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
  • Table of Contents
  • Mastering Object-oriented Python
  • Credits
  • About the Author
  • About the Reviewers
  • www.PacktPub.com
    • Support files, eBooks, discount offers and more
      • Why Subscribe?
      • Free Access for Packt account holders
  • Preface
    • What this book covers
    • What you need for this book
    • Who this book is for
    • Conventions
    • Reader feedback
      • Customer support
      • Downloading the example code for this book
      • Errata
      • Piracy
      • Questions
  • Some Preliminaries
    • About casino Blackjack
      • Playing the game
      • Blackjack player strategies
      • Object design for simulating Blackjack
    • Performance the timeit module
    • Testing unittest and doctest
      • Unit testing and technology spikes
    • Docstrings RST markup and documentation tools
    • The IDE question
    • About special method names
    • Summary
  • 1. Pythonic Classes via Special Methods
    • Pythonic Classes via Special Methods
    • 1. 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
        • Faulty factory design and the vague else clause
        • Simplicity and consistency using elif sequences
        • Simplicity using mapping and class objects
          • Two parallel mappings
          • Mapping to a tuple of values
          • The partial function solution
          • Fluent APIs for factories
      • Implementing __init__() in each subclass
      • Simple composite objects
        • Wrapping a collection class
        • Extending a collection class
        • More requirements and another design
      • Complex composite objects
        • Complete composite object initialization
      • Stateless objects without __init__()
      • Some additional class definitions
      • Multi-strategy __init__()
        • More complex initialization alternatives
        • Initializing static methods
      • Yet more __init__() techniques
        • Initialization with type validation
        • Initialization, encapsulation, and privacy
      • Summary
    • 2. Integrating Seamlessly with Python Basic Special Methods
      • The __repr__() and __str__() methods
        • Non collection __str__() and __repr__()
        • Collection __str__() and __repr__()
      • The __format__() method
        • Nested formatting specifications
        • Collections and delegating format specifications
      • The __hash__() method
        • Deciding what to hash
        • Inheriting definitions for immutable objects
        • Overriding definitions for immutable objects
        • Overriding definitions for mutable objects
        • Making a frozen hand from a mutable hand
      • The __bool__() method
      • The __bytes__() method
      • The comparison operator methods
        • Designing comparisons
        • Implementation of comparison for objects of the same class
        • Implementation of comparison for objects of mixed classes
        • Hard totals, soft totals, and polymorphism
        • A mixed class comparison example
      • The __del__() method
        • The reference count and destruction
        • Circular references and garbage collection
        • Circular references and the weakref module
        • The __del__() and close() methods
      • The __new__() method and immutable objects
      • The __new__() method and metaclasses
        • Metaclass example 1 ordered attributes
        • Metaclass example 2 self-reference
      • Summary
    • 3. Attribute Access, Properties, and Descriptors
      • Basic attribute processing
        • Attributes and the __init__() method
      • Creating properties
        • Eagerly computed properties
        • Setter and deleter properties
      • Using special methods for attribute access
        • Creating immutable objects with __slots__
        • Creating immutable objects as a tuple subclass
        • Eagerly computed attributes
      • The __getattribute__() method
      • Creating descriptors
        • Using a nondata descriptor
        • Using a data descriptor
      • Summary, design considerations, and trade-offs
        • Properties versus attributes
        • Designing with descriptors
        • Looking forward
    • 4. The ABCs of Consistent Design
      • Abstract base classes
      • Base classes and polymorphism
      • Callables
      • Containers and collections
      • Numbers
      • Some additional abstractions
        • The iterator abstraction
        • Contexts and context managers
      • The abc module
      • Summary, design considerations, and trade-offs
        • Looking forward
    • 5. Using Callables and Contexts
      • Designing with ABC callables
      • Improving performance
        • Using memoization or caching
      • Using functools for memoization
        • Aiming for simplicity using the callable API
      • Complexities and the callable API
      • Managing contexts and the with statement
        • Using the decimal context
        • Other contexts
      • Defining the __enter__() and __exit__() methods
        • Handling exceptions
      • Context manager as a factory
        • Cleaning up in a context manager
      • Summary
        • Callable design considerations and trade-offs
        • Context manager design considerations and trade-offs
        • Looking forward
    • 6. Creating Containers and Collections
      • ABCs of collections
      • Examples of special methods
      • Using the standard library extensions
        • The namedtuple() function
        • The deque class
        • The ChainMap use case
        • The OrderedDict collection
        • The defaultdict subclass
        • The counter collection
      • Creating new kinds of collections
      • Defining a new kind of sequence
        • A statistical list
        • Choosing eager versus lazy calculation
        • Working with __getitem__(), __setitem__(), __delitem__(), and slices
        • Implementing __getitem__(), __setitem__(), and __delitem__()
        • Wrapping a list and delegating
        • Creating iterators with __iter__()
      • Creating a new kind of mapping
      • Creating a new kind of set
        • Some design rationale
        • Defining the Tree class
        • Defining the TreeNode class
        • Demonstrating the binary tree set
      • Summary
        • Design considerations and Trade-offs
        • Looking forward
    • 7. Creating Numbers
      • ABCs of numbers
        • Deciding which types to use
        • The method resolution and the reflected operator concept
      • The arithmetic operators special methods
      • Creating a numeric class
        • Defining FixedPoint initialization
        • Defining FixedPoint binary arithmetic operators
        • Defining FixedPoint unary arithmetic operators
        • Implementing FixedPoint reflected operators
        • Implementing FixedPoint comparison operators
      • Computing a numeric hash
        • Designing more useful rounding
      • Implementing other special methods
      • Optimization with the in-place operators
      • Summary
        • Design considerations and trade-offs
        • Looking forward
    • 8. Decorators and Mixins Cross-cutting Aspects
      • Class and meaning
        • Constructing the functions
        • Constructing the class
        • Some class design principles
        • Aspect-oriented programming
      • Using built-in decorators
        • Using standard library decorators
      • Using standard library mixin classes
        • Using the context manager mixin class
        • Turning off a class feature
      • Writing a simple function decorator
        • Creating separate loggers
      • Parameterizing a decorator
      • Creating a method function decorator
      • Creating a class decorator
      • Adding method functions to a class
      • Using decorators for security
      • Summary
        • Design considerations and trade-offs
        • Looking forward
  • 2. Persistence and Serialization
    • Persistence and Serialization
    • 9. Serializing and Saving JSON, YAML, Pickle, CSV, and XML
      • Understanding persistence, class, state, and representation
        • Common Python terminologies
      • Filesystem and network considerations
      • Defining classes to support persistence
        • Rendering a blog and posts
      • Dumping and loading with JSON
        • Supporting JSON in our classes
        • Customizing JSON encoding
        • Customizing JSON decoding
        • The security and the eval() issue
        • Refactoring the encode function
        • Standardizing the date string
        • Writing JSON to a file
      • Dumping and loading with YAML
        • Formatting YAML data on a file
        • Extending the YAML representation
        • Security and safe loading
      • Dumping and loading with pickle
        • Designing a class for reliable pickle processing
        • Security and the global issue
      • Dumping and loading with CSV
        • Dumping simple sequences to CSV
        • Loading simple sequences from CSV
        • Handling containers and complex classes
        • Dumping and loading multiple row types in a CSV file
        • Filtering CSV rows with an iterator
        • Dumping and loading joined rows in a CSV file
      • Dumping and loading with XML
        • Dumping objects using string templates
        • Dumping objects with xml.etree.ElementTree
        • Loading XML documents
      • Summary
        • Design considerations and trade-offs
        • Schema evolution
        • Looking forward
    • 10. Storing and Retrieving Objects via Shelve
      • Analyzing persistent object use cases
        • The ACID properties
      • Creating a shelf
      • Designing shelvable objects
        • Designing keys for our objects
        • Generating surrogate keys for objects
        • Designing a class with a simple key
        • Designing classes for containers or collections
        • Referring to objects via foreign keys
        • Designing CRUD operations for complex objects
      • Searching, scanning, and querying
      • Designing an access layer for shelve
        • Writing a demonstration script
      • Creating indexes to improve efficiency
        • Creating top-level indices
      • Adding yet more index maintenance
      • The writeback alternative to index updates
        • Schema evolution
      • Summary
        • Design considerations and trade-offs
        • Application software layers
        • Looking forward
    • 11. Storing and Retrieving Objects via SQLite
      • SQL databases, persistence, and objects
        • The SQL data model rows and tables
        • CRUD processing via SQL DML statements
        • Querying rows with the SQL SELECT statement
        • SQL transactions and the ACID properties
        • Designing primary and foreign database keys
      • Processing application data with SQL
        • Implementing class-like processing in pure SQL
      • Mapping Python objects to SQLite BLOB columns
      • Mapping Python objects to database rows manually
        • Designing an access layer for SQLite
        • Implementing container relationships
      • Improving performance with indices
      • Adding an ORM layer
        • Designing ORM-friendly classes
        • Building the schema with the ORM layer
        • Manipulating objects with the ORM layer
      • Querying post objects given a tag string
      • Improving performance with indices
        • Schema evolution
      • Summary
        • Design considerations and trade-offs
        • Mapping alternatives
        • Keys and key designs
        • Application software layers
        • Looking forward
    • 12. Transmitting and Sharing Objects
      • Class, state, and representation
      • Using HTTP and REST to transmit objects
        • Implementing CRUD operations via REST
        • Implementing non-CRUD operations
        • The REST protocol and ACID
        • Choosing a representation JSON, XML, or YAML
      • Implementing a REST server WSGI and mod_wsgi
        • Creating a simple REST application and server
        • Implementing a REST client
        • Demonstrating and unit testing the RESTful services
      • Using Callable classes for WSGI applications
        • Designing RESTful object identifiers
        • Multiple layers of REST services
        • Creating the roulette server
        • Creating the roulette client
      • Creating a secure REST service
        • The WSGI Authentication application
      • Implementing REST with a web application framework
      • Using a message queue to transmit objects
        • Defining processes
        • Building queues and supplying data
      • Summary
        • Design considerations and trade-offs
        • Schema evolution
        • Application software layers
        • Looking forward
    • 13. Configuration Files and Persistence
      • Configuration file use cases
      • Representation, persistence, state, and usability
        • Application configuration design patterns
        • Configuring via object construction
        • Implementing a configuration hierarchy
      • Storing the configuration in the INI files
      • Handling more literals via the eval() variants
      • Storing the configuration in PY files
        • Configuration via class definitions
        • Configuration via SimpleNamespace
        • Using Python with exec() for the configuration
      • Why is exec() a nonproblem?
      • Using ChainMap for defaults and overrides
      • Storing the configuration in JSON or YAML files
        • Using flattened JSON configurations
        • Loading a YAML configuration
      • Storing the configuration in property files
        • Parsing a properties file
        • Using a properties file
      • Storing the configuration in XML files PLIST and others
        • Customized XML configuration files
      • Summary
        • Design considerations and trade-offs
        • Creating a shared configuration
        • Schema evolution
        • Looking Forward
  • 3. Testing, Debugging, Deploying, and Maintaining
    • Testing, Debugging, Deploying, and Maintaining
    • 14. The Logging and Warning Modules
      • Creating a basic log
        • Creating a shared class-level logger
        • Configuring the loggers
        • Starting up and shutting down the logging system
        • Naming the loggers
        • Extending the logger levels
        • Defining handlers for multiple destinations
        • Managing the propagation rules
      • Configuration gotcha
      • Specializing logging for control, debug, audit, and security
        • Creating a debugging log
        • Creating audit and security logs
      • Using the warnings module
        • Showing API changes with a warning
        • Showing configuration problems with a warning
        • Showing possible software problems with a warning
      • Advanced logging the last few messages and network destinations
        • Building an automatic tail buffer
        • Sending logging messages to a remote process
        • Preventing queue overrun
      • Summary
        • Design considerations and trade-offs
        • Looking forward
    • 15. Designing for Testability
      • Defining and isolating units for testing
        • Minimizing the dependencies
        • Creating simple unit tests
        • Creating a test suite
        • Including edge and corner cases
        • Mocking dependencies for testing
        • Using more mocks to test more behaviors
      • Using doctest to define test cases
        • Combining doctest and unittest
        • Creating a more complete test package
      • Using setup and teardown
        • Using setup and teardown with OS resources
        • Using setup and teardown with databases
      • The TestCase class hierarchy
      • Using externally defined expected results
      • Automated integration or performance testing
      • Summary
        • Design considerations and trade-offs
        • Looking forward
    • 16. Coping With the Command Line
      • The OS interface and the command line
        • Arguments and options
      • Parsing the command line with argparse
        • A simple on/off option
        • An option with an argument
        • Positional arguments
        • All other arguments
        • --version display and exit
        • --help display and exit
      • Integrating command-line options and environment variables
        • Providing more configurable defaults
        • Overriding configuration file settings with environment variables
        • Overriding environment variables with the configuration files
        • Making the configuration aware of the None values
      • Customizing the help output
      • Creating a top-level main() function
        • Ensuring DRY for the configuration
        • Managing nested configuration contexts
      • Programming In The Large
        • Designing command classes
        • Adding the analysis command subclass
        • Adding and packaging more features into an application
        • Designing a higher-level composite command
      • Additional composite command design patterns
      • Integrating with other applications
      • Summary
        • Design considerations and trade-offs
        • Looking forward
    • 17. The Module and Package Design
      • Designing a module
        • Some module design patterns
        • Module versus class
        • The expected content of a module
      • Whole module versus module items
      • Designing a package
        • Designing a module-package hybrid
        • Designing a package with alternate implementations
      • Designing a main script and the __main__ module
        • Creating an executable script file
        • Creating a __main__ module
        • Programming in the large
      • Designing long-running applications
      • Organizing code into src, bin, and test
      • Installing Python modules
      • Summary
        • Design considerations and trade-offs
        • Looking forward
    • 18. Quality and Documentation
      • Writing docstrings for the help() function
      • Using pydoc for documentation
      • Better output via the RST markup
        • Blocks of text
        • The RST inline markup
        • RST directives
        • Learning RST
      • Writing effective docstrings
      • Writing file-level docstrings, including modules and packages
        • Writing API details in RST markup
        • Writing class and method function docstrings
        • Writing function docstrings
      • More sophisticated markup techniques
      • Using Sphinx to produce the documentation
        • Using the Sphinx quickstart
        • Writing the Sphinx documentation
        • Filling in the 4+1 views for documentation
        • Writing the implementation document
        • Creating the Sphinx cross-references
        • Refactoring Sphinx files into directories
      • Writing the documentation
      • Literate programming
        • Use cases for literate programming
        • Working with a literate programming tool
      • Summary
        • Design considerations and trade-offs
  • Index

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