This book introduces the terminology of the object-oriented paradigm. It focuses on object-oriented design with step-by-step examples. It guides us from simple inheritance, one of the most useful tools in the object-oriented programmer's toolbox through exception handling to design patterns, an object-oriented way of looking at object-oriented concepts.
Along the way, we'll learn to integrate the object-oriented and not-so-object-oriented aspects of the Python programming language. We will learn the complexities of string and file manipulation, emphasizing (as Python 3 does) the difference between binary and textual data.
We'll then cover the joys of unit testing, using not one, but two unit testing frameworks. Finally, we'll explore, through Python's various concurrency paradigms, how to make objects work well together at the same time.
This book is loosely divided into four major parts. In the first four chapters, we will dive into the formal principles of object-oriented programming and how Python leverages them. In chapters 5 through 8, we will cover some of Python's idiosyncratic applications of these principles by learning how they are applied to a variety of Python's built-in functions. Chapters 9 through 11 cover design patterns, and the final two chapters discuss two bonus topics related to Python programming that may be of interest.
Chapter 1, Object-oriented Design, covers important object-oriented concepts. It deals mainly with terminology such as abstraction, classes, encapsulation, and inheritance. We also briefly look at UML to model our classes and objects.
Chapter 2, Objects in Python, discusses classes and objects and how they are used in Python. We will learn about attributes and behaviors on Python objects, and also the organization of classes into packages and modules. Lastly, we will see how to protect our data.
Chapter 3, When Objects Are Alike, gives us a more in-depth look into inheritance. It covers multiple inheritance and shows us how to extend built-ins. This chapter also covers how polymorphism and duck typing work in Python.
Chapter 4, Expecting the Unexpected, looks into exceptions and exception handling. We will learn how to create our own exceptions and how to use exceptions for program flow control.
Chapter 5, When to Use Object-oriented Programming, deals with creating and using objects. We will see how to wrap data using properties and restrict data access. This chapter also discusses the DRY principle and how not to repeat code.
Chapter 6, Python Data Structures, covers the object-oriented features of Python's built-in classes. We'll cover tuples, dictionaries, lists, and sets, as well as a few more advanced collections. We'll also see how to extend these standard objects.
Chapter 7, Python Object-oriented Shortcuts, as the name suggests, deals with time-savers in Python. We will look at many useful built-in functions such as method overloading using default arguments. We'll also see that functions themselves are objects and how this is useful.
Chapter 8, Strings and Serialization, looks at strings, files, and formatting. We'll discuss the difference between strings, bytes, and bytearrays, as well as various ways to serialize textual, object, and binary data to several canonical representations.
Chapter 9, The Iterator Pattern, introduces us to the concept of design patterns and covers Python's iconic implementation of the iterator pattern. We'll learn about list, set, and dictionary comprehensions. We'll also demystify generators and coroutines.
Chapter 10, Python Design Patterns I, covers several design patterns, including the decorator, observer, strategy, state, singleton, and template patterns. Each pattern is discussed with suitable examples and programs implemented in Python.
Chapter 11, Python Design Patterns II, wraps up our discussion of design patterns with coverage of the adapter, facade, flyweight, command, abstract, and composite patterns. More examples of how idiomatic Python code differs from canonical implementations are provided.
Chapter 12, Testing Object-oriented Programs, opens with why testing is so important in Python applications. It emphasizes test-driven development and introduces two different testing suites: unittest and py.test. Finally, it discusses mocking test objects and code coverage.
Chapter 13, Concurrency, is a whirlwind tour of Python's support (and lack thereof) of concurrency patterns. It discusses threads, multiprocessing, futures, and the new AsyncIO library.
Each chapter includes relevant examples and a case study that collects the chapter's contents into a working (if not complete) program.
All the examples in this book rely on the Python 3 interpreter. Make sure you are not using Python 2.7 or earlier. At the time of writing, Python 3.4 was the latest release of Python. Most examples will work on earlier revisions of Python 3, but you are encouraged to use the latest version to minimize frustration.
All of the examples should run on any operating system supported by Python. If this is not the case, please report it as a bug.
Some of the examples need a working Internet connection. You'll probably want to have one of these for extracurricular research and debugging anyway!
In addition, some of the examples in this book rely on third-party libraries that do not ship with Python. These are introduced within the book at the time they are used, so you do not need to install them in advance. However, for completeness, here is a list:
pip
requests
pillow
bitarray
This book specifically targets people who are new to object-oriented programming. It assumes you have basic Python skills. You'll learn object-oriented principles in depth. It is particularly useful for system administrator types who have used Python as a "glue" language and would like to improve their programming skills.
If you are familiar with object-oriented programming in other languages, then this book will help you understand the idiomatic ways to apply your knowledge in the Python ecosystem.
This book uses a variety of text styles to distinguish between different kinds of information. Here are some examples of these styles, and an explanation of their meaning.
Code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles are shown as follows: "We look up the class in the dictionary and store it in a variable named PropertyClass."
A block of code is set as follows:
def add_property(self):
property_type = get_valid_input(
"What type of property? ",
("house", "apartment")).lower()
payment_type = get_valid_input(
"What payment type? ",
("purchase", "rental")).lower()When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:
def add_property(self): property_type = get_valid_input( "What type of property? ", ("house", "apartment")).lower() payment_type = get_valid_input( "What payment type? ", ("purchase", "rental")).lower()
Any command-line input or output is written as follows:
>>> c1 = Contact("John A", "johna@example.net") >>> c2 = Contact("John B", "johnb@example.net") >>> c3 = Contact("Jenna C", "jennac@example.net") >>> [c.name for c in Contact.all_contacts.search('John')] ['John A', 'John B']
New terms and important words are shown in bold. Words that you see on the screen, in menus or dialog boxes for example, appear in the text like this: "It will fail with a not enough arguments error similar to the one we received earlier when we forgot the self argument."
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