How to use `async for` in Python?

I mean what do I get from using async for. Here is the code I write with async for, AIter(10) could be replaced with get_range().

But the code runs like sync not async.

import asyncio

async def get_range():
    for i in range(10):
        print(f"start {i}")
        await asyncio.sleep(1)
        print(f"end {i}")
        yield i

class AIter:
    def __init__(self, N):
        self.i = 0
        self.N = N

    def __aiter__(self):
        return self

    async def __anext__(self):
        i = self.i
        print(f"start {i}")
        await asyncio.sleep(1)
        print(f"end {i}")
        if i >= self.N:
            raise StopAsyncIteration
        self.i += 1
        return i

async def main():
    async for p in AIter(10):
        print(f"finally {p}")

if __name__ == "__main__":
    asyncio.run(main())

The result I excepted should be :

start 1
start 2
start 3
...
end 1
end 2
...
finally 1
finally 2
...

However, the real result is:

start 0
end 0
finally 0
start 1
end 1
finally 1
start 2
end 2

I know I could get the excepted result by using asyncio.gather or asyncio.wait.

But it is hard for me to understand what I got by use async for here instead of simple for.

What is the right way to use async for if I want to loop over several Feature object and use them as soon as one is finished. For example:

async for f in feature_objects:
    data = await f
    with open("file", "w") as fi:
        fi.write()

But it is hard for me to understand what I got by use async for here instead of simple for.

The underlying misunderstanding is expecting async for to automatically parallelize the iteration. It doesn't do that, it simply allows sequential iteration over an async source. For example, you can use async for to iterate over lines coming from a TCP stream, messages from a websocket, or database records from an async DB driver. The iteration being async means that you can run it in parallel with other async tasks (including other such iterations) in the same event loop.

Ordinary for is incapable of async iteration, at least not without blocking the thread it's running in. This is because for calls __next__ as a blocking function and doesn't await its result. And you cannot manually await elements obtained by for because for expects __next__ to signal the end of iteration by raising StopIteration. If __next__ is a coroutine, the StopIteration exception won't be visible before awaiting it. This is why async for was introduced, not just in Python, but also in other languages with async/await and generalized for.

In other words, while ordinary for foo in bar(): ... desugars to something like:

__it = bar().__iter__()
while True:
    try:
        foo = __it.__next__()  # await missing
    except StopIteration:
        break
    ...

...async for foo in bar(): ... desugars to:

__ait = bar().__aiter__()
while True:
    try:
        foo = await __ait.__anext__()  # await present
    except StopAsyncIteration:
        break
    ...

If you want to run the loop iterations in parallel, you need to start them as parallel coroutines and use asyncio.as_completed or equivalent to retrieve their results as they come:

async def x(i):
    print(f"start {i}")
    await asyncio.sleep(1)
    print(f"end {i}")
    return i

# run x(0)..x(10) concurrently and process results as they arrive
for f in asyncio.as_completed([x(i) for i in range(10)]):
    result = await f
    # ... do something with the result ...

If you don't care about reacting to results immediately as they arrive, but you need them all, you can make it even simpler by using asyncio.gather:

# run x(0)..x(10) concurrently and process results when all are done
results = await asyncio.gather(*[x(i) for i in range(10)])