BUG: period[h] + column splicing is not work

[oxygenbilly]

Pandas version checks

• I have checked that this issue has not already been reported.

• 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

pr = pd.period_range('2024-01-01 00:00:00', '2024-01-01 02:00:00', freq='h')
df = pd.DataFrame(index=pr)
df['date'] = df.index.to_timestamp().floor('D')
df['hour'] = df.index.hour
df.index.name = 'value'
df = df.reset_index()
df = df.pivot(index='date', columns='hour', values='value')

print(df)
# hour                       0                 1                 2
# date                                                            
# 2024-01-01  2024-01-01 00:00  2024-01-01 01:00  2024-01-01 02:00

print(df[[0,1,2]])
# hour                       0                 1                 2
# date                                                            
# 2024-01-01  2024-01-01 00:00  2024-01-01 00:00  2024-01-01 00:00

Issue Description

when the datatype is period[h], the slicing will not produce the correct results.
if the datatype is changed to object. result is correct

Expected Behavior

The expected behavior is to slice the column properly based on hour [0, 1, 2].
However the above results give all [0] column for all of [0, 1, 2]

Installed Versions

INSTALLED VERSIONS

commit : d9cdd2e
python : 3.10.12.final.0
python-bits : 64
OS : Linux
OS-release : 6.1.85+
Version : #1 SMP PREEMPT_DYNAMIC Thu Jun 27 21:05:47 UTC 2024
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : en_US.UTF-8
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8

pandas : 2.2.2
numpy : 1.26.4
pytz : 2024.2
dateutil : 2.8.2
setuptools : 75.1.0
pip : 24.1.2
Cython : 3.0.11
pytest : 7.4.4
hypothesis : None
sphinx : 5.0.2
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 5.3.0
html5lib : 1.1
pymysql : None
psycopg2 : 2.9.10
jinja2 : 3.1.4
IPython : 7.34.0
pandas_datareader : 0.10.0
adbc-driver-postgresql: None
adbc-driver-sqlite : None
bs4 : 4.12.3
bottleneck : 1.4.2
dataframe-api-compat : None
fastparquet : None
fsspec : 2024.10.0
gcsfs : 2024.10.0
matplotlib : 3.8.0
numba : 0.60.0
numexpr : 2.10.1
odfpy : None
openpyxl : 3.1.5
pandas_gbq : 0.24.0
pyarrow : 17.0.0
pyreadstat : None
python-calamine : None
pyxlsb : None
s3fs : None
scipy : 1.13.1
sqlalchemy : 2.0.36
tables : 3.8.0
tabulate : 0.9.0
xarray : 2024.10.0
xlrd : 2.0.1
zstandard : None
tzdata : 2024.2
qtpy : None
pyqt5 : None

[rhshadrach]

Thanks for the report! Confirmed on main. Further investigations and PRs to fix are welcome!

[DhruvBShetty]

Hello, @oxygenbilly, I would like to investigate this issue, if you aren't working on it.

[veronicabenedict]

@DhruvBShetty Hi! if you are no longer working on this, I would like to take this issue

[veronicabenedict]

take

[DhruvBShetty]

@veronicabenedict Hi, sure, go ahead.

[yuanx749]

Also not working for other period datetypes with column indexing.
A simpler reproducible example:

df = pd.DataFrame(
    data=[
        pd.Period("2024-01-01", freq="D"),
        pd.Period("2024-01-02", freq="D"),
        pd.Period("2024-01-03", freq="D"),
    ]
).T

print(df)
#             0           1           2
# 0  2024-01-01  2024-01-02  2024-01-03

print(df[[0, 1, 2]])
#             0           1           2
# 0  2024-01-01  2024-01-01  2024-01-01

[snitish]

@veronicabenedict any luck investigating this?