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BUG: read_hdf() doesn't handle datetime64[ms] properly #61479

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Closed
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a-ma72 opened this issue May 22, 2025 · 2 comments · May be fixed by #61482
Closed
3 tasks done

BUG: read_hdf() doesn't handle datetime64[ms] properly #61479

a-ma72 opened this issue May 22, 2025 · 2 comments · May be fixed by #61482
Labels
Bug Closing Candidate May be closeable, needs more eyeballs IO HDF5 read_hdf, HDFStore Needs Info Clarification about behavior needed to assess issue

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@a-ma72
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a-ma72 commented May 22, 2025

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```
@a-ma72 a-ma72 added Bug Needs Triage Issue that has not been reviewed by a pandas team member labels May 22, 2025
@rhshadrach
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Thanks for the report. On main, I'm seeing that the correct output.

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)
#                 dates  tags
# 0 2025-05-21 18:44:22    12
# 1 2025-05-21 19:12:42    45

df.to_hdf("dates.h5", key="dates")
df2 = pd.read_hdf("dates.h5", key="dates")
print(df2)
#                 dates  tags
# 0 2025-05-21 18:44:22    12
# 1 2025-05-21 19:12:42    45

You've checked that you confirmed this bug exists on the main branch. Are you certain you were running on the main branch of pandas (not the latest released version).

@rhshadrach rhshadrach added IO HDF5 read_hdf, HDFStore Needs Info Clarification about behavior needed to assess issue Closing Candidate May be closeable, needs more eyeballs and removed Needs Info Clarification about behavior needed to assess issue Needs Triage Issue that has not been reviewed by a pandas team member labels May 23, 2025
@a-ma72
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a-ma72 commented May 23, 2025

You are right, the “main branch” was not installed correctly in my test case and fell back to the last release version, sorry.

@AnushaUKumar AnushaUKumar mentioned this issue May 23, 2025
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BUG: Raise error when filtering HDF5 with tz-aware index (GH#61479) AnushaUKumar/pandas
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@mroeschke @a-ma72 @rhshadrach