Closed
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
df = pd.DataFrame(
{
"dates": [
pd.to_datetime("2025-05-21 18:44:22"),
pd.to_datetime("2025-05-21 19:12:42"),
],
"tags": [
12,
45,
]
},
)
df["dates"] = df["dates"].astype("datetime64[ms]")
print(df.dtypes)
print(df)
df.to_hdf("dates.h5", key="dates")
df2 = pd.read_hdf("dates.h5", key="dates")
print(df2)
df2["corrected"] = df2["dates"].astype("i8").astype("datetime64[ms]")
print(df2)Issue Description
Dataframes containing dtype of "datetime64[ms]" seem to be correctly written in hdf format, but the readback is misinterpreted as “datetime64[ns]”.
The output of the code above is:
tags int64
dtype: object
dates tags
0 2025-05-21 18:44:22 12
1 2025-05-21 19:12:42 45
dates tags
0 1970-01-01 00:29:07.853062 12
1 1970-01-01 00:29:07.854762 45
dates tags corrected
0 1970-01-01 00:29:07.853062 12 2025-05-21 18:44:22
1 1970-01-01 00:29:07.854762 45 2025-05-21 19:12:42```
### Expected Behavior
Correct dates when read back.
### Installed Versions
```INSTALLED VERSIONS
------------------
commit : d9cdd2ee5a58015ef6f4d15c7226110c9aab8140
python : 3.11.9.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.22631
machine : AMD64
processor : Intel64 Family 6 Model 183 Stepping 1, GenuineIntel
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : de_DE.cp1252
pandas : 2.2.2
numpy : 1.26.4
pytz : 2024.1
dateutil : 2.9.0.post0
setuptools : 80.8.0
pip : 25.1.1
Cython : 3.1.1
pytest : 8.3.5
hypothesis : 6.131.20
sphinx : 8.2.3
blosc : None
feather : None
xlsxwriter : 3.2.3
lxml.etree : 5.4.0
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 3.1.6
IPython : 8.36.0
pandas_datareader : None
adbc-driver-postgresql: None
adbc-driver-sqlite : None
bs4 : 4.13.4
bottleneck : 1.5.0
dataframe-api-compat : None
fastparquet : None
fsspec : 2025.5.0
gcsfs : None
matplotlib : 3.8.4
numba : 0.61.2
numexpr : 2.10.2
odfpy : None
openpyxl : 3.1.2
pandas_gbq : None
pyarrow : 17.0.0
pyreadstat : None
python-calamine : None
pyxlsb : None
s3fs : None
scipy : 1.15.3
sqlalchemy : 2.0.30
tables : 3.10.2
tabulate : None
xarray : 2025.4.0
xlrd : 2.0.1
zstandard : 0.23.0
tzdata : 2025.2
qtpy : 2.4.3
pyqt5 : None```