Learning Python - Mark Lutz [101]
>>> L = [1, 2, 3]
>>> M = [1, 2, 3] # M and L reference different objects
>>> L == M # Same values
True
>>> L is M # Different objects
False
Now, watch what happens when we perform the same operations on small numbers:
>>> X = 42
>>> Y = 42 # Should be two different objects
>>> X == Y
True
>>> X is Y # Same object anyhow: caching at work!
True
In this interaction, X and Y should be == (same value), but not is (same object) because we ran two different literal expressions. Because small integers and strings are cached and reused, though, is tells us they reference the same single object.
In fact, if you really want to look under the hood, you can always ask Python how many references there are to an object: the getrefcount function in the standard sys module returns the object’s reference count. When I ask about the integer object 1 in the IDLE GUI, for instance, it reports 837 reuses of this same object (most of which are in IDLE’s system code, not mine):
>>> import sys
>>> sys.getrefcount(1) # 837 pointers to this shared piece of memory
837
This object caching and reuse is irrelevant to your code (unless you run the is check!). Because you cannot change numbers or strings in-place, it doesn’t matter how many references there are to the same object. Still, this behavior reflects one of the many ways Python optimizes its model for execution speed.
Dynamic Typing Is Everywhere
Of course, you don’t really need to draw name/object diagrams with circles and arrows to use Python. When you’re starting out, though, it sometimes helps you understand unusual cases if you can trace their reference structures. If a mutable object changes out from under you when passed around your program, for example, chances are you are witnessing some of this chapter’s subject matter firsthand.
Moreover, even if dynamic typing seems a little abstract at this point, you probably will care about it eventually. Because everything seems to work by assignment and references in Python, a basic understanding of this model is useful in many different contexts. As you’ll see, it works the same in assignment statements, function arguments, for loop variables, module imports, class attributes, and more. The good news is that there is just one assignment model in Python; once you get a handle on dynamic typing, you’ll find that it works the same everywhere in the language.
At the most practical level, dynamic typing means there is less code for you to write. Just as importantly, though, dynamic typing is also the root of Python’s polymorphism, a concept we introduced in Chapter 4 and will revisit again later in this book. Because we do not constrain types in Python code, it is highly flexible. As you’ll see, when used well, dynamic typing and the polymorphism it provides produce code that automatically adapts to new requirements as your systems evolve.
Chapter Summary
This chapter took a deeper look at Python’s dynamic typing model—that is, the way that Python keeps track of object types for us automatically, rather than requiring us to code declaration statements in our scripts. Along the way, we learned how variables and objects are associated by references in Python; we also explored the idea of garbage collection, learned how shared references to objects can affect multiple variables, and saw how references impact the notion of equality in Python.
Because there is just one assignment model in Python, and because assignment pops up everywhere in the language, it’s important that you have a handle on the model before moving on. The following quiz should help you review some of this chapter’s ideas. After that, we’ll resume our object tour in the next chapter, with strings.
Test Your Knowledge: Quiz
Consider the following three statements. Do they change the value printed