BUG: crash in df.groupby.rolling.mean with forward window indexer · Issue #43267 · pandas-dev/pandas (original) (raw)

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Code Sample, a copy-pastable example

Your code here

Here is a replicate code: df = pd.DataFrame({'B': [np.nan, 1, 2, np.nan, 4,5,6,7,8,9,10,11,12,13,14,15]}) df['A']=1 indexer = pd.api.indexers.FixedForwardWindowIndexer(window_size=12) # forward 12 period

First time is OK

df['mean']=df.groupby('A')['B'].rolling(window=indexer, min_periods=1).mean().reset_index().loc[:,'B']

Second time would crash!

df['mean']=df.groupby('A')['B'].rolling(window=indexer, min_periods=1).mean().reset_index().loc[:,'B']

If don't have groupby, it won't crash. If don't forward window indexer, it won't crash. If in the first time, it won't crash.

Problem description

[this should explain why the current behaviour is a problem and why the expected output is a better solution]

When you use df.groupby.rolling.mean with a forward-looking period, it would crash if you run the df.groupby.rolling.mean again.

Expected Output

Output of pd.show_versions()

INSTALLED VERSIONS ------------------ commit : c7f7443python : 3.8.11.final.0 python-bits : 64 OS : Windows OS-release : 10 Version : 10.0.19042 machine : AMD64 processor : Intel64 Family 6 Model 165 Stepping 2, GenuineIntel byteorder : little LC_ALL : None LANG : zh_CN LOCALE : Chinese (Simplified)_China.936

pandas : 1.3.1
numpy : 1.20.3
pytz : 2021.1
dateutil : 2.8.2
pip : 21.2.2
setuptools : 52.0.0.post20210125
Cython : 0.29.24
pytest : 6.2.4
hypothesis : None
sphinx : 4.0.2
blosc : None
feather : None
xlsxwriter : 3.0.1
lxml.etree : 4.6.3
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.3
IPython : 7.26.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : 1.3.2
fsspec : 2021.07.0
fastparquet : None
gcsfs : None
matplotlib : 3.4.2
numexpr : 2.7.3
odfpy : None
openpyxl : 3.0.7
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.6.2
sqlalchemy : 1.4.22
tables : 3.6.1
tabulate : None
xarray : None
xlrd : 2.0.1
xlwt : 1.3.0
numba : 0.53.1
[paste the output of pd.show_versions() here leaving a blank line after the details tag]