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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

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

Steven F. Lott

Eлектронна книга
  • Mastering Object-oriented Python
    • Table of Contents
    • Mastering Object-oriented Python
    • Credits
    • About the Author
    • About the Reviewers
    • www.PacktPub.com
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        • 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
  • Назва: 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
  • Автор: Steven F. Lott
  • Оригінальна назва: 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.
  • ISBN: 9781783280988, 9781783280988
  • Дата видання: 2014-04-22
  • Формат: Eлектронна книга
  • Ідентифікатор видання: e_3bln
  • Видавець: Packt Publishing