Generators Are Single-Iterator Objects

Both generator functions and generator expressions are their own iterators and thus support just one active iteration—unlike some built-in types, you can’t have multiple iterators of either positioned at different locations in the set of results. For example, using the prior section’s generator expression, a generator’s iterator is the generator itself (in fact, calling iter on a generator is a no-op):

>>> G = (c * 4 for c in 'SPAM')
>>> iter(G) is G                          # My iterator is myself: G has __next__
True

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If you iterate over the results stream manually with multiple iterators, they will all point to the same position:

>>> G = (c * 4 for c in 'SPAM')           # Make a new generator
>>> I1 = iter(G)                          # Iterate manually
>>> next(I1)
'SSSS'
>>> next(I1)
'PPPP'
>>> I2 = iter(G)                          # Second iterator at same position!
>>> next(I2)
'AAAA'

Moreover, once any iteration runs to completion, all are exhausted—we have to make a new generator to start again:

>>> list(I1)                              # Collect the rest of I1's items
['MMMM']
>>> next(I2)                              # Other iterators exhausted too
StopIteration

>>> I3 = iter(G)                          # Ditto for new iterators
>>> next(I3)
StopIteration

>>> I3 = iter(c * 4 for c in 'SPAM')      # New generator to start over
>>> next(I3)
'SSSS'

The same holds true for generator functions—the following def statement-based equivalent supports just one active iterator and is exhausted after one pass:

>>> def timesfour(S):
...     for c in S:
...         yield c * 4
...
>>> G = timesfour('spam')                 # Generator functions work the same way
>>> iter(G) is G
True
>>> I1, I2 = iter(G), iter(G)
>>> next(I1)
'ssss'
>>> next(I1)
'pppp'
>>> next(I2)                              # I2 at same position as I1
'aaaa'

This is different from the behavior of some built-in types, which support multiple iterators and passes and reflect their in-place changes in active iterators:

>>> L = [1, 2, 3, 4]
>>> I1, I2 = iter(L), iter(L)
>>> next(I1)
1
>>> next(I1)
2
>>> next(I2)                              # Lists support multiple iterators
1
>>> del L[2:]                             # Changes reflected in iterators
>>> next(I1)
StopIteration

When we begin coding class-based iterators in Part VI, we’ll see that it’s up to us to decide how any iterations we wish to support for our objects, if any.