| Can this be merged into pandas? Pandas does not currently install tqdm by default. pandas-dev/pandas//pyproject.toml [project.optional-dependencies] https://github.com/pandas-dev/pandas/blob/8943c97c597677ae98... Dask solves for various adjacent problems; IDK if pandas, dask, or dask-cudf would be faster with async? Dask docs > Scheduling > Dask Distributed (local) https://docs.dask.org/en/stable/scheduling.html#dask-distrib... : > Asynchronous Futures API Dask docs > Deploy Dask Clusters; local multiprocessing poll, k8s (docker desktop, podman-desktop,), public and private clouds, dask-jobqueue (SLURM,), dask-mpi:
https://docs.dask.org/en/stable/deploying.html#deploy-dask-c... Dask docs > Dask DataFrame:
https://docs.dask.org/en/stable/dataframe.html : > Dask DataFrames are a collection of many pandas DataFrames. > The API is the same. The execution is the same. > [concurrent.futures and/or @dask.delayed] tqdm.dask: https://tqdm.github.io/docs/dask/#tqdmdask .. tests/tests_pandas.py: https://github.com/tqdm/tqdm/blob/master/tests/tests_pandas.... , tests/tests_dask.py: https://github.com/tqdm/tqdm/blob/master/tests/tests_dask.py tqdm with dask.distributed: https://github.com/tqdm/tqdm/issues/1230#issuecomment-222379... , not yet a PR: https://github.com/tqdm/tqdm/issues/278#issuecomment-5070062... dask.diagnostics.progress: https://docs.dask.org/en/stable/diagnostics-local.html#progr... dask.distributed.progress: https://docs.dask.org/en/stable/diagnostics-distributed.html... dask-labextension runs in JupyterLab and has a parallel plot visualization of the dask task graph and progress through it: https://github.com/dask/dask-labextension dask-jobqueue docs > Interactive Use > Viewing the Dask Dashboard:
https://jobqueue.dask.org/en/latest/clusters-interactive.htm... https://examples.dask.org/ > "Embarrassingly parallel Workloads" tutorial re: "three different ways of doing this with Dask: dask.delayed, concurrent.Futures, dask.bag":
https://examples.dask.org/applications/embarrassingly-parall... |
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.