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()
df['date1'] = pd.Timestamp.now()
df['date2'] = pd.Timestamp('now')
df['date3'] = pd.Timestamp('2023-11-08 11:13:14')
df.dtypesIssue Description
Three columns above have three different types:
date1 datetime64[us]
date2 datetime64[ns]
date3 datetime64[s]
This is rather unexpected behavior. I could not find an explanation for it on https://pandas.pydata.org/docs/dev/whatsnew/v2.1.0.html#
Expected Behavior
date1 datetime64[ns]
date2 datetime64[ns]
date3 datetime64[ns]
Installed Versions
INSTALLED VERSIONS
commit : a60ad39
python : 3.11.5.final.0
python-bits : 64
OS : Linux
OS-release : 5.10.102.1-microsoft-standard-WSL2
Version : #1 SMP Wed Mar 2 00:30:59 UTC 2022
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : en_US.UTF-8
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 2.1.2
numpy : 1.24.2
pytz : 2023.3.post1
dateutil : 2.8.2
setuptools : 65.5.0
pip : 23.3.1
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : 2.9.6
jinja2 : None
IPython : 8.16.1
pandas_datareader : None
bs4 : None
bottleneck : None
dataframe-api-compat: None
fastparquet : None
fsspec : None
gcsfs : None
matplotlib : 3.8.0
numba : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyreadstat : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : 1.4.49
tables : None
tabulate : None
xarray : None
xlrd : None
zstandard : None
tzdata : 2023.3
qtpy : None
pyqt5 : None