Pandas version checks
-
[X] I have checked that this issue has not already been reported.
-
[X] I have confirmed this bug exists on the latest version of pandas.
-
[ ] I have confirmed this bug exists on the main branch of pandas.
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 : 2e218d10984e9919f0296931d92ea851c6a6faf5
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
Comment From: m-richards
Over in geopandas (https://github.com/geopandas/geopandas/pull/2088, https://github.com/geopandas/geopandas/issues/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
Comment From: MarcoGorelli
moving off the 2.0 milestone