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Home > Programming > Object Oriented Programming > Mastering Object-oriented Python
Mastering Object-oriented Python
Mastering Object-oriented Python

Mastering Object-oriented Python: If you want to master object-oriented Python programming this book is a must-have. With 750 code samples and a relaxed tutorial, it's a seamless route to programming Python.

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Mastering Object-oriented Python

Chapter 2. Integrating Seamlessly with Python Basic Special Methods

There are a number of special methods that permit close integration between our classes and Python. Standard Library Reference calls them basic. A better term might be foundational or essential. These special methods form a foundation for building classes that seamlessly integrate with other Python features.

For example, we need string representations of a given object's value. The base class, object, has a default implementation of __repr__() and __str__() that provides string representations of an object. Sadly, these default representations are remarkably uninformative. We'll almost always want to override one or both of these default definitions. We'll also look at __format__(), which is a bit more sophisticated but serves the same purpose.

We'll also look at other conversions, specifically __hash__(), __bool__(), and __bytes__(). These methods will convert an object into a number, a true/false value, or a string of bytes...

The __repr__() and __str__() methods

Python has two string representations of an object. These are closely aligned with the built-in functions repr(), str(), print(), and the string.format() method.

  • Generally, the str() method representation of an object is commonly expected to be more friendly to humans. This is built by an object's __str__() method.

  • The repr() method representation is often going to be more technical, perhaps even a complete Python expression to rebuild the object. The documentation says:

    For many types, this function makes an attempt to return a string that would yield an object with the same value when passed to eval().

    This is built by an object's __repr__() method.

  • The print() function will use str() to prepare an object for printing.

  • The format() method of a string can also access these methods. When we use {!r} or {!s} formatting, we're requesting __repr__() or __str__(), respectively.

Let's look at the default implementations first.

The following is a simple class hierarchy...

The __format__() method

The __format__() method is used by string.format() as well as the format() built-in function. Both of these interfaces are used to get presentable string versions of a given object.

The following are the two ways in which arguments will be presented to __format__():

  • someobject.__format__(""): This happens when the application does format(someobject) or something equivalent to "{0}".format(someobject). In these cases, a zero-length string specification was provided. This should produce a default format.

  • someobject.__format__(specification): This happens when the application does format(someobject, specification) or something equivalent to "{0:specification}".format(someobject).

Note that something equivalent to "{0!r}".format() or "{0!s}".format() doesn't use the __format__() method. These use __repr__() or __str__() directly.

With a specification of "", a sensible response is return str(self). This provides an obvious consistency between the various string representations...

The __hash__() method

The built-in hash() function invokes the __hash__() method of a given object. This hash is a calculation which reduces a (potentially complex) value to a small integer value. Ideally, a hash reflects all the bits of the source value. Other hash calculations—often used for cryptographic purposes—can produce very large values.

Python includes two hash libraries. The cryptographic-quality hash functions are in hashlib. The zlib module has two high-speed hash functions: adler32() and crc32(). For relatively simple values, we don't use either of these. For large, complex values, these algorithms can be of help.

The hash() function (and the associated __hash__() method) is used to create a small integer key that is used to work with collections such as set, frozenset, and dict. These collections use the hash value of an immutable object to rapidly locate the object in the collection.

Immutability is important here; we'll mention it many times. Immutable objects don't change...

The __bool__() method

Python has a pleasant definition of falsity. The reference manual lists a large number of values that will test as equivalent to False. This includes things such as False, 0, '', (), [], and {}. Most other objects will test as equivalent to True.

Often, we'll want to check for an object being "not empty" with a simple statement as follows:

if some_object:
    process( some_object )

Under the hood, this is the job of the bool() built-in function. This function depends on the __bool__() method of a given object.

The default __bool__() method returns True. We can see this with the following code:

>>> x = object()
>>> bool(x)
True

For most classes, this is perfectly valid. Most objects are not expected to be False. For collections, however, this is not appropriate. An empty collection should be equivalent to False. A nonempty collection can return True. We might want to add a method like this to our Deck objects.

If we're wrapping a list, we might have something...

The __bytes__() method

There are relatively few occasions to transform an object into bytes. We'll look at this in detail in Part 2, Persistence and Serialization.

In the most common situation, an application can create a string representation, and the built-in encoding capabilities of the Python IO classes will be used to transform the string into bytes. This works perfectly for almost all situations. The main exception would be when we're defining a new kind of string. In that case, we'd need to define the encoding of that string.

The bytes() function does a variety of things, depending on the arguments:

  • bytes(integer): This returns an immutable bytes object with the given number of 0x00 values.

  • bytes(string): This will encode the given string into bytes. Additional parameters for encoding and error handling will define the details of the encoding process.

  • bytes(something): This will invoke something.__bytes__() to create a bytes object. The encoding or error arguments will not be used here...

The comparison operator methods

Python has six comparison operators. These operators have special method implementations. According to the documentation, the mapping works as follows:

  • x<y calls x.__lt__(y)

  • x<=y calls x.__le__(y)

  • x==y calls x.__eq__(y)

  • x!=y calls x.__ne__(y)

  • x>y calls x.__gt__(y)

  • x>=y calls x.__ge__(y)

We'll return to comparison operators again when looking at numbers in Chapter 7, Creating Numbers.

There's an additional rule regarding what operators are actually implemented that's relevant here. These rules are based on the idea that the object's class on the left defines the required special method. If it doesn't, Python can try an alternative operation by changing the order.

Tip

Here are the two basic rules

First, the operand on the left is checked for an operator implementation: A<B means A.__lt__(B).

Second, the operand on the right is checked for a reversed operator implementation: A<B means B.__gt__(A).

The rare exception to this occurs when the...

The __del__() method

The __del__() method has a rather obscure use case.

The intent is to give an object a chance to do any cleanup or finalization just before the object is removed from memory. This use case is handled much more cleanly by context manager objects and the with statement. This is the subject of Chapter 5, Using Callables and Contexts. Creating a context is much more predictable than dealing with __del__() and the Python garbage collection algorithm.

In the case where a Python object has a related OS resource, the __del__() method is a last chance to cleanly disentangle the resource from the Python application. As examples, a Python object that conceals an open file, a mounted device, or perhaps a child subprocess might all benefit from having the resource released as part of __del__() processing.

The __del__() method is not invoked at any easy-to-predict time. It's not always invoked when the object is deleted by a del statement, nor is it always invoked when an object is deleted...

The __new__() method and immutable objects

One use case for the __new__() method is to initialize objects that are otherwise immutable. The __new__() method is where our code can build an uninitialized object. This allows processing before the __init__() method is called to set the attribute values of the object.

The __new__() method is used to extend the immutable classes where the __init__() method can't easily be overridden.

The following is a class that does not work. We'll define a version of float that carries around information on units:

class Float_Fail( float ):
    def __init__( self, value, unit ):
        super().__init__( value )
        self.unit = unit

We're trying (improperly) to initialize an immutable object.

The following is what happens when we try to use this class definition:

>>> s2 = Float_Fail( 6.5, "knots" )
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
TypeError: float() takes at most 1 argument (2 given)

From this, we see...

The __new__() method and metaclasses

The other use case for the __new__() method as a part of a metaclass is to control how a class definition is built. This is distinct from how __new__() controls building an immutable object, shown previously.

A metaclass builds a class. Once a class object has been built, the class object is used to build instances. The metaclass of all class definitions is type. The type() function is used to create class objects.

Additionally, the type() function can be used as a function to reveal the class of an object.

The following is a silly example of building a new, nearly useless class directly with type() as a constructor:

Useless= type("Useless",(),{})

Once we've created this class, we can create objects of this Useless class. However, they won't do much because they have no methods or attributes.

We can use this newly-minted Useless class to create objects, for what little it's worth. The following is an example:

>>> Useless()
<__main__.Useless object...

Summary

We've looked at a number of basic special methods, which are essential features of any class that we design. These methods are already part of every class, but the defaults we inherit from the object may not match our processing requirements.

We'll almost always have a need to override __repr__(), __str__(), and __format__(). The default implementations of these methods aren't very helpful at all.

We rarely need to override __bool__() unless we're writing our own collection. That's the subject of Chapter 6, Creating Containers and Collections.

We often need to override the comparison and __hash__() methods. The definitions are suitable for simple immutable objects but not at all appropriate for mutable objects. We may not need to write all the comparison operators; we'll look at the @functools.total_ordering decorator in Chapter 8, Decorators and Mixins – Cross-cutting Aspects.

The other two basic special method names, __new__() and __del__(), are for more specialized purposes. Using...

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What you will learn

  • Create applications with flexible logging, powerful configuration and commandline options, automated unit tests, and good documentation
  • Get to grips with different design patterns for the __init__() method
  • Design callable objects and context managers
  • Perform object serialization in formats such as JSON, YAML, Pickle, CSV, and XML
  • Map Python objects to a SQL database using the builtin SQLite module
  • Transmit Python objects via RESTful web services
  • Devise strategies for automated unit testing, including how to use the doctest and the unittest.mock module
  • Parse commandline arguments and integrate this with configuration files and environment variables
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Publication date : Apr 22, 2014
Length: 634 pages
Edition :
Language : English
ISBN-13 : 9781783280971
Category :
Programming
Languages :
Python
Concepts :
Object Oriented Programming

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Publication date : Apr 22, 2014
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Edition :
Language : English
ISBN-13 : 9781783280971
Category :
Programming
Languages :
Python
Concepts :
Object Oriented Programming

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Table of Contents

18 Chapters
The __init__() Method Chevron down icon Chevron up icon
The __init__() Method
The implicit superclass – object
The base class object __init__() method
Implementing __init__() in a superclass
Using __init__() to create manifest constants
Leveraging __init__() via a factory function
Implementing __init__() in each subclass
Simple composite objects
Complex composite objects
Stateless objects without __init__()
Some additional class definitions
Multi-strategy __init__()
Yet more __init__() techniques
Summary
Integrating Seamlessly with Python Basic Special Methods Chevron down icon Chevron up icon
Integrating Seamlessly with Python Basic Special Methods
The __repr__() and __str__() methods
The __format__() method
The __hash__() method
The __bool__() method
The __bytes__() method
The comparison operator methods
The __del__() method
The __new__() method and immutable objects
The __new__() method and metaclasses
Summary
Attribute Access, Properties, and Descriptors Chevron down icon Chevron up icon
Attribute Access, Properties, and Descriptors
Basic attribute processing
Creating properties
Using special methods for attribute access
The __getattribute__() method
Creating descriptors
Summary, design considerations, and trade-offs
The ABCs of Consistent Design Chevron down icon Chevron up icon
The ABCs of Consistent Design
Abstract base classes
Base classes and polymorphism
Callables
Containers and collections
Numbers
Some additional abstractions
The abc module
Summary, design considerations, and trade-offs
Using Callables and Contexts Chevron down icon Chevron up icon
Using Callables and Contexts
Designing with ABC callables
Improving performance
Using functools for memoization
Complexities and the callable API
Managing contexts and the with statement
Defining the __enter__() and __exit__() methods
Context manager as a factory
Summary
Creating Containers and Collections Chevron down icon Chevron up icon
Creating Containers and Collections
ABCs of collections
Examples of special methods
Using the standard library extensions
Creating new kinds of collections
Defining a new kind of sequence
Creating a new kind of mapping
Creating a new kind of set
Summary
Creating Numbers Chevron down icon Chevron up icon
Creating Numbers
ABCs of numbers
The arithmetic operator's special methods
Creating a numeric class
Computing a numeric hash
Implementing other special methods
Optimization with the in-place operators
Summary
Decorators and Mixins – Cross-cutting Aspects Chevron down icon Chevron up icon
Decorators and Mixins – Cross-cutting Aspects
Class and meaning
Using built-in decorators
Using standard library mixin classes
Writing a simple function decorator
Parameterizing a decorator
Creating a method function decorator
Creating a class decorator
Adding method functions to a class
Using decorators for security
Summary
Serializing and Saving – JSON, YAML, Pickle, CSV, and XML Chevron down icon Chevron up icon
Serializing and Saving – JSON, YAML, Pickle, CSV, and XML
Understanding persistence, class, state, and representation
Filesystem and network considerations
Defining classes to support persistence
Dumping and loading with JSON
Dumping and loading with YAML
Dumping and loading with pickle
Dumping and loading with CSV
Dumping and loading with XML
Summary
Storing and Retrieving Objects via Shelve Chevron down icon Chevron up icon
Storing and Retrieving Objects via Shelve
Analyzing persistent object use cases
Creating a shelf
Designing shelvable objects
Searching, scanning, and querying
Designing an access layer for shelve
Creating indexes to improve efficiency
Adding yet more index maintenance
The writeback alternative to index updates
Summary
Storing and Retrieving Objects via SQLite Chevron down icon Chevron up icon
Storing and Retrieving Objects via SQLite
SQL databases, persistence, and objects
Processing application data with SQL
Mapping Python objects to SQLite BLOB columns
Mapping Python objects to database rows manually
Improving performance with indices
Adding an ORM layer
Querying post objects given a tag string
Improving performance with indices
Summary
Transmitting and Sharing Objects Chevron down icon Chevron up icon
Transmitting and Sharing Objects
Class, state, and representation
Using HTTP and REST to transmit objects
Implementing a REST server – WSGI and mod_wsgi
Using Callable classes for WSGI applications
Creating a secure REST service
Implementing REST with a web application framework
Using a message queue to transmit objects
Summary
Configuration Files and Persistence Chevron down icon Chevron up icon
Configuration Files and Persistence
Configuration file use cases
Representation, persistence, state, and usability
Storing the configuration in the INI files
Handling more literals via the eval() variants
Storing the configuration in PY files
Why is exec() a nonproblem?
Using ChainMap for defaults and overrides
Storing the configuration in JSON or YAML files
Storing the configuration in property files
Storing the configuration in XML files – PLIST and others
Summary
The Logging and Warning Modules Chevron down icon Chevron up icon
The Logging and Warning Modules
Creating a basic log
Configuration gotcha
Specializing logging for control, debug, audit, and security
Using the warnings module
Advanced logging – the last few messages and network destinations
Summary
Designing for Testability Chevron down icon Chevron up icon
Designing for Testability
Defining and isolating units for testing
Using doctest to define test cases
Using setup and teardown
The TestCase class hierarchy
Using externally defined expected results
Automated integration or performance testing
Summary
Coping With the Command Line Chevron down icon Chevron up icon
Coping With the Command Line
The OS interface and the command line
Parsing the command line with argparse
Integrating command-line options and environment variables
Customizing the help output
Creating a top-level main() function
Programming In The Large
Additional composite command design patterns
Integrating with other applications
Summary
The Module and Package Design Chevron down icon Chevron up icon
The Module and Package Design
Designing a module
Whole module versus module items
Designing a package
Designing a main script and the __main__ module
Designing long-running applications
Organizing code into src, bin, and test
Installing Python modules
Summary
Quality and Documentation Chevron down icon Chevron up icon
Quality and Documentation
Writing docstrings for the help() function
Using pydoc for documentation
Better output via the RST markup
Writing effective docstrings
Writing file-level docstrings, including modules and packages
More sophisticated markup techniques
Using Sphinx to produce the documentation
Writing the documentation
Literate programming
Summary

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David Jul 01, 2017
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A+ thanks!
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Z-Car Man Sep 10, 2015
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Although I have worked in all areas of the software lifecycle I was new to Python a year ago. When I heard I would be programming in Python I brought a number of books aimed at the intermediate Python programmer. These were expensive and verbose, and failed to hit the mark. How you can write over 1000 pages and skimp some of the fundamentals is beyond me. My original “C” book, Kernigan and Richie, was just over 200 pages and to the point. Word processors have a lot to answer for! My exposure to OO has been limited (C# for a few months and not explained well) and some wxPython. With the project I am now working on I felt I needed a good book but not a door stop. After researching quite a bit I came across this book and felt it was worth buying. At nearly 600 pages it is not a small book but Steven Lott has kept it to the point and relevant throughout. Many authors on software related subjects could learn a lot on how to structure a book from him.The book is broken down into three main sections: Pythonic Classes via Special Methods, Persistence and Serialization and Testing, Debugging, Deploying, and Maintaining. This makes it more manageable. My OO skills in Python have grown markedly in a week. Highly recommended.
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As represented; no problems
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Mike Driscoll May 16, 2014
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This is one of Packt’s best books and also one of the best advanced Python books I’ve read. Let’s take a few moments to talk about the chapters. The book is based around the concept of casino Blackjack, which is kind of an odd topic for a programming book. Regardless, the author uses it and a few other examples to help demonstrate some fairly advanced topics in Python. The first chapter is all about Python __init__() method. It shows the reader how to use __init__ in a superclass and a factory function. The second chapter jumps into all of Python’s basic special methods, such as __repr__, __format__, __hash__, etc. You will learn a lot about metaprogramming in these first couple of chapters. Also note that this book is for Python 3 only.Chapter 3 digs into attributes, properties and descriptors. You will learn how to use __slots__, create immutable objects and work with “eagerly computer attributes”. Chapters 4-6 are about creating and working with callables, contexts and containers. There is information about memoization, creating custom callables, how to use __enter__ / __exit__, working with the collections module (deque, ChainMap, OrderedDict, etc) and more! Chapter 7 talks about creating your own Number, which was something I’d never considered doing. The author admits that you normally wouldn’t do it either, but he does teach the reader some interesting concepts (numeric hashes and in-place operators). Chapter 8 finishes up Part 1 with information on decorators and mixins.Part 2 is all about persistence and serialization. Chapter 9 focuses on JSON, YAML and Pickle. The author favors YAML, so you’ll see a lot of examples using it in this section. Chapter 10 digs into using Python shelve objects and using CRUD operations in relation to complex objects. Chapter 11 is about SQLite. Chapter 12 goes into details of using Python to create a REST server and create WSGI applications. Chapter 13 rounds out Part 2 by covering configuration files using Python, JSON, YAML and PLIST.The last section of the book covers testing, debugging, deploying and maintaining. It jumps right in with chapter 14′s topics on the logging and warning modules. Chapter 15 details how to create unittests and doctests in Python. Chapter 16 covers working with command line options via argparse and creating a main() function. In chapter 17, we learn how to design modules and packages. The last chapter of the book goes over quality assurance and documentation. You will learn a bit about the RST markup language and Sphinx for creating documentation.I found Part 1 to be the most interesting part of the book. I learned a great deal about how classes work, metaprogramming techniques and the difference between callables and functions. I think the book is worth purchasing just for that section! Part 2 has a lot of interesting items too, although I question the author’s insistence on using YAML over JSON. I also do not understand why PLIST was even covered as a configuration file type. Part 3 seemed a bit rushed to me. The chapters aren’t as detailed nor the examples as interesting. On the other hand, I may be a bit jaded as this section was mostly material that I already knew. Overall, I found this to be one of the best Python books I’ve read in the last few years. I would definitely recommend it to anyone who wants to learn more about Python’s internals, especially Python’s “magic methods”.
Amazon Verified review Amazon
Louis P. Mar 14, 2016
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Great content on python classes and some other topics (serialization, etc.). I like a book better than having to google every step or search the documentation. I don't use it anymore but it was a great way to make progress with python.
Amazon Verified review Amazon

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About the author

Profile icon Steven F. Lott
Steven F. Lott
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Steven Lott has been programming since computers were large, expensive, and rare. Working for decades in high tech has given him exposure to a lot of ideas and techniques, some bad, but most are helpful to others. Since the 1990s, Steven has been engaged with Python, crafting an array of indispensable tools and applications. His profound expertise has led him to contribute significantly to Packt Publishing, penning notable titles like "Mastering Object-Oriented," "The Modern Python Cookbook," and "Functional Python Programming." A self-proclaimed technomad, Steven's unconventional lifestyle sees him residing on a boat, often anchored along the vibrant east coast of the US. He tries to live by the words “Don't come home until you have a story.”
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