Thank you for the input! To be honest, I don’t use Dask often, and as a regular Pandas user, I don’t feel the most qualified to comment—but here we go.

Can this be merged into Pandas?

I’d be honored if something I built got incorporated into Pandas! That said, keeping aiopandas as a standalone package has the advantage of working with older Pandas versions, which is useful for workflows where upgrading isn’t feasible. I also can’t speak to the downstream implications of adding this directly into Pandas.

Pandas does not install tqdm by default.

That makes sense, and aiopandas doesn’t require tqdm either. You can pass any class with __init__, update, and close methods as the tqdm argument, and it will work the same. Keeping dependencies minimal helps avoid unnecessary breakage.

What about Dask?

I’m not a regular Dask user, so I can’t comment much on its internals. Dask already supports async coroutines (Dask Async API), but for simple async API calls or LLM requests, aiopandas is meant to be a lightweight extension of Pandas rather than a full-scale parallelization framework. If you’re already using Dask, it probably covers most of what you need, but if you’re just looking to add async support to Pandas without additional complexity, aiopandas might be a more lightweight option.