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BUG: dataframe.to_hdf function incompatible with timestamps of format "datetime64[us, UTC]" #60353

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ChristophWiesmeyr opened this issue Nov 18, 2024 · 3 comments · May be fixed by #60387
Open
2 of 3 tasks

BUG: dataframe.to_hdf function incompatible with timestamps of format "datetime64[us, UTC]" #60353

ChristophWiesmeyr opened this issue Nov 18, 2024 · 3 comments · May be fixed by #60387
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@KevsterAmp
Labels
Bug IO HDF5 read_hdf, HDFStore Needs Tests Unit test(s) needed to prevent regressions Timezones Timezone data dtype

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@ChristophWiesmeyr
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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

if __name__ == "__main__":
    dataframe = pd.DataFrame(
        {
            "start_time": [
                pd.to_datetime("2024-08-26 15:13:14.700000+00:00"),
                pd.to_datetime("2024-08-26 15:14:14.700000+00:00"),
            ]
        }
    )
    dataframe["start_time_us"] = dataframe.start_time.astype("datetime64[us, UTC]")

    dataframe.to_hdf("test.hdf", key="Annotations", mode="w")

    recovered_dataframe = pd.read_hdf("test.hdf", key="Annotations")

    pd.testing.assert_frame_equal(dataframe, recovered_dataframe)

Issue Description

Dumping a dataframe with a column of datetime64[us, UTC] datetype to an HDF file seems to write datetime[ns, UTC] into the file. When recovering the data from the HDF file it seems that the dates are wrong, which is probably caused by an erroneous interpretation of the values as datetime[ns, UTC].

Expected Behavior

datetime64[us, UTC] values which have been written from a dataframe into an HDF file using the to_hdf function should be recoverable using the pd.read_hdf function.

Installed Versions

INSTALLED VERSIONS

commit : 0691c5c
python : 3.10.12
python-bits : 64
OS : Linux
OS-release : 5.15.0-118-generic
Version : #128-Ubuntu SMP Fri Jul 5 09:28:59 UTC 2024
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 2.2.3
numpy : 1.26.4
pytz : 2024.2
dateutil : 2.9.0.post0
pip : 24.2
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 : 2024.5.0
fsspec : 2024.9.0
html5lib : None
hypothesis : None
gcsfs : None
jinja2 : 3.1.4
lxml.etree : 5.3.0
matplotlib : 3.7.3
numba : None
numexpr : 2.10.1
odfpy : None
openpyxl : 3.1.5
pandas_gbq : None
psycopg2 : None
pymysql : None
pyarrow : 17.0.0
pyreadstat : None
pytest : None
python-calamine : None
pyxlsb : None
s3fs : None
scipy : 1.14.1
sqlalchemy : None
tables : 3.10.1
tabulate : None
xarray : None
xlrd : None
xlsxwriter : None
zstandard : None
tzdata : 2024.2
qtpy : None
pyqt5 : None
@ChristophWiesmeyr ChristophWiesmeyr added Bug Needs Triage Issue that has not been reviewed by a pandas team member labels Nov 18, 2024
@rhshadrach
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Thanks for the report! I cannot reproduce this on main, but can on 2.2.x. Wondering if it might be due to #59089. Still need to run a git bisect to see what fixed this and if a test needs to be added.

@rhshadrach rhshadrach added IO HDF5 read_hdf, HDFStore Localization Internationalization of data labels Nov 18, 2024
@mroeschke mroeschke added Timezones Timezone data dtype and removed Localization Internationalization of data labels Nov 19, 2024
@rhshadrach
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This was inadvertently fixed by #59018. Could use a test.

@rhshadrach rhshadrach added Needs Tests Unit test(s) needed to prevent regressions and removed Needs Triage Issue that has not been reviewed by a pandas team member labels Nov 19, 2024
@KevsterAmp
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Take

@KevsterAmp KevsterAmp linked a pull request Nov 21, 2024 that will close this issue
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TST: Add test for retaining dtype of datetime columns in DataFrame.to_hdf KevsterAmp/pandas
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@ChristophWiesmeyr @mroeschke @rhshadrach @KevsterAmp