Comparisons, Equality, and Truth

All Python objects also respond to comparisons: tests for equality, relative magnitude, and so on. Python comparisons always inspect all parts of compound objects until a result can be determined. In fact, when nested objects are present, Python automatically traverses data structures to apply comparisons recursively from left to right, and as deeply as needed. The first difference found along the way determines the comparison result.

For instance, a comparison of list objects compares all their components automatically:

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>>> L1 = [1, ('a', 3)]           # Same value, unique objects
>>> L2 = [1, ('a', 3)]
>>> L1 == L2, L1 is L2           # Equivalent? Same object?
(True, False)

Here, L1 and L2 are assigned lists that are equivalent but distinct objects. Because of the nature of Python references (studied in Chapter 6), there are two ways to test for equality:

 

 
  • The == operator tests value equivalence. Python performs an equivalence test, comparing all nested objects recursively.
  • The is operator tests object identity. Python tests whether the two are really the same object (i.e., live at the same address in memory).

In the preceding example, L1 and L2 pass the == test (they have equivalent values because all their components are equivalent) but fail the is check (they reference two different objects, and hence two different pieces of memory). Notice what happens for short strings, though:

>>> S1 = 'spam'
>>> S2 = 'spam'
>>> S1 == S2, S1 is S2
(True, True)

Here, we should again have two distinct objects that happen to have the same value: == should be true, and is should be false. But because Python internally caches and reuses some strings as an optimization, there really is just a single string 'spam' in memory, shared by S1 and S2; hence, the is identity test reports a true result. To trigger the normal behavior, we need to use longer strings:

>>> S1 = 'a longer string'
>>> S2 = 'a longer string'
>>> S1 == S2, S1 is S2
(True, False)

Of course, because strings are immutable, the object caching mechanism is irrelevant to your code—strings can’t be changed in-place, regardless of how many variables refer to them. If identity tests seem confusing, see Chapter 6 for a refresher on object reference concepts.

As a rule of thumb, the == operator is what you will want to use for almost all equality checks; is is reserved for highly specialized roles. We’ll see cases where these operators are put to use later in the book.

Relative magnitude comparisons are also applied recursively to nested data structures:

>>> L1 = [1, ('a', 3)]
>>> L2 = [1, ('a', 2)]
>>> L1 < L2, L1 == L2, L1 > L2        # Less, equal, greater: tuple of results
(False, False, True)

Here, L1 is greater than L2 because the nested 3 is greater than 2. The result of the last line is really a tuple of three objects—the results of the three expressions typed (an example of a tuple without its enclosing parentheses).

In general, Python compares types as follows:

 

 
  • Numbers are compared by relative magnitude.
  • Strings are compared lexicographically, character by character ("abc" < "ac").
  • Lists and tuples are compared by comparing each component from left to right.
  • Dictionaries compare as equal if their sorted (key, value) lists are equal. Relative magnitude comparisons are not supported for dictionaries in Python 3.0, but they work in 2.6 and earlier as though comparing sorted (key, value) lists.
  • Nonnumeric mixed-type comparisons (e.g., 1 < 'spam') are errors in Python 3.0. They are allowed in Python 2.6, but use a fixed but arbitrary ordering rule. By proxy, this also applies to sorts, which use comparisons internally: nonnumeric mixed-type collections cannot be sorted in 3.0.

In general, comparisons of structured objects proceed as though you had written the objects as literals and compared all their parts one at a time from left to right. In later chapters, we’ll see other object types that can change the way they get compared.