Learning Python - Mark Lutz [341]
Once you get used to programming this way (by software customization), you’ll find that when it’s time to write a new program, much of your work may already be done—your task largely becomes one of mixing together existing superclasses that already implement the behavior required by your program. For example, someone else might have written the Employee, Reader, and Writer classes in this example for use in a completely different program. If so, you get all of that person’s code “for free.”
In fact, in many application domains, you can fetch or purchase collections of superclasses, known as frameworks, that implement common programming tasks as classes, ready to be mixed into your applications. These frameworks might provide database interfaces, testing protocols, GUI toolkits, and so on. With frameworks, you often simply code a subclass that fills in an expected method or two; the framework classes higher in the tree do most of the work for you. Programming in such an OOP world is just a matter of combining and specializing already debugged code by writing subclasses of your own.
Of course, it takes a while to learn how to leverage classes to achieve such OOP utopia. In practice, object-oriented work also entails substantial design work to fully realize the code reuse benefits of classes—to this end, programmers have begun cataloging common OOP structures, known as design patterns, to help with design issues. The actual code you write to do OOP in Python, though, is so simple that it will not in itself pose an additional obstacle to your OOP quest. To see why, you’ll have to move on to Chapter 26.
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[57] In other literature, you may also occasionally see the terms base classes and derived classes used to describe superclasses and subclasses, respectively.
[58] If you’ve ever used C++ or Java, you’ll recognize that Python’s self is the same as the this pointer, but self is always explicit in Python to make attribute accesses more obvious.
[59] Note that the company list in this example could be stored in a file with Python object pickling, introduced in Chapter 9 when we met files, to yield a persistent employee database. Python also comes with a module named shelve, which would allow you to store the pickled representation of the class instances in an access-by-key filesystem; the third-party open source ZODB system does the same but has better support for production-quality object-oriented databases.
Chapter Summary
We took an abstract look at classes and OOP in this chapter, taking in the big picture before we dive into syntax details. As we’ve seen, OOP is mostly about looking up attributes in trees of linked objects; we call this lookup an inheritance search. Objects at the bottom of the tree inherit attributes from objects higher up in the tree—a feature that enables us to program by customizing code, rather than changing it, or starting from scratch. When used well, this model of programming can cut development time radically.
The next chapter will begin to fill in the coding details behind the picture painted here. As we get deeper into Python classes, though, keep in mind that the OOP model in Python is very simple; as I’ve already stated, it’s really just about looking up attributes in object trees. Before we move on, here’s a quick quiz to review what we’ve covered here.
Test Your Knowledge: Quiz
What is the main point of OOP in Python?
Where does an inheritance search look for an attribute?
What is the difference between a class object and an instance object?
Why is the first argument in a class method function special?
What is the __init__ method used for?
How do you create a class instance?
How do you create a class?
How do you specify a class’s superclasses?
Test Your Knowledge: Answers
OOP is about code reuse—you factor code to minimize redundancy and program by customizing what already exists instead of changing code in-place or starting from