append ids when specific column is the same

For example I have this table

| ID       | VALUE        |

| -------- | -------------- |
| 1    | row24           |
| 2   | row24            |
| 3    | row1            |
| 4   | row15            |
| 5    | row16           |
| 6   | row17            |
| 8   | row24            |
| 7   | row17            |
| 9   | row19            |

Output should be:

| ID       | VALUE        |

| -------- | -------------- |
| [1,2,8]    | row24           |

| 3    | row1            |
| 4   | row15            |
| 5    | row16           |
| [6,7]   | row17         |
| 9   | row19            |

I think maybe groupby in pandas is a solution, i tried some but it didnt work…

>Solution :

If need combination lists and scalars use GroupBy.agg with lambda function:

df =  (df.groupby('VALUE', sort=False)['ID']
        .agg(lambda x: list(x) if len(x) > 1 else x)
        .reset_index(name='IDS'))
print (df)
   VALUE        IDS
0  row24  [1, 2, 8]
1   row1          3
2  row15          4
3  row16          5
4  row17     [6, 7]
5  row19          9

Because ig aggregate only list get oalso one element lists:

df =  (df.groupby('VALUE', sort=False)['ID']
        .agg(list)
        .reset_index(name='IDS'))
print (df)
   VALUE        IDS
0  row24  [1, 2, 8]
1   row1        [3]
2  row15        [4]
3  row16        [5]
4  row17     [6, 7]
5  row19        [9]