Mastering Object-oriented Python

Book Image

Mastering Object-oriented Python

By : Steven F. Lott, Steven F. Lott

Book Image

Mastering Object-oriented Python

By: Steven F. Lott, Steven F. Lott

Overview of this book

Mastering Object-oriented Python

Mastering Object-oriented Python

Credits

About the Author

About the Author

About the Reviewers

About the Reviewers

www.PacktPub.com

www.PacktPub.com

Preface

Some Preliminaries

Some Preliminaries

Free Chapter

The __init__() Method

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

Integrating Seamlessly with Python Basic Special Methods

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

Attribute Access, Properties, and Descriptors

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

The ABCs of Consistent Design

Abstract base classes

Base classes and polymorphism

Containers and collections

Some additional abstractions

Summary, design considerations, and trade-offs

Using Callables and Contexts

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

Creating Containers and Collections

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

Creating Numbers

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

Decorators and Mixins – Cross-cutting Aspects

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

Serializing and Saving – JSON, YAML, Pickle, CSV, and XML

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

Storing and Retrieving Objects via Shelve

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

Storing and Retrieving Objects via SQLite

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

Transmitting and Sharing Objects

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

Configuration Files and Persistence

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

The Logging and Warning Modules

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

Designing for Testability

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

Coping With the Command Line

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

The Module and Package Design

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

Quality and Documentation

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

Index

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Using externally defined expected results

For some applications, users can articulate processing rules that describe the software's behavior. In other cases, the job of an analyst or a designer transforms the user's desires into procedural descriptions of the software.

In many cases, it's easier for users to provide concrete examples of expected results. For some business-oriented applications, the users may be more comfortable creating a spreadsheet that shows us sample inputs and expected results. Working from user-supplied, concrete sample data can simplify the developing software.

Whenever possible, have real users produce concrete examples of correct results. Creating procedural descriptions or software specifications is remarkably difficult. Creating concrete examples and generalizing from the examples to a software specification is less fraught with complexity and confusion. Further, it plays into a style of development where the test cases drive the development effort. Given a suite...