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Showing content from https://github.com/pandas-dev/pandas/issues/49021 below:

comparing pd.Timedelta with timedelta.max fails · Issue #49021 · pandas-dev/pandas · GitHub

• 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.

import pandas as pd
from datetime import timedelta
pd.Timedelta("1s") < pd.Timedelta.max  # succeeds, returns True
pd.Timedelta("1s") < timedelta.max  # fails, raises OverflowError

It looks like timedelta.max is too large to be represented as a pd.Timedelta.

pandas._libs.tslibs.np_datetime.OutOfBoundsTimedelta: Python int too large to convert to C long

As a workaround, I convert the pandas Timedelta to a datetime.timedelta instead:

pd.Timedelta("1s").to_pytimedelta() < timedelta.max  # succeeds, returns True

The issue also occurs when comparing a pd.Timedelta to timedelta.min.

In case this could be easily addressed in a fix, I'd be happy to work on it. It looks like the relevant part of the Pandas codebase is timedeltas.pyx, which already has a # TODO: watch out for overflows. One idea would be to except the OverflowError and do the comparison with datetime.timedelta objects (by converting self.value to a datetime.timedelta rather than ots.value to a pd.Timedelta). I believe this would imply losing the nanosecond precision (but keeping microsecond precision) when comparing pd.Timedelta values to datetime.timedelta values larger than pd.Timedelta.max.

In case it is not easily fixed, perhaps we could consider what a useful addition would be to the documentation of pd.Timedelta and/or pd.Timedelta.max.

commit : 87cfe4e
python : 3.9.9.final.0
python-bits : 64
OS : Linux
OS-release : 5.15.11-76051511-generic
Version : #202112220937 164018548121.04~b3a2c21-Ubuntu SMP Mon Jan 3 16:5
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.5.0
numpy : 1.21.5
pytz : 2021.3
dateutil : 2.8.2
setuptools : 62.0.0
pip : 22.2.2
Cython : None
pytest : 6.2.5
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : 1.1
pymysql : None
psycopg2 : 2.9.2
jinja2 : 3.0.3
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
brotli : None
fastparquet : None
fsspec : None
gcsfs : None
matplotlib : None
numba : 0.55.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyreadstat : None
pyxlsb : None
s3fs : None
scipy : 1.7.3
snappy : None
sqlalchemy : 1.4.28
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
zstandard : None
tzdata : None

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