BUG: scalar out of bounds input does not raise for to_datetime

[kmuehlbauer]

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 numpy as np
import pandas as pd
uint64_max = np.iinfo("uint64").max

try:
    dtime = pd.to_datetime(uint64_max, unit="ns")
    print("Wrong:", dtime)
    dtime = pd.to_datetime([uint64_max], unit="ns")
except Exception as err:
    print(err)

Wrong: 1969-12-31 23:59:59.999999999
cannot convert input 18446744073709551615 with the unit 'ns', at position 0

Issue Description

Trying to convert out of bounds input does not raise for scalar input.

Expected Behavior

Should raise.

Installed Versions

INSTALLED VERSIONS

commit : 0691c5c
python : 3.12.4
python-bits : 64
OS : Linux
OS-release : 5.14.21-150500.55.83-default
Version : #1 SMP PREEMPT_DYNAMIC Wed Oct 2 08:09:07 UTC 2024 (0d53847)
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : de_DE.UTF-8
LOCALE : de_DE.UTF-8

pandas : 2.2.3
numpy : 2.2.1
pytz : 2024.1
dateutil : 2.9.0
pip : 24.0
Cython : None
sphinx : 8.1.0
IPython : 8.25.0
adbc-driver-postgresql: None
adbc-driver-sqlite : None
bs4 : 4.12.3
blosc : None
bottleneck : 1.4.0
dataframe-api-compat : None
fastparquet : None
fsspec : 2024.6.1
html5lib : None
hypothesis : 6.114.1
gcsfs : None
jinja2 : 3.1.4
lxml.etree : 5.2.2
matplotlib : 3.10.0
numba : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
psycopg2 : None
pymysql : None
pyarrow : 17.0.0
pyreadstat : None
pytest : 8.2.2
python-calamine : None
pyxlsb : None
s3fs : 2024.6.1
scipy : 1.14.0
sqlalchemy : None
tables : None
tabulate : 0.9.0
xarray : 2025.1.1.dev3+g251329e3
xlrd : None
xlsxwriter : None
zstandard : 0.22.0
tzdata : 2024.1
qtpy : None
pyqt5 : None

[rhshadrach]

Thanks for the report! Confirmed on main, further investigations and PRs to fix are welcome!

[fynnsu]

I've narrowed down the source of the issue.

First, the reason these two cases behave different is that:

• the scalar uint64_max gets converted to np.array([uint64_max]) (which infers dtype='uint64')
• the list [uint64_max] get converted to np.array([uint64_max], dtype='O')

You can confirm this by passing either of the converted forms directly into pd.to_datetime

Then there is special handling for int/uint/float np.arrays in pandas/core/tools/datetimes.py _to_datetime_with_unit():

        if arg.dtype.kind in "iu":
            # Note we can't do "f" here because that could induce unwanted
            #  rounding GH#14156, GH#20445
            arr = arg.astype(f"datetime64[{unit}]", copy=False) # <---- LINE 1
            try:
                arr = astype_overflowsafe(arr, np.dtype("M8[ns]"), copy=False) # <---- LINE 2 
            except OutOfBoundsDatetime:
                if errors == "raise":
                    raise
                arg = arg.astype(object)
                return _to_datetime_with_unit(arg, unit, name, utc, errors)
            tz_parsed = None

Line 2 correctly handles overflow between different datetime formats (e.g. ns to s) but at this stage Line 1 has already converted from uint64 to datetime64[ns] with uncaught overflow.