Use asyncio coroutine to run functions in parallel?

I have the following code which read data from database (read_db) and write the data to parquet file (data.to_parquet). Both I/O operations take a while to run.

def main():
    while id < 1000:
       logging.info(f'reading - id: {id}')
       data = read_db(id) # returns a dataframe

       logging.info(f'saving - id: {id}')
       data.to_parquet(f'{id}.parquet')
       logging.info(f'saved - id: {id}')

       id += 1
       

It's slow so I want read_db(n+1) and to_parquet(n) running concurrently. I need to keep each step of id finishing sequentially though. Here is the asynchronous version

def async_wrap(f):
    @wraps(f)
    async def run(*args, loop=None, executor=None, **kwargs):
        if loop is None:
            loop = asyncio.get_event_loop()
        p = partial(f, *args, **kwargs)
        return await loop.run_in_executor(executor, p)
    return run

async def main():
    read_db_async = async_wrap(read_db)
    while id < 1000:
       logging.info(f'reading - id: {id}')
       data = await read_db_async(id) # returns a dataframe

       logging.info(f'saving - id: {id}')
       to_parquet_async = async_wrap(data.to_parquet)
       await data.to_parquet(f'{id}.parquet')
       logging.info(f'saved - id: {id}')

       id += 1

asyncio.get_event_loop().run_until_complete(main())

I excepted to see the some out of order of logs:

reading - id: 1
saving - id: 1      (saving 1 and reading 2 run in parallel)
reading - id: 2
saved - id: 1
saving - id: 2
reading - id: 3
saved - id: 2
.....

But, the actually logs are the same of synchronous code?

reading - id: 1
saving - id: 1
saved - id: 1
reading - id: 2
saving - id: 2
saved - id: 2
reading - id: 3
.....

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