BUG: datetime64[s] data changes when put into HDFStore

[shaunfinn]

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

x = pd.Timestamp("20220101")                         # datetime64[s]
y = pd.Timestamp("2022-01-01T00:00:00.000000000")    # datetime64[ns]

df = pd.DataFrame({"x":x, "y":y}, index=list(range(5)))

store = pd.HDFStore("store.h5", 'w')  
store.put('data', df, format='table')
df2 = store.get('data')
store.close()

df.equals(df2)     # <  Error here. df should equal df2

Issue Description

df.equals(df2) = False
Should be True.

When a dataframe containing datetime64[s] (i.e. column "x") is saved to HDFstore and retrieved, the values change.
Specifically, they are 10^9 times smaller.
It looks like the HDFStore assumes datetime data is in nanoseconds (datetime64[ns])

Expected Behavior

df.equals(df2) = True.

Works with pandas 1.5.3

Installed Versions

INSTALLED VERSIONS

commit : d9cdd2e
python : 3.10.11.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.22631
machine : AMD64
processor : Intel64 Family 6 Model 140 Stepping 1, GenuineIntel
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : English_United States.1252

pandas : 2.2.2
numpy : 1.26.4
pytz : 2024.1
dateutil : 2.9.0.post0
setuptools : 70.1.1
pip : 24.1
Cython : None
pytest : 8.2.2
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.1.4
IPython : 8.25.0
pandas_datareader : None
adbc-driver-postgresql: None
adbc-driver-sqlite : None
bs4 : 4.12.3
bottleneck : 1.4.0
dataframe-api-compat : None
fastparquet : 2024.5.0
fsspec : 2024.6.0
gcsfs : None
matplotlib : None
numba : None
numexpr : 2.10.1
odfpy : None
openpyxl : 3.1.0
pandas_gbq : None
pyarrow : 16.1.0
pyreadstat : None
python-calamine : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : 3.9.2
tabulate : None
xarray : None
xlrd : 2.0.1
zstandard : None
tzdata : 2024.1
qtpy : None
pyqt5 : None

[Aloqeely]

Thanks for the report! This has been reported in #59004 and is fixed on main.
Going to close, the fix will be released with pandas v3.0