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Showing content from https://github.com/pandas-dev/pandas/issues/55487 below:

Assignment of Timestamp Scalar uses micrsosecond precision, Series uses nano · Issue #55487 · pandas-dev/pandas · GitHub

• I have checked that this issue has not already been reported.

• I have confirmed this bug exists on the latest version of pandas.

• I have confirmed this bug exists on the main branch of pandas.

import pandas as pd
ts = pd.Timestamp.now()
df = pd.DataFrame({"a": [1]})
df["direct_assignment"] = ts
df["series_assignment"] = pd.Series(ts)
df.dtypes


yields

```python
a                             int64
direct_assignment    datetime64[us]
series_assignment    datetime64[ns]
dtype: object

yields

I was surprised to see the dtype mismatch here

At least for backwards compatability we might want to still make the scalar assignment still yield nanosecond resolution

commit : c2cd90a
python : 3.10.12.final.0
python-bits : 64
OS : Linux
OS-release : 6.2.0-33-generic
Version : #33-Ubuntu SMP PREEMPT_DYNAMIC Tue Sep 5 14:49:19 UTC 2023
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8

pandas : 2.2.0dev0+341.gc2cd90ac54
numpy : 1.24.4
pytz : 2023.3.post1
dateutil : 2.8.2
setuptools : 68.0.0
pip : 23.2.1
Cython : 0.29.33
pytest : 7.4.2
hypothesis : 6.87.1
sphinx : 7.2.6
blosc : None
feather : None
xlsxwriter : 3.1.6
lxml.etree : 4.9.3
html5lib : 1.1
pymysql : 1.4.6
psycopg2 : 2.9.7
jinja2 : 3.1.2
IPython : 8.16.1
pandas_datareader : None
bs4 : 4.12.2
bottleneck : 1.3.7
dataframe-api-compat: None
fastparquet : 2023.8.0
fsspec : 2023.9.2
gcsfs : 2023.9.2
matplotlib : 3.7.3
numba : 0.57.1
numexpr : 2.8.7
odfpy : None
openpyxl : 3.1.2
pandas_gbq : None
pyarrow : 13.0.0
pyreadstat : 1.2.3
python-calamine : None
pyxlsb : 1.0.10
s3fs : 2023.9.2
scipy : 1.11.3
sqlalchemy : 2.0.21
tables : 3.8.0
tabulate : 0.9.0
xarray : 2023.9.0
xlrd : 2.0.1
zstandard : 0.21.0
tzdata : 2023.3
qtpy : None
pyqt5 : None

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