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BUG: freq information lost after stack() is called #49054

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@MMCMA

Description

@MMCMA

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  • I have confirmed this bug exists on the main branch of pandas.

Reproducible Example

import pandas as pd
idx = pd.date_range('20200101', periods=12, freq='BM')
df = pd.DataFrame(dict(a=range(12)), index=idx)
print(df.index.freq)
>> <BusinessMonthEnd>
df.stack()
print(df.index.freq()
>> None

Issue Description

Some of our CI pipeline unittests failed after upgrading to pandas 1.5.0 (linux). (I can't share this example.)

I tried to replicate the error above locally, and it seems to behave the same way. However, the same issue is only visible in ipython within my pycharm console. When I open a python instance via cmd, I cannot replicate the behaviour, which makes this bug a bit harder to trace I believe.

Expected Behavior

I would expect that the index.freq property would not be lost

Installed Versions

INSTALLED VERSIONS

commit : 87cfe4e
python : 3.9.7.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19043
machine : AMD64
processor : Intel64 Family 6 Model 142 Stepping 12, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : English_United States.1252
pandas : 1.5.0
numpy : 1.22.4
pytz : 2021.3
dateutil : 2.8.2
setuptools : 58.0.4
pip : 21.2.4
Cython : 0.29.25
pytest : 6.2.4
hypothesis : None
sphinx : 4.3.1
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.6.4
html5lib : None
pymysql : None
psycopg2 : 2.9.3
jinja2 : 3.0.3
IPython : 7.29.0
pandas_datareader: 0.10.0
bs4 : None
bottleneck : None
brotli : None
fastparquet : None
fsspec : None
gcsfs : None
matplotlib : 3.5.0
numba : 0.55.2
numexpr : None
odfpy : None
openpyxl : 3.0.9
pandas_gbq : None
pyarrow : 8.0.0
pyreadstat : None
pyxlsb : None
s3fs : None
scipy : 1.7.3
snappy : None
sqlalchemy : 1.4.31
tables : None
tabulate : 0.8.9
xarray : None
xlrd : None
xlwt : None
zstandard : None
tzdata : None

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    BugNeeds TriageIssue that has not been reviewed by a pandas team memberRegressionFunctionality that used to work in a prior pandas version

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