Learning Python - Mark Lutz [227]
>>> x = intersect([1, 2, 3], (1, 4)) # Mixed types
>>> x # Saved result object
[1]
This time, we passed in different types of objects to our function—a list and a tuple (mixed types)—and it still picked out the common items. Because you don’t have to specify the types of arguments ahead of time, the intersect function happily iterates through any kind of sequence objects you send it, as long as they support the expected interfaces.
For intersect, this means that the first argument has to support the for loop, and the second has to support the in membership test. Any two such objects will work, regardless of their specific types—that includes physically stored sequences like strings and lists; all the iterable objects we met in Chapter 14, including files and dictionaries; and even any class-based objects we code that apply operator overloading techniques (we’ll discuss these later in the book).[35]
Here again, if we pass in objects that do not support these interfaces (e.g., numbers), Python will automatically detect the mismatch and raise an exception for us—which is exactly what we want, and the best we could do on our own if we coded explicit type tests. By not coding type tests and allowing Python to detect the mismatches for us, we both reduce the amount of code we need to write and increase our code’s flexibility.
Local Variables
Probably the most interesting part of this example is its names. It turns out that the variable res inside intersect is what in Python is called a local variable—a name that is visible only to code inside the function def and that exists only while the function runs. In fact, because all names assigned in any way inside a function are classified as local variables by default, nearly all the names in intersect are local variables:
res is obviously assigned, so it is a local variable.
Arguments are passed by assignment, so seq1 and seq2 are, too.
The for loop assigns items to a variable, so the name x is also local.
All these local variables appear when the function is called and disappear when the function exits—the return statement at the end of intersect sends back the result object, but the name res goes away. To fully explore the notion of locals, though, we need to move on to Chapter 17.
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[35] This code will always work if we intersect files’ contents obtained with file.readlines(). It may not work to intersect lines in open input files directly, though, depending on the file object’s implementation of the in operator or general iteration. Files must generally be rewound (e.g., with a file.seek(0) or another open) after they have been read to end-of-file once. As we’ll see in Chapter 29 when we study operator overloading, classes implement the in operator either by providing the specific __contains__ method or by supporting the general iteration protocol with the __iter__ or older __getitem__ methods; if coded, classes can define what iteration means for their data.
Chapter Summary
This chapter introduced the core ideas behind function definition—the syntax and operation of the def and return statements, the behavior of function call expressions, and the notion and benefits of polymorphism in Python functions. As we saw, a def statement is executable code that creates a function object at runtime; when the function is later called, objects are passed into it by assignment (recall that assignment means object reference in Python, which, as we learned in Chapter 6, really means pointer internally), and computed values are sent back by return. We also began exploring the concepts of local variables and scopes in this chapter, but we’ll save all the details on those topics for Chapter 17. First, though, a quick quiz.
Test Your Knowledge: Quiz
What is the point of coding functions?
At what time does Python create a function?
What does a function return if it has no return statement in it?
When does the code nested inside the function definition statement