Datasets map is slower than pandas apply

The map method is slower than pandas’ apply method in some of my tests. I understand that map function can be expedited using multi-processing, but is there anything else that can be done to improve performance?

E.g. using pandas:

import pandas as pd

fields = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h']

dummy_df.col_c.apply(lambda e: [e.get(c) or 0 for c in fields])

This takes 1.5 seconds to run on my machine.

E.g. using map:

dummy_ds = Dataset.from_pandas(dummy_df) r: {'col_d': [r['col_c'].get(c) or 0 for c in fields]})

This takes 48.7 seconds to run.