BUG: `round`, `ceil`, `floor` returning incorrect results with Division By Zero warning in `pandas-2.0` · Issue #52761 · pandas-dev/pandas (original) (raw)

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Reproducible Example

In [1]: import pandas as pd

In [2]: s = pd.Series(["2020-05-31 08:00:00", "2000-12-31 04:00:05", "1800-03-14 07:30:20"], dtype='datetime64[ms]')

In [3]: s Out[3]: 0 2020-05-31 08:00:00 1 2000-12-31 04:00:05 2 1800-03-14 07:30:20 dtype: datetime64[ms]

In [4]: s.dt.round('U') /nvme/0/pgali/envs/cudfdev/lib/python3.10/site-packages/pandas/core/arrays/datetimelike.py:2006: RuntimeWarning: divide by zero encountered in divmod result_i8 = round_nsint64(values, mode, nanos) Out[4]: 0 1970-01-01 1 1970-01-01 2 1970-01-01 dtype: datetime64[ms]

Issue Description

Performing a rounding operation of resolution lesser than the dtype, or when there is sub-second data and the rounding resolution is a sub-second data should ideally be a no-op. Instead we seem to be running into division by zero scenario and returning incorrect results.

Expected Behavior

In [3]: s.dt.round('U') Out[3]: 0 2020-05-31 08:00:00 1 2000-12-31 04:00:05 2 1800-03-14 07:30:20 dtype: datetime64[ms]

Installed Versions

In [5]: pd.show_versions()
/nvme/0/pgali/envs/cudfdev/lib/python3.10/site-packages/_distutils_hack/init.py:33: UserWarning: Setuptools is replacing distutils.
warnings.warn("Setuptools is replacing distutils.")

INSTALLED VERSIONS

commit : 478d340
python : 3.10.10.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-76-generic
Version : #86-Ubuntu SMP Fri Jan 17 17:24:28 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8

pandas : 2.0.0
numpy : 1.23.5
pytz : 2023.3
dateutil : 2.8.2
setuptools : 67.6.1
pip : 23.1
Cython : 0.29.34
pytest : 7.3.1
hypothesis : 6.72.0
sphinx : 5.3.0
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.1.2
IPython : 8.12.0
pandas_datareader: None
bs4 : 4.12.2
bottleneck : None
brotli :
fastparquet : None
fsspec : 2023.4.0
gcsfs : None
matplotlib : None
numba : 0.56.4
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 11.0.0
pyreadstat : None
pyxlsb : None
s3fs : 2023.4.0
scipy : 1.10.1
snappy :
sqlalchemy : 1.4.46
tables : None
tabulate : 0.9.0
xarray : None
xlrd : None
zstandard : None
tzdata : 2023.3
qtpy : None
pyqt5 : None