Description
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I have checked that this issue has not already been reported.
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I have confirmed this bug exists on the latest version of pandas.
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Note: Please read this guide detailing how to provide the necessary information for us to reproduce your bug.
Code Sample, a copy-pastable example
# prepare a dataframe where problem becomes obvious (details not important)
df = pd.DataFrame({'A': [1] * 20 + [2] * 12 + [3] * 8,
'B': np.arange(40)})
df = df.reset_index().set_index(['index','A']).reorder_levels([1,0])
index_mapping = {a:(a-20)/10 for a in range(20,32)}
df = df.rename(index=index_mapping)
index_mapping = {a:(a-32)/10 for a in range(32,40)}
df = df.rename(index=index_mapping)
df = df.rename_axis(index={'index':'time'})
# groupby - rolling operation (here is the bug)
df.groupby('A').rolling(4).mean()Problem description
Applying groupby.rolling on a multiindex dataframe should preserve the level that the rolling operation is applied to. Instead that index level is removed. This loses information and causes non-unique index values.
df
df after applying groupby('A').rolling misses index level 'time'
Expected Output
All index levels should be preserved. In older versions of pandas (1.1.2) the index levels were preserved, but there used to be a different bug: The whole multilevel index structure was duplicated, leading to twice the number of original levels. I guess that in an attempt to fix this, this new bug got introduced.
Output of pd.show_versions()
INSTALLED VERSIONS
commit : 7d32926
python : 3.8.1.final.0
python-bits : 64
OS : Darwin
OS-release : 19.6.0
Version : Darwin Kernel Version 19.6.0: Mon Aug 31 22:12:52 PDT 2020; root:xnu-6153.141.2~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : None.UTF-8
pandas : 1.2.2
numpy : 1.20.1
pytz : 2020.1
dateutil : 2.8.1
pip : 21.0.1
setuptools : 41.2.0
Cython : 0.29.20
pytest : 5.4.2
hypothesis : None
sphinx : 3.0.4
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.5.2
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.14.0
pandas_datareader: None
bs4 : 4.9.1
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.1
numexpr : 2.7.1
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.16.0
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : None
tables : 3.6.1
tabulate : 0.8.7
xarray : 0.16.0
xlrd : 1.2.0
xlwt : None
numba : 0.48.0