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 [2]: import pandas as pd
In [3]: from io import StringIO
In [4]: csv_str = ',t1,t2\n0,2020-08-01 09:00:00,1940-08-31 06:00:00\n1,1920-05-01 10:30:00,2020-08-02 10:00:00\n'
In [5]: df = pd.read_csv(StringIO(csv_str))
In [6]: df = df[['t1', 't2']]
In [7]: df['t1'] = df['t1'].astype('datetime64[us]')
In [8]: df['t2'] = df['t2'].astype('datetime64[us]')
In [9]: df
Out[9]:
t1 t2
0 2020-08-01 09:00:00 1940-08-31 06:00:00
1 1920-05-01 10:30:00 2020-08-02 10:00:00
In [10]: df.max(axis=1)
Out[10]:
0 2020-08-01 09:00:00
1 2020-08-02 10:00:00
dtype: datetime64[us]
In [11]: df['t2'] = df['t2'].astype('datetime64[ms]')
In [12]: df.dtypes
Out[12]:
t1 datetime64[us]
t2 datetime64[ms]
dtype: object
In [13]: df.max(axis=1)
Out[13]:
0 2020-08-01 09:00:00
1 2020-08-02 10:00:00
dtype: datetime64[ns]Issue Description
In the above example, a row-wise operation on different time resolutions seems to be defaulting to ns time resolution, whereas here we can clearly choose to return datetime64[us](the lowest possible time resolution depending on the operation performed-min).
Expected Behavior
In [13]: df.max(axis=1)
Out[13]:
0 2020-08-01 09:00:00
1 2020-08-02 10:00:00
dtype: datetime64[us]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 : 2023.3
dateutil : 2.8.2
setuptools : 67.6.1
pip : 23.0.1
Cython : 0.29.34
pytest : 7.2.2
hypothesis : 6.70.2
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 : 4.12.0
bottleneck : None
brotli :
fastparquet : None
fsspec : 2023.3.0
gcsfs : None
matplotlib : None
numba : 0.56.4
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 10.0.1
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