Comprehending Set and Dictionary Comprehensions
In a sense, set and dictionary comprehensions are just syntactic sugar for passing generator expressions to the type names. Because both accept any iterable, a generator works well here:
>>> {x * x for x in range(10)} # Comprehension
{0, 1, 4, 81, 64, 9, 16, 49, 25, 36}
>>> set(x * x for x in range(10)) # Generator and type name
{0, 1, 4, 81, 64, 9, 16, 49, 25, 36}
>>> {x: x * x for x in range(10)}
{0: 0, 1: 1, 2: 4, 3: 9, 4: 16, 5: 25, 6: 36, 7: 49, 8: 64, 9: 81}
>>> dict((x, x * x) for x in range(10))
{0: 0, 1: 1, 2: 4, 3: 9, 4: 16, 5: 25, 6: 36, 7: 49, 8: 64, 9: 81}
As for list comprehensions, though, we can always build the result objects with manual code, too. Here are statement-based equivalents of the last two comprehensions:
>>> res = set()
>>> for x in range(10): # Set comprehension equivalent
... res.add(x * x)
...
>>> res
{0, 1, 4, 81, 64, 9, 16, 49, 25, 36}
>>> res = {}
>>> for x in range(10): # Dict comprehension equivalent
... res[x] = x * x
...
>>> res
{0: 0, 1: 1, 2: 4, 3: 9, 4: 16, 5: 25, 6: 36, 7: 49, 8: 64, 9: 81}
Notice that although both forms accept iterators, they have no notion of generating results on demand—both forms build objects all at once. If you mean to produce keys and values upon request, a generator expression is more appropriate:
>>> G = ((x, x * x) for x in range(10))
>>> next(G)
(0, 0)
>>> next(G)
(1, 1)
