df.groupby(…).agg(set) produces different result compared to df.groupby(…).agg(lambda x: set(x))

Question

Answering this question it turned out that df.groupby(...).agg(set) and df.groupby(...).agg(lambda x: set(x)) are producing different results.

Answer 1

Perhaps as @Edchum commented agg applies the python builtin functions considering the groupby object as a mini dataframe, whereas when a defined function is passed it applies it for every column. An example to illustrate this is via print.

df.groupby('user_id').agg(print,end='\n\n')

 class_type instructor  user_id
0  Krav Maga        Bob        1
4   Ju-jitsu      Alice        1

  class_type instructor  user_id
1       Yoga      Alice        2
5  Krav Maga      Alice        2

  class_type instructor  user_id
2   Ju-jitsu        Bob        3
6     Karate        Bob        3


df.groupby('user_id').agg(lambda x : print(x,end='\n\n'))

0    Krav Maga
4     Ju-jitsu
Name: class_type, dtype: object

1         Yoga
5    Krav Maga
Name: class_type, dtype: object

2    Ju-jitsu
6      Karate
Name: class_type, dtype: object

3    Krav Maga
Name: class_type, dtype: object

...

Hope this is the reason why applying set gave the result like the one mentioned above.