BUG: groupby rolling and ewm methods return pandas objects instead of modin.pandas objects

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Assessment

Difficulty
3/5
Estimated time
1-2 days
Newbie friendliness
45/100
Issue type
Bug
Clarity
Mostly clear
Activity status
Stale
Tech stack
pandas, python
Domain
data

Research direction

Start by running the reproducible example for groupby().rolling() and groupby().ewm() and inspect the corresponding Modin entry points. Compare their return types with pandas, then add or update focused tests showing that both methods return Modin pandas objects; done means the example reports Modin types.

Written by the indexing model from the issue text.

Description

bug 🦗 P2 pandas.groupby
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Reproducible Example
import modin.pandas as pd

df = pd.DataFrame({'A': [1, 1, 2, 1, 2], 'B': [5, 6, 7, 8, 9], 'C': [10, 11, 12, 13, 14]})
print(type(df.groupby('A').rolling(1).count()))
print(type(df.groupby('A').ewm(1).sum()))
Issue Description

returning pandas dataframe/series instead of the respective modin types

Expected Behavior

should return modin.pandas objects

Error Logs

Replace this line with the error backtrace (if applicable).
Installed Versions

INSTALLED VERSIONS

commit : be98fe6cca629471392bf7de11f258a15fc47283
python : 3.8.16.final.0
python-bits : 64
OS : Darwin
OS-release : 22.5.0
Version : Darwin Kernel Version 22.5.0: Mon Apr 24 20:51:50 PDT 2023; root:xnu-8796.121.2~5/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8

Modin dependencies

modin : 0.23.0rc0+4.gbe98fe6cc
ray : 2.4.0
dask : 2023.4.1
distributed : 2023.4.1
hdk : None

pandas dependencies

pandas : 2.0.2
numpy : 1.24.3
pytz : 2023.3
dateutil : 2.8.2
setuptools : 66.0.0
pip : 23.0.1
Cython : 0.29.34
pytest : 7.3.1
hypothesis : None
sphinx : 7.0.0
blosc : None
feather : 0.4.1
xlsxwriter : None
lxml.etree : 4.9.2
html5lib : None
pymysql : None
psycopg2 : 2.9.6
jinja2 : 3.1.2
IPython : 8.12.1
pandas_datareader: None
bs4 : 4.12.2
bottleneck : None
brotli : 1.0.9
fastparquet : 2022.12.0
fsspec : 2023.4.0
gcsfs : None
matplotlib : 3.7.1
numba : None
numexpr : 2.8.4
odfpy : None
openpyxl : 3.0.10
pandas_gbq : 0.19.1
pyarrow : 11.0.0
pyreadstat : None
pyxlsb : None
s3fs : 0.4.2
scipy : 1.10.1
snappy : None
sqlalchemy : 1.4.45
tables : 3.8.0
tabulate : None
xarray : 2023.1.0
xlrd : 2.0.1
zstandard : None
tzdata : 2023.3
qtpy : None
pyqt5 : None

Dominant language
Python
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