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
In [34]: s = pd.Series([1233242342344, 232432434324, 332434242344], dtype='datetime64[ms]')
In [35]: s - np.datetime64("nat", 'ms')
Out[35]:
0 NaT
1 NaT
2 NaT
dtype: timedelta64[ns] # Can we preserve `ms` instead of `ns` here?
In [36]: s
Out[36]:
0 2009-01-29 15:19:02.344
1 1977-05-14 04:33:54.324
2 1980-07-14 14:50:42.344
dtype: datetime64[ms]
In [37]: s - np.datetime64("nat")
Out[37]:
0 NaT
1 NaT
2 NaT
dtype: timedelta64[ns] # Can we preserve `ms` instead of `ns` here?
In [38]: s + np.timedelta64("nat")
Out[38]:
0 NaT
1 NaT
2 NaT
dtype: datetime64[ns] # Can we preserve `ms` instead of `ns` here?
In [39]: s + np.timedelta64("nat", 'ms')
Out[39]:
0 NaT
1 NaT
2 NaT
dtype: datetime64[ns] # Can we preserve `ms` instead of `ns` here?
In [42]: p = pd.Series([None, None, None], dtype='datetime64[ms]')
In [43]: p
Out[43]:
0 NaT
1 NaT
2 NaT
dtype: datetime64[ms]
In [44]: s - p
Out[44]:
0 NaT
1 NaT
2 NaT
dtype: timedelta64[ms] # This looks good.Issue Description
When we perform a binary operation against a nat scalar, we seem to be always type-casting the result to ns resolution. Whereas incase of a nat series, we seem to be returning the expected type consistently. Can we make this behavior consistent in scalar nat cases too?
Expected Behavior
array vs array binop & array vs scalar binop must yield similar time resolutions.
Installed Versions
INSTALLED VERSIONS
commit : c2a7f1a
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 : 2.0.0rc1
numpy : 1.23.5
pytz : 2023.2
dateutil : 2.8.2
setuptools : 67.6.0
pip : 23.0.1
Cython : 0.29.33
pytest : 7.2.2
hypothesis : 6.70.1
sphinx : 5.3.0
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.1.2
IPython : 8.11.0
pandas_datareader: None
bs4 : 4.12.0
bottleneck : None
brotli :
fastparquet : None
fsspec : 2023.3.0
gcsfs : None
matplotlib : None
numba : 0.56.4
numexpr : None
odfpy : None
openpyxl : 3.1.2
pandas_gbq : None
pyarrow : 11.0.0
pyreadstat : None
pyxlsb : None
s3fs : 2023.3.0
scipy : 1.10.1
snappy :
sqlalchemy : 1.4.46
tables : None
tabulate : 0.9.0
xarray : None
xlrd : None
zstandard : None
tzdata : None
qtpy : None
pyqt5 : None