Weird behaviour with groupby on ordered categorical columns

Question

MCVE

df = pd.DataFrame({
    'Cat': ['SF', 'W', 'F', 'R64', 'SF', 'F'], 
    'ID': [1, 1, 1, 2, 2, 2]
})

df.Cat = pd.Categorical(
    df.Cat, categories=['R64', 'SF', 'F', 'W'], ordered=True)

As you can see, I've define an ordered categorical column on Cat. To verify, check;

0     SF
1      W
2      F
3    R64
4     SF
5      F
Name: Cat, dtype: category
Categories (4, object): [R64 < SF < F < W]

I want to find the largest category PER ID. Doing groupby + max works.

df.groupby('ID').Cat.max()

ID
1    W
2    F
Name: Cat, dtype: object

But I don't want ID to be the index, so I specify as_index=False.

df.groupby('ID', as_index=False).Cat.max()

   ID Cat
0   1   W
1   2  SF

Oops! Now, the max is taken lexicographically. Can anyone explain whether this is intended behaviour? Or is this a bug?

Note, for this problem, the workaround is df.groupby('ID').Cat.max().reset_index().

Note,

>>> pd.__version__
'0.22.0'

Answer 1

This is not intended behavior, it's a bug.

Source diving shows the flag does two completely different things. The one simply ignores grouper levels and names, it just takes the values with a new range index. The other one clearly keeps them.