Closed
Description
Pandas version checks
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I have checked that this issue has not already been reported.
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I have confirmed this bug exists on the latest version of pandas.
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I have confirmed this bug exists on the main branch of pandas.
Reproducible Example
import pandas as pd
df = pd.DataFrame(["NaT", "2024-04-16 09:20:00.123456789"], dtype="datetime64[ns]")
df.max(axis=1)
# prints:
#
# 0 NaT
# 1 2024-04-16 09:20:00.123456768
# dtype: datetime64[ns]Issue Description
The max (axis=1) of a DataFrame of one column should return that column (type pd.Series).
However, when the dtype is datetime64[ns] and the column contains a NaT, there are small differences with the original and returned column.
In the MWE the difference is 21 nanoseconds.
Note: This only happens if there is a NaT in column values.
Expected Behavior
Expected behavior is to print
0 NaT
1 2024-04-16 09:20:00.123456789
dtype: datetime64[ns]
Installed Versions
INSTALLED VERSIONS
------------------
commit : 0691c5cf90477d3503834d983f69350f250a6ff7
python : 3.11.10
python-bits : 64
OS : Linux
OS-release : 4.14.355-271.569.amzn2.x86_64
Version : #1 SMP Tue Nov 5 10:11:37 UTC 2024
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : C.UTF-8
LOCALE : en_US.UTF-8
pandas : 2.2.3
numpy : 1.26.0
pytz : 2024.1
dateutil : 2.9.0
pip : 24.3.1
Cython : 0.29.30
sphinx : None
IPython : 8.21.0
adbc-driver-postgresql: None
adbc-driver-sqlite : None
bs4 : 4.12.3
blosc : None
bottleneck : None
dataframe-api-compat : None
fastparquet : None
fsspec : 2024.10.0
html5lib : 1.1
hypothesis : None
gcsfs : None
jinja2 : 3.1.4
lxml.etree : None
matplotlib : 3.8.1
numba : None
numexpr : 2.10.1
odfpy : None
openpyxl : 3.1.5
pandas_gbq : None
psycopg2 : None
pymysql : None
pyarrow : 15.0.2
pyreadstat : None
pytest : None
python-calamine : None
pyxlsb : None
s3fs : 2024.10.0
scipy : 1.14.1
sqlalchemy : None
tables : 3.8.0
tabulate : 0.9.0
xarray : None
xlrd : None
xlsxwriter : None
zstandard : None
tzdata : 2024.2
qtpy : None
pyqt5 : None