Groupby pandas dataframe to produce a single list column of distinct groups and counts

still trying to think through how to describe this properly (will update question), but here’s my have/want minimal, reproducable, example of what I’m trying to do.

have = pd.DataFrame({'id': [1,1,1,2,2], 'grp': ['a', 'b', 'c', 'd', 'e'], 'val': [5,4,3,2,1]})
>>> have
   id grp  val
0   1   a    5
1   1   b    4
2   1   c    3
3   2   d    2
4   2   e    1

want = pd.DataFrame({'id': [1,2], 'results': [[('a', 5), ('b', '4'), ('c', 3)], [('d',2), ('e',1)]]})

>>> want
   id                   results
0   1  [(a, 5), (b, 4), (c, 3)]
1   2          [(d, 2), (e, 1)]

>Solution :

You can try groupby id column then zip the grp and val columns

out = (have.groupby('id')
       .apply(lambda g: list(zip(g['grp'], g['val'])))
       .rename('result')
       .reset_index())

print(out)

   id                    result
0   1  [(a, 5), (b, 4), (c, 3)]
1   2          [(d, 2), (e, 1)]