map_sync with pandas operation function does not finish.

Map_sync with pandas operation function does not finish.

I have very long dataframe. So I split the dataframe into 40 sub-dataframes, and apply pandas operation to 40 sub-dataframes parallelly by using map_sync. The pandas operation is just about groupby and apply.

My code is like this:
PEN = 40
dfs = np.array_split(target_df, PEN)
c = ipp.Cluster(n=PEN)
with c as rc:
e_all = rc[:]
results = e_all.map_sync(FUCTION, dfs)
results

I have 30 target_dfs. For the first 10 target dfs map_sync worked fine. But after that map_sync didn't complete.
I have found that without parallelism, the pandas job applied to target_df completes in under 2 hours.
I use window os and Ipyparallel version is the lastest.

Start with the provided Windows reproduction using ipyparallel's map_sync, 40 sub-dataframes, and the pandas groupby/apply operation; inspect client and worker output when processing stops after roughly 10 target dataframes. The issue is done when the cause of the incomplete map_sync is identified and a reproducible fix or confirmed limitation is documented.

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