BUG: Localizing Timestamp.min changes dtype to object

[robert-schmidtke]

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

pd.Series([pd.Timestamp.min.tz_localize("utc")]).dtype
# dtype('O')

# not an issue when adding to it
pd.Series([(pd.Timestamp.min + pd.Timedelta(microseconds=1)).tz_localize("utc")]).dtype
# datetime64[ns, UTC]

# not an issue with pd.Timestamp.max
pd.Series([pd.Timestamp.max.tz_localize("utc")]).dtype
# datetime64[ns, UTC]

# not an issue with now
pd.Series([pd.Timestamp.utcnow()]).dtype
# datetime64[ns, UTC]

Issue Description

When localizing pd.Timestamp.min, the dtype of a pd.Series holding just that value changes to object.

In pandas 2.1.4, this is not the case.

import pandas as pd
pd.Series([pd.Timestamp.min.tz_localize("utc")]).dtype
# datetime64[ns, UTC]

Expected Behavior

Keep the datetime62[ns, UTC] dtype.

Installed Versions

INSTALLED VERSIONS

commit : f538741
python : 3.11.7.final.0
python-bits : 64
OS : Linux
OS-release : 5.15.133.1-microsoft-standard-WSL2
Version : #1 SMP Thu Oct 5 21:02:42 UTC 2023
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : C.UTF-8
LOCALE : en_US.UTF-8

pandas : 2.2.0
numpy : 1.26.3
pytz : 2023.4
dateutil : 2.8.2
setuptools : 69.0.3
pip : 23.3.2
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader : None
adbc-driver-postgresql: None
adbc-driver-sqlite : None
bs4 : None
bottleneck : None
dataframe-api-compat : None
fastparquet : None
fsspec : None
gcsfs : None
matplotlib : None
numba : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 14.0.2
pyreadstat : None
python-calamine : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
zstandard : None
tzdata : 2023.4
qtpy : None
pyqt5 : None

[rhshadrach]

Thanks for the report, confirmed on main. Further investigations and PRs to fix are welcome!

[robert-schmidtke]

I did a bisect and looks like this PR introduced that behavior change: #55151

commit e477eee4916ab0a867f2755d008dde9cf006f671
Author: William Ayd <[email protected]>
Date:   Wed Nov 15 13:20:46 2023 -0500

    Remove undefined behavior from npy_datetimestruct_to_datetime (#55151)

@WillAyd would you have an idea what could be the cause of this? I have to admit I don't fully grasp the impact of that PR :)

[quangngd]

Shorter reproduce code

from pandas import DatetimeIndex

DatetimeIndex([pd.Timestamp.min.tz_localize("utc")])

In the above Reproducible Example, the Series constructor eventually try to test for datetime at pandas/_libs/lib.pyx:maybe_convert_objects in the same way.

[WillAyd]

My guess is some of the algorithms were previously invoking undefined behavior at the very lower bounds and now we are explicitly catching that. Especially since adding a microsecond to the timestamp doesn't cause the problem, probably something at the nanosecond precision is overflowing

[robert-schmidtke]

I did some basic debugging and I believe the issue is that this overflows: scaleMicrosecondsToNanoseconds(microseconds, &nanoseconds) even though the next add of dts->ps / 1000 would be within the int64_t range again.

  if (base == NPY_FR_ns) {
    int64_t nanoseconds;

    // microseconds is -9223372036854776 here, so would scale to -9223372036854776000
    // but INT64_MIN is -9223372036854775808
    PD_CHECK_OVERFLOW(
        scaleMicrosecondsToNanoseconds(microseconds, &nanoseconds));

    // dts->ps is 193000 here, and adding 193 to -9223372036854776000 would be fine:
    // -9223372036854775807
    PD_CHECK_OVERFLOW(
        checked_int64_add(nanoseconds, dts->ps / 1000, &nanoseconds));

    return nanoseconds;
  }

[WillAyd]

Great find! Happy to accept any PRs you see to solve it