The current title of this post is probably incorrect and may even be misleading. I had a hard time coming up with a suitable name for it. But the idea goes like this: sometimes you might find yourself in a situation where you need to iterate through a generator more than once. Sure, you can use an iterable like a tuple or list to allow multiple iterations, but if the number of elements is large, that’ll cause an OOM error. On the other hand, once you’ve already consumed a generator, you’ll need to restart it if you want to go through it again. This behavior is common in pretty much every programming language that supports the generator construct.

So, in the case where a function returns a generator and you’ve already consumed its values, you’ll need to call the function again to generate a new instance of the generator that you can use. Observe:

from __future__ import annotations
from collections.abc import Generator


def get_numbers(start: int, end: int, step: int) -> Generator[int, None, None]:
    yield from range(start, end, step)

This can be used like this:

numbers = get_numbers(1, 10, 2)

for number in numbers:
    print(number)

It’ll return:

1
3
5
7
9

Now, if you try to consume the iterable again, you’ll get empty value. Run this again:

for number in numbers:
    print(number)

It won’t print anything since the previous loop has exhausted the generator. This is expected and if you want to loop through the same elements again, you’ll have to call the function again to produce another generator that you can consume. So, the following will always work:

for number in get_numbers():
    print(number)

If you run this snippet multiple times, on each pass, the get_numbers() function will be called again and that’ll return a new generator for you to iterate through. Calling the generator function like this works but here’s another thing that I learned today while reading Effective Python1 by Brett Slatkin. You can create a class with the __iter__ method and yield numbers from it just like the function. Then when you initiate the class, the instance of the class will allow you to loop through it multiple times; each time creating a new generator.

I knew that you could create an iterable class by adding __iter__ to a class and yielding values from it. But I wasn’t aware that the you could also iterate through the instance of the class multiple times and the class will run __iter__ on each pass and produce a new generator for you to consume.

For example:

from __future__ import annotations
from collections.abc import Generator


class NumberGen:
    def __init__(self, start: int, end: int, step: int) -> None:
        self.start = start
        self.end = end
        self.step = step

    def __iter__(self) -> Generator[int, None, None]:
        yield from range(self.start, self.end, self.step)

Now use the class as such:

numbers = NumberGen()
for number in numbers:
    print(number)

This prints:

1
3
5
7
9

If you run the for-loop again on the number instance, you’ll see that the snippet will print the same numbers again. Here, instantiating the NumberGen class creates a NumberGen instance that is not a generator per se, but can return a generator if you call the iter() function on the instance. When you run the for loop on the instance, it runs the underlying __iter__ method to produce a new generator that the loop can iterate through. This allows you to run the for-loop multiple times on the instance, since each run creates a new generator that the loop can consume.

A generator can still only be consumed once but each time you’re running a new for-loop on the above instance, the __iter__ method on it gets called and the method returns a new generator for you to iterate through.

This is more convenient than having to repeatedly call a generator function if your API needs to consume a generator multiple times.

  1. Effective Python - Brett Slatkin ↩︎

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