Link: Pandarallel

result = df.groupby('group').parallel_apply(group_operation)

: While pandarallel can offer significant speedups, there's always some overhead involved in creating and managing parallel processes. For smaller datasets, this might negate the benefits of parallelization. pandarallel

pandarallel.initialize(progress_bar=True) result = df

: Certain steps in machine learning pipelines, like data preparation and feature engineering, can benefit from parallelization. like data preparation and feature engineering