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Multiple Versus Single Iterators
It’s interesting to see how the range object differs from the built-ins described in this section—it supports len and indexing, it is not its own iterator (you make one with iter when iterating manually), and it supports multiple iterators over its result that remember their positions independently:
>>> R = range(3) # range allows multiple iterators
>>> next(R)
TypeError: range object is not an iterator
>>> I1 = iter(R)
>>> next(I1)
0
>>> next(I1)
1
>>> I2 = iter(R) # Two iterators on one range
>>> next(I2)
0
>>> next(I1) # I1 is at a different spot than I2
2
By contrast, zip, map, and filter do not support multiple active iterators on the same result:
>>> Z = zip((1, 2, 3), (10, 11, 12))
>>> I1 = iter(Z)
>>> I2 = iter(Z) # Two iterators on one zip
>>> next(I1)
(1, 10)
>>> next(I1)
(2, 11)
>>> next(I2) # I2 is at same spot as I1!
(3, 12)
>>> M = map(abs, (-1, 0, 1)) # Ditto for map (and filter)
>>> I1 = iter(M); I2 = iter(M)
>>> print(next(I1), next(I1), next(I1))
1 0 1
>>> next(I2)
StopIteration
>>> R = range(3) # But range allows many iterators
>>> I1, I2 = iter(R), iter(R)
>>> [next(I1), next(I1), next(I1)]
[0 1 2]
>>> next(I2)
0
When we code our own iterable objects with classes later in the book (Chapter 29), we’ll see that multiple iterators are usually supported by returning new objects for the iter call; a single iterator generally means an object returns itself. In Chapter 20, we’ll also find that generator functions and expressions behave like map and zip instead of range in this regard, supporting a single active iteration. In that chapter, we’ll see some subtle implications of one-shot iterators in loops that attempt to scan multiple times.
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