If a Python generator produces values once and then appears empty, it has probably been exhausted. A generator object is a one-pass iterator, not a reusable collection: to iterate again, create a new generator and, when necessary, a new underlying data source—or save finite results in a list.
Why a generator is empty on the second pass
A generator function and a generator object are different things. A function containing yield creates a generator object when called; it does not immediately run through the function and return all its values. Each call to next(), or each step of a loop, resumes that object until it yields a value. When the function returns or reaches its end, the iterator signals that it is finished with StopIteration. That is the normal end-of-iteration signal, not an error by itself. See the Python language reference on yield expressions and the built-in exception documentation.
def numbers():
yield 1
yield 2
g = numbers()
print(list(g)) # [1, 2]
print(list(g)) # [] — g has already been exhaustedlist(g) consumes the values from g. So does a for loop, sum(), or another operation that reads the iterator. After the first consumer reaches the end, the same generator object has no built-in rewind operation. Calling iter(g) does not recreate its execution state; it returns the iterator itself. The iterator behavior is described in the Python built-in functions documentation.
How to iterate again
Call the generator function again
If the generator’s inputs can be recreated, call its function again for a fresh generator object:
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def numbers():
yield 1
yield 2
first_pass = list(numbers())
second_pass = list(numbers())Each call to numbers() creates a separate iterator and runs the generator body as that iterator is consumed.
Store finite results when reuse is needed
If you need to revisit the same finite results and they fit comfortably in memory, materialize them once:
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items = list(make_items())
for item in items:
process(item)
for item in items:
compare(item)The list is reusable; the generator used to create it is still consumed. Avoid this approach when the results are too large to hold comfortably in memory or are unbounded.
Recreate the underlying source too
A new outer generator is not enough if it wraps an already-consumed iterator. For example, if a generator reads from a file, cursor, or other one-shot source, a second pass needs a new source as well as a new wrapper. Reopen the file or rerun the source-producing operation when that is appropriate. If the source cannot be reproduced cheaply or has side effects, consider changing the algorithm to perform both operations in one pass or using a source-specific way to query it again.
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Choose a reuse pattern that fits the source
| Pattern | Use it when | Trade-off |
|---|---|---|
| Create a new generator | The inputs and source can be recreated cheaply and repeatably. | Recomputes the results on each pass. |
Materialize with list() |
The results are finite, needed more than once, and fit comfortably in memory. | Stores the full result in memory. |
| Reopen or recreate the source | The generator reads from a one-shot source such as a file or cursor. | Requires a fresh source; wrapping the consumed iterator again does not restore its data. |
| Change the algorithm | The source is large, unbounded, costly to reproduce, or has side effects. | May require combining operations into one pass or using a source-specific repeat query. |
The choice depends on whether the source can be reproduced, whether all results fit in memory, the cost of recomputing them, and any side effects or external state involved.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What StopIteration and RuntimeError mean
StopIteration tells Python’s iterator protocol that there is no next value. A for loop handles it internally and ends the loop. If you call next(g) directly after the iterator is exhausted, the exception reaches your code. Use next(g, default) when you want a default instead:
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value = next(g, None)Choose a unique sentinel instead of None if None could itself be a valid value.
Use return to finish a generator normally
Do not use raise StopIteration as a generator’s normal way to finish. Let the function reach its end or use return. Under PEP 479, an unhandled StopIteration escaping from a generator body is converted to RuntimeError; Python enabled this behavior for all code in version 3.7. If a direct next() inside the generator is expected to run out, catch the exception at that call site:
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def take_two(iterator):
for _ in range(2):
try:
value = next(iterator)
except StopIteration:
return
yield valueThis example ends the generator normally if its input has fewer than two remaining values.
Quick Recap
Debug a generator that unexpectedly has no values
- Check whether the variable has already been passed to
list(),sum(), a loop, or another consumer. - Find the first place it was advanced. A diagnostic
next(g)consumes a value; it is not a peek. - Check whether the generator wraps an iterator that was itself already consumed.
- For another pass, recreate the original source and generator, or deliberately store finite results if they fit in memory.
- If the traceback says
RuntimeError: generator raised StopIteration, inspect the generator for an uncaughtnext()or explicitraise StopIteration. Catch expected exhaustion or usereturn.
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