Does pandarallel not support parallel_apply with multiple columns groupby?

[akaymd]

In my environment, df.groupby(args).apply(func) with single column can be replaced by parallel_apply as follows

df.groupby("col1").apply(func)

However, groupby with multiple columns did not work in parallel.

df.groupby(["col1", "col2", ...]) .apply(func)

Does pandarallel not support parallel_apply with multiple columns groupby?

[perveen-shaheen]

I have the exact use-case, weirdly enough it was working till last week. Is this supported?

[AymanElsayeed]

@akaymd what are the versions of Python, Pandas, Numpy, and Pandarallel?

[nalepae]

Pandaral·lel is looking for a maintainer!
If you are interested, please open an GitHub issue.

[shermansiu]

Is there a minimally working code example to share? As well as the versions of Python and the relevant packages?

[shermansiu]

Using groupby on multiple columns works fine for me.

import pandas as pd
import pandarallel


pandarallel.pandarallel.initialize()


df = pd.DataFrame({"foo": range(20), "bar": range(20, 40)})
df["even"] = df["foo"] % 2 == 0
df["four"] = df["foo"] % 4 == 0
assert df.groupby(["even", "four"]).apply(lambda x: x+1).equals(df.groupby(["even", "four"]).parallel_apply(lambda x: x+1))

Python: 3.10.13
Pandarallel: 1.6.5
Pandas: 2.2.0