GroupBy(..., as_index=True).agg() drops index when

[adamhooper]

Code Sample, a copy-pastable example if possible

In [1]: import pandas as pd

In [2]: pd.DataFrame({"A": [1997], "B": pd.Series(["b"], dtype="category").cat.as_ordered()}).groupby("A", as_index=True).agg({"B": "size"})
Out[2]:
      B
A
1997  1

In [3]: pd.DataFrame({"A": [1997], "B": pd.Series(["b"], dtype="category").cat.as_ordered()}).groupby("A", as_index=True).agg({"B": "min"})
Out[3]:
   B
0  b

Problem description

When aggregating min, max or first of a categorical column, .agg() returns a dataframe with a default index instead of the index returned by groupby().

(This may be related to #13416 ... but I think it's a clear, well-defined bug so maybe easier to resolve?)

In my case, I think I can work around this problem with a hack: if I .agg({"B": ["min", "size"]}) and then ignore the ("B", "size") output column, Pandas will output a dataframe with the correct index.

Expected Output

Out[3]:
      B
A
1997  b

Output of pd.show_versions()

INSTALLED VERSIONS ------------------ commit : None python : 3.7.4.final.0 python-bits : 64 OS : Linux OS-release : 5.2.11-200.fc30.x86_64 machine : x86_64 processor : x86_64 byteorder : little LC_ALL : None LANG : en_US.UTF-8 LOCALE : en_US.UTF-8

pandas : 0.25.0
numpy : 1.16.1
pytz : 2018.9
dateutil : 2.8.0
pip : 19.0.3
setuptools : 40.8.0
Cython : 0.29.5
pytest : 4.5.0
hypothesis : None
sphinx : 2.1.1
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.2.5
html5lib : 1.0.1
pymysql : None
psycopg2 : 2.8.3 (dt dec pq3 ext lo64)
jinja2 : 2.10.1
IPython : 7.2.0
pandas_datareader: None
bs4 : 4.6.3
bottleneck : None
fastparquet : 0.2.1
gcsfs : None
lxml.etree : 4.2.5
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.14.1
pytables : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None

[ZOUG]

I've encountered a related issue, in which the aggregation function is called directly and a Categorical object is returned rather than a dataframe.

df = pd.DataFrame({"A": [1997], "B": pd.Series(["b"], dtype="category").cat.as_ordered()})
df.groupby("A")["B"].first()

Output:

[b]
Categories (1, object): [b]

[simonjayhawkins]

xref #33098

While searching for related issue for my other pull request, I found that this issue must have been fixed with another commit in the past (don't know when this issue was fixed).