all the topics that made Python 3 Object-oriented Programming an instant Packt classic. It's also packed with updated content to reflect recent changes in the core Python library and covers modern third-party packages that were not available on the Python 3 platform when the book was first published.  

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Python 3 Object-Oriented Programming - Second Edition
Dusty Phillips
Aug, 2015 | 460 pages
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Table of Contents (21 chapters)
Python 3 Object-oriented Programming Second Edition
Python 3 Object-oriented Programming Second Edition
Credits
Credits
About the Author
About the Author
About the Reviewers
About the Reviewers
www.PacktPub.com
www.PacktPub.com
Introduction to the second edition
Introduction to the second edition
Preface
Preface
Free Chapter
1
Object-oriented Design
Object-oriented Design
Introducing object-oriented
Objects and classes
Specifying attributes and behaviors
Hiding details and creating the public interface
Composition
Inheritance
Case study
Exercises
Summary
2
Objects in Python
Objects in Python
Creating Python classes
Modules and packages
Organizing module contents
Who can access my data?
Third-party libraries
Case study
Exercises
Summary
3
When Objects Are Alike
When Objects Are Alike
Basic inheritance
Multiple inheritance
Polymorphism
Abstract base classes
Case study
Exercises
Summary
4
Expecting the Unexpected
Expecting the Unexpected
Raising exceptions
Case study
Exercises
Summary
5
When to Use Object-oriented Programming
When to Use Object-oriented Programming
Treat objects as objects
Adding behavior to class data with properties
Manager objects
Case study
Exercises
Summary
6
Python Data Structures
Python Data Structures
Empty objects
Tuples and named tuples
Dictionaries
Lists
Sets
Extending built-ins
Queues
Case study
Exercises
Summary
7
Python Object-oriented Shortcuts
Python Object-oriented Shortcuts
Python built-in functions
An alternative to method overloading
Functions are objects too
Case study
Exercises
Summary
8
Strings and Serialization
Strings and Serialization
Strings
Regular expressions
Serializing objects
Case study
Exercises
Summary
9
The Iterator Pattern
The Iterator Pattern
Design patterns in brief
Iterators
Comprehensions
Generators
Coroutines
Case study
Exercises
Summary
10
Python Design Patterns I
Python Design Patterns I
The decorator pattern
The observer pattern
The strategy pattern
The state pattern
The singleton pattern
The template pattern
Exercises
Summary
11
Python Design Patterns II
Python Design Patterns II
The adapter pattern
The facade pattern
The flyweight pattern
The command pattern
The abstract factory pattern
The composite pattern
Exercises
Summary
12
Testing Object-oriented Programs
Testing Object-oriented Programs
Why test?
Unit testing
Testing with py.test
Imitating expensive objects
How much testing is enough?
Case study
Exercises
Summary
13
Concurrency
Concurrency
Threads
Multiprocessing
Futures
AsyncIO
Case study
Exercises
Summary
Index
Index

Imitating expensive objects

Sometimes, we want to test code that requires an object be supplied that is either expensive or difficult to construct. While this may mean your API needs rethinking to have a more testable interface (which typically means a more usable interface), we sometimes find ourselves writing test code that has a ton of boilerplate to set up objects that are only incidentally related to the code under test.

For example, imagine we have some code that keeps track of flight statuses in a key-value store (such as redis or memcache) such that we can store the timestamp and the most recent status. A basic version of such code might look like this:

import datetime
import redis

class FlightStatusTracker:
    ALLOWED_STATUSES = {'CANCELLED', 'DELAYED', 'ON TIME'}

    def __init__(self):
        self.redis = redis.StrictRedis()

    def change_status(self, flight, status):
        status = status.upper()
        if status not in self.ALLOWED_STATUSES:
            raise ValueError...
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