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parallel_apply is not working when using pandas >= 2.1. In my case, I am using it after a groupby.
Progress bar doesn't show up, the processing seems to be run sequentially (according to Activty Monitor).
The text was updated successfully, but these errors were encountered:
I am experiencing the exact issue:
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It works just fine for me on Pandas 2.1. Do you have a minimal code example to reproduce your bug?
Python: 3.10.13 Pandarallel: 1.6.5 Pandas: 2.1.0 Ubuntu 22.04
import pandas as pd import pandarallel pandarallel.pandarallel.initialize(nb_workers=2, progress_bar=True) df = pd.DataFrame({"foo": range(200), "bar": range(200, 400)}) df["even"] = df["foo"] % 2 == 0 assert df.groupby("even").apply(lambda x: x+1).equals(df.groupby("even").parallel_apply(lambda x: x+1))
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General
Bug description
parallel_apply is not working when using pandas >= 2.1. In my case, I am using it after a groupby.
Observed behavior
Progress bar doesn't show up, the processing seems to be run sequentially (according to Activty Monitor).
The text was updated successfully, but these errors were encountered: