Update compute_dict_like to get all columns

[undermyumbrella1]

• closes BUG: groupby.agg fails when input has duplicate columns and dict input #55041 (Replace xxxx with the GitHub issue number)
• Tests added and passed if fixing a bug or adding a new feature
• All code checks passed.
• Added type annotations to new arguments/methods/functions.
• Added an entry in the latest doc/source/whatsnew/vX.X.X.rst file if fixing a bug or adding a new feature.

[rhshadrach]

Can you see if the following works instead:

subset = selected_obj[[key]]
subobj = obj._gotitem(key, ndim=2, subset=subset)
result = getattr(subobj, op_name)(how, **kwargs)

[undermyumbrella1]

Using getattr on DataFrame throws exception in this case

df = pd.DataFrame(
        [[1, 2, 3], [1, 3, 4], [2, 4, 5]],
        columns=["a", "b", "c"],
    )

    gb = df.groupby("a")
    result = gb.agg({"c": ["sum", "min", "max", "min"]})
    print(result)

This is due to reconstruct_func in DataFrameGroupby

if not relabeling:
        if isinstance(func, list) and len(func) > len(set(func)):
            # GH 28426 will raise error if duplicated function names are used and
            # there is no reassigned name
            raise SpecificationError(
                "Function names must be unique if there is no new column names "
                "assigned"
            )

[undermyumbrella1]

Thank you for the review, I have added comments as per the review.

[rhshadrach]

Thanks for the patience here @undermyumbrella1 - I should be able to get to this by Friday.

[rhshadrach]

The logic in the for loop that I was trying to change looks good. In general it'd be best if we didn't have to split up a DataFrame into Series for performance, but to flexibly handle all the different cases we need to it doesn't seem possible to me.

[rhshadrach]

Can you add one more like this but with a list instead, e.g. ["min", "max"]