BUG: .rolling() returns incorrect values when ts index is not nano seconds

[haim0n]

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

import pandas as pd

df_time = pd.DataFrame({'B': [0, 1, 2, 4, 5, 6]},
                       index=[pd.Timestamp('20130101 09:00:00'),
                              pd.Timestamp('20130101 09:00:02'),
                              pd.Timestamp('20130101 09:00:03'),
                              pd.Timestamp('20130101 09:00:06'),
                              pd.Timestamp('20130101 09:00:07'),
                              pd.Timestamp('20130101 09:00:08')])
sum1 = df_time.rolling('1s').sum()
# micro seconds / milliseconds breaks the correct rolling!
df_time.index = df_time.index.as_unit('us')
sum2 = df_time.rolling('1s').sum()

pd.testing.assert_frame_equal(sum1, sum2)

Issue Description

The rolling function returns incorrect values in case of the index isn't nano seconds.

Expected Behavior

Both sums should be identical

Installed Versions

INSTALLED VERSIONS

commit : 37ea63d
python : 3.10.9.final.0
python-bits : 64
OS : Linux
OS-release : 6.2.0-31-generic
Version : #31~22.04.1-Ubuntu SMP PREEMPT_DYNAMIC Wed Aug 16 13:45:26 UTC 2
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_IL
LOCALE : en_IL.UTF-8
pandas : 2.0.1
numpy : 1.24.3
pytz : 2023.3
dateutil : 2.8.2
setuptools : 57.5.0
pip : 22.3.1
Cython : 3.0.0
pytest : 7.3.1
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : 3.1.2
lxml.etree : 4.9.3
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.1.2
IPython : 8.14.0
pandas_datareader: None
bs4 : 4.12.2
bottleneck : 1.3.7
brotli : 1.0.9
fastparquet : 2023.8.0
fsspec : 2023.6.0
gcsfs : 2023.6.0
matplotlib : 3.7.2
numba : 0.57.1
numexpr : 2.8.4
odfpy : None
openpyxl : 3.1.2
pandas_gbq : 0.19.2
pyarrow : 12.0.1
pyreadstat : None
pyxlsb : 1.0.10
s3fs : None
scipy : 1.10.1
snappy : None
sqlalchemy : None
tables : 3.8.0
tabulate : None
xarray : 2023.7.0
xlrd : 2.0.1
zstandard : None
tzdata : 2023.3
qtpy : None
pyqt5 : None

[paulreece]

I tested this locally on 2.1.0 and can confirm it is a bug.

[haimjether]

Note that on main branch, the usecase should be changed to:

import pandas as pd

df_time = pd.DataFrame({'B': [0, 1, 2, 4, 5 ,6]},
                       index=[pd.Timestamp('20130101 09:00:00'),
                              pd.Timestamp('20130101 09:00:02'),
                              pd.Timestamp('20130101 09:00:03'),
                              pd.Timestamp('20130101 09:00:06'),
                              pd.Timestamp('20130101 09:00:07'),
                              pd.Timestamp('20130101 09:00:08')])
sum1 = df_time.rolling('1s').sum()
print(sum1)
# micro seconds / milliseconds breaks the correct rolling!
df_time.index = df_time.index.as_unit('us')
sum2 = df_time.rolling('1s').sum()
print(sum2)

assert sum1.values == sum2.values

[hkhojasteh]

I'm going to fix it :)