BUG: Using df.groupby(..).rolling(..) on non-monotonic timestamp column does not raise an exception · Issue #43909 · pandas-dev/pandas (original) (raw)
- 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 master branch of pandas.
Reproducible Example
import pandas as pd
shuffled = [3, 0, 1, 2] sec = 1_000_000_000 df = pd.DataFrame([{'t': pd.Timestamp(2 * x * sec), 'x': x+1, 'c': 42} for x in shuffled])
t x c
0 1970-01-01 00:00:06 4 42
1 1970-01-01 00:00:00 1 42
2 1970-01-01 00:00:02 2 42
3 1970-01-01 00:00:04 3 42
Following row raises an Exception (as expected):
df.rolling(on='t', window='3s').x.sum()
Following code should raise the same exception, but instead it gives meaningless results:
df.groupby('c').rolling(on='t', window='3s').x.sum()
c t
42 1970-01-01 00:00:06 4.0
1970-01-01 00:00:00 1.0
1970-01-01 00:00:02 3.0
1970-01-01 00:00:04 6.0
Issue Description
When calling df.groupby(..).rolling(on='...', ...) on some Timestamp column (with timedelta window) which is not sorted, pandas gives some meaningless results.
Expected Behavior
An exception should be raised, as is the case when we call df.rolling(on=...) (without the groupby) on non-monotonic column
Installed Versions
INSTALLED VERSIONS
------------------
commit : 73c68257545b5f8530b7044f56647bd2db92e2ba
python : 3.8.10.final.0
python-bits : 64
OS : Linux
OS-release : 5.11.0-37-generic
Version : #41~20.04.2-Ubuntu SMP Fri Sep 24 09:06:38 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.3.3
numpy : 1.21.2
pytz : 2021.1
dateutil : 2.8.2
pip : 21.2.3
setuptools : 45.2.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.5.0
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.13.0
pandas_datareader: None
bs4 : 4.8.2
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.7.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : 2.0.1
xlwt : None
numba : None