Show HN: Aiopandas – Async .apply() and .map() for Pandas, Faster API/LLMs Calls

It seems like this hack would be fine for notebooks, but not something I’d be interested in for production code.

Why not just something like this?

  def f(n):
      time.sleep(random.uniform(0.1, 0.3))  # Simulate network delay
      return pd.DataFrame({"A": [n, n+1], "B": [n*2, (n+1)*2]})

  with ThreadPoolExecutor() as ex:
    df = pd.concat(ex.map(f, range(3)), ignore_index=True)

indeed... the longer i write python, the more i just try to solve stuff with a simple ThreadPoolExecutor.

I think doing this is not the best choice for cpu-bound work, which is likely what you're running into with pandas, but nevertheless... I like how you can almost always slap a threadpool onto something and speed things up, with minimal cognitive overhead.