Learning Python - Mark Lutz [245]
At least, that’s the case for assignment to argument names themselves. When arguments are passed mutable objects like lists and dictionaries, we also need to be aware that in-place changes to such objects may live on after a function exits, and hence impact callers. Here’s an example that demonstrates this behavior:
>>> def changer(a, b): # Arguments assigned references to objects
... a = 2 # Changes local name's value only
... b[0] = 'spam' # Changes shared object in-place
...
>>> X = 1
>>> L = [1, 2] # Caller
>>> changer(X, L) # Pass immutable and mutable objects
>>> X, L # X is unchanged, L is different!
(1, ['spam', 2])
In this code, the changer function assigns values to argument a itself, and to a component of the object referenced by argument b. These two assignments within the function are only slightly different in syntax but have radically different results:
Because a is a local variable name in the function’s scope, the first assignment has no effect on the caller—it simply changes the local variable a to reference a completely different object, and does not change the binding of the name X in the caller’s scope. This is the same as in the prior example.
Argument b is a local variable name, too, but it is passed a mutable object (the list that L references in the caller’s scope). As the second assignment is an in-place object change, the result of the assignment to b[0] in the function impacts the value of L after the function returns.
Really, the second assignment statement in changer doesn’t change b—it changes part of the object that b currently references. This in-place change impacts the caller only because the changed object outlives the function call. The name L hasn’t changed either—it still references the same, changed object—but it seems as though L differs after the call because the value it references has been modified within the function.
Figure 18-1 illustrates the name/object bindings that exist immediately after the function has been called, and before its code has run.
Figure 18-1. References: arguments. Because arguments are passed by assignment, argument names in the function may share objects with variables in the scope of the call. Hence, in-place changes to mutable arguments in a function can impact the caller. Here, a and b in the function initially reference the objects referenced by variables X and L when the function is first called. Changing the list through variable b makes L appear different after the call returns.
If this example is still confusing, it may help to notice that the effect of the automatic assignments of the passed-in arguments is the same as running a series of simple assignment statements. In terms of the first argument, the assignment has no effect on the caller:
>>> X = 1
>>> a = X # They share the same object
>>> a = 2 # Resets 'a' only, 'X' is still 1
>>> print(X)
1
The assignment through the second argument does affect a variable at the call, though, because it is an in-place object change:
>>> L = [1, 2]
>>> b = L # They share the same object
>>> b[0] = 'spam' # In-place change: 'L' sees the change too
>>> print(L)
['spam', 2]
If you recall our discussions about shared mutable objects in Chapters 6 and 9, you’ll recognize the phenomenon at work: changing a mutable object in-place can impact other references to that object. Here, the effect is to make one of the arguments work like both an input and an output of the function.
Avoiding Mutable Argument Changes
This behavior of in-place changes to mutable arguments isn’t a bug—it’s simply the way argument passing works in Python. Arguments are passed to functions by reference (a.k.a. pointer) by default because that is what we normally want. It means we can pass large objects around our programs without making multiple copies along the way, and we can easily update these objects as we go. In fact, as we’ll see in Part VI, Python’s class model depends upon changing