book

Python 3 Object-Oriented Programming - Second Edition

by Dusty Phillips

August 2015

Beginner to intermediate

460 pages

11h 20m

English

Packt Publishing

Read now

Unlock full access

Support files, eBooks, discount offers, and moreWhy subscribe?Free access for Packt account holders
What this book covers

Downloading the example codeErrataPiracyQuestions
Introducing object-oriented
Data describes objectsBehaviors are actions
Inheritance provides abstractionMultiple inheritance
Creating Python classesAdding attributesMaking it do somethingTalking to yourselfMore argumentsInitializing the objectExplaining yourself
Organizing the modulesAbsolute importsRelative imports
Basic inheritanceExtending built-insOverriding and super
The diamond problemDifferent sets of arguments
Using an abstract base classCreating an abstract base classDemystifying the magic
Raising exceptionsRaising an exceptionThe effects of an exceptionHandling exceptionsThe exception hierarchyDefining our own exceptions
Treat objects as objects
Properties in detailDecorators – another way to create propertiesDeciding when to use properties
Removing duplicate codeIn practice
Empty objects
Named tuples
Dictionary use casesUsing defaultdictCounter
Sorting lists
FIFO queuesLIFO queuesPriority queues
Python built-in functionsThe len() functionReversedEnumerateFile I/OPlacing it in context
Default argumentsVariable argument listsUnpacking arguments
Using functions as attributesCallable objects
StringsString manipulationString formattingEscaping bracesKeyword argumentsContainer lookupsObject lookupsMaking it look rightStrings are UnicodeConverting bytes to textConverting text to bytesMutable byte strings
Matching patternsMatching a selection of charactersEscaping charactersMatching multiple charactersGrouping patterns togetherGetting information from regular expressionsMaking repeated regular expressions efficient
Customizing picklesSerializing web objects
Design patterns in brief
The iterator protocol
List comprehensionsSet and dictionary comprehensionsGenerator expressions
Yield items from another iterable
Back to log parsingClosing coroutines and throwing exceptionsThe relationship between coroutines, generators, and functions
The decorator patternA decorator exampleDecorators in Python
An observer example
A strategy exampleStrategy in Python
A state exampleState versus strategyState transition as coroutines
Singleton implementation
A template example
The adapter pattern
Why test?Test-driven development
Assertion methodsReducing boilerplate and cleaning upOrganizing and running testsIgnoring broken tests
One way to do setup and cleanupA completely different way to set up variablesSkipping tests with py.test
Implementing it
ThreadsThe many problems with threadsShared memoryThe global interpreter lockThread overhead
Multiprocessing poolsQueuesThe problems with multiprocessing
AsyncIO in actionReading an AsyncIO futureAsyncIO for networkingUsing executors to wrap blocking codeStreamsExecutors

Content preview from Python 3 Object-Oriented Programming - Second Edition

Regular expressions

You know what's really hard to do using object-oriented principles? Parsing strings to match arbitrary patterns, that's what. There have been a fair number of academic papers written in which object-oriented design is used to set up string parsing, but the result is always very verbose and hard to read, and they are not widely used in practice.

In the real world, string parsing in most programming languages is handled by regular expressions. These are not verbose, but, boy, are they ever hard to read, at least until you learn the syntax. Even though regular expressions are not object oriented, the Python regular expression library provides a few classes and objects that you can use to construct and run regular expressions.