Description
Pandas version checks
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.
Reproducible Example
import pandas as pd
date = pd.Timestamp("2025-01-01")
df = pd.DataFrame(columns=["date"], index=["a", "b", "c"])
df["date"] = date
print(df["date"].dtype) # Output: datetime64[s] Expected: datetime64[ns]
print(df.to_json()) # Output: {"date":{"a":1696,"b":1696,"c":1696}}
# Expected: {"date":{"a":1735689600000,"b":1735689600000,"c":1735689600000}}Issue Description
When assigning a pd.Timestamp to a column in a DataFrame, the resulting dtype of the column is not as expected, and the output of to_json() is incorrect.
Expected Behavior
The dtype of the date column should default to datetime64[ns] after assignment.
The output of df.to_json() should correctly represent the timestamp in milliseconds since the epoch.
Installed Versions
INSTALLED VERSIONS
commit : 0691c5c
python : 3.12.4
python-bits : 64
OS : Darwin
OS-release : 24.4.0
Version : Darwin Kernel Version 24.4.0: Fri Apr 11 18:33:47 PDT 2025; root:xnu-11417.101.15~117/RELEASE_ARM64_T6000
machine : arm64
processor : arm
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 2.2.3
numpy : 2.2.4
pytz : 2025.2
dateutil : 2.9.0.post0
pip : 25.0.1
Cython : None
sphinx : None
IPython : None
adbc-driver-postgresql: None
adbc-driver-sqlite : None
bs4 : None
blosc : None
bottleneck : None
dataframe-api-compat : None
fastparquet : None
fsspec : None
html5lib : None
hypothesis : None
gcsfs : None
jinja2 : 3.1.6
lxml.etree : None
matplotlib : None
numba : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
psycopg2 : None
pymysql : None
pyarrow : None
pyreadstat : None
pytest : None
python-calamine : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlsxwriter : None
zstandard : None
tzdata : 2025.2
qtpy : None
pyqt5 : None
None
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BUG: Fix Dataframe handling of scalar Timestamp pandas-dev#61444
Farsidetfs commentedon May 17, 2025
take
Farsidetfs commentedon May 17, 2025
I was able to duplicate and believe my PR will resolve this issue. If confirmed as a bug worth fixing, will update tests and change document and submit PR for approval.
rhshadrach commentedon May 17, 2025
Thanks for the report, it seems to me the issue lies with Timestamp creation rather than assignment to a DataFrame.
As such, closing as a duplicate of #58989
Farsidetfs commentedon May 19, 2025
@rhshadrach
I think this is a separate issue as this version fails as well.
#this test fails
date2 = Timestamp(year=2025, month=1, day=1)
df4 = DataFrame(index=['a', 'b', 'c'], columns=["date"], dtype='datetime64[ns]')
df4["date"] = date2
assert df4["date"].dtype == "datetime64[ns]"
print("df4 assertion passed")
#this test passes
date = Timestamp("2025-01-01")
df2 = DataFrame(index=['a', 'b', 'c'], columns=["date"], dtype='datetime64[ns]')
df2["date"] = [date]*len(df2)
assert df2["date"].dtype == "datetime64[ns]"
print("df2 assertion passed")