BUG: Partial multiindex columns selection breaks categorical dtypes

[epizut]

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Reproducible Example

import pandas as pd
df = pd.DataFrame([('value')], columns=pd.Index([('A', 'B')], names=['lvl1', 'lvl2'])).astype('category')

# Broken object dtypes: 
df['A'].dtypes
# lvl2
# B    object

# Please note that an explicit full level selection works as expected:
df[[('A', 'B')]].dtypes
# lvl1  lvl2
# A     B       category

Issue Description

When partially selecting the first level from a multiindex columns catagorical dataframe, dtpyes are upcasted to object.

Expected Behavior

# Expected categorical dtypes:
df['A'].dtypes
# lvl2
# B    category

Installed Versions

INSTALLED VERSIONS ------------------ commit : 2e218d1 python : 3.9.12.final.0 python-bits : 64 OS : Windows OS-release : 10 Version : 10.0.19042 machine : AMD64 processor : Intel64 Family 6 Model 85 Stepping 4, GenuineIntel byteorder : little LC_ALL : None LANG : None LOCALE : English_United States.1252

pandas : 1.5.3
numpy : 1.23.5
pytz : 2022.7.1
dateutil : 2.8.2
setuptools : 65.3.0
pip : 22.2.2
Cython : 0.29.30
pytest : 7.1.1
hypothesis : None
sphinx : 4.5.0
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.9.0
html5lib : None
pymysql : None
psycopg2 : 2.9.3
jinja2 : 3.1.1
IPython : 8.2.0
pandas_datareader: None
bs4 : 4.11.1
bottleneck : None
brotli : None
fastparquet : None
fsspec : 2022.3.0
gcsfs : None
matplotlib : 3.5.1
numba : 0.56.4
numexpr : None
odfpy : None
openpyxl : 3.0.9
pandas_gbq : None
pyarrow : 7.0.0
pyreadstat : None
pyxlsb : None
s3fs : None
scipy : 1.8.1
snappy : None
sqlalchemy : 1.4.41
tables : None
tabulate : 0.8.9
xarray : 2022.3.0
xlrd : 2.0.1
xlwt : None
zstandard : None
tzdata : None

[m-richards]

Over in geopandas (geopandas/geopandas#2088, geopandas/geopandas#1763) we're seeing what I think is essentially the same issue, partial column selections do not preserve dtypes. Here's an analogue from that context where I've replace the geopandas dependency with a nullable float dtype:

In [5]: df = pd.DataFrame(
   ...:             [[1, 0], [0, 1]],
   ...:             columns=[
   ...:                 ["foo", "foo"],
   ...:                 ["location", "location"],
   ...:                 ["x", "y"],
   ...:             ],
   ...:         )

In [6]: df['floats'] = pd.Series([1.1, 1.2], dtype='Float64')

In [7]: df.dtypes
Out[7]:
foo     location  x      int64
                  y      int64
floats                 Float64
dtype: object

In [8]: df['floats']
Out[8]:
0    1.1
1    1.2
Name: floats, dtype: object
# would expect this to be Float64 dtype

[MarcoGorelli]

moving off the 2.0 milestone