BUG: isin check with numpy array works incorrectly when using UInt64 dtype

[qltwis]

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Reproducible Example

import pandas as pd
import numpy as np

pd.Series([635554097106142143],dtype="UInt64").isin(np.array([635554097106142079]))

Issue Description

The isin check returns True, although clearly 635554097106142143 ≠ 635554097106142079

Presumably during the check the values are converted to a dtype with smaller precision.

Expected Behavior

Not using the UInt64 dtype as well as not checking against a numpy array produce the expected result

I.e. both

pd.Series([635554097106142143],dtype="int64").isin(np.array([635554097106142079]))

and

pd.Series([635554097106142143],dtype="UInt64").isin([635554097106142079])

Evaluate to False

Installed Versions

NSTALLED VERSIONS

commit : d9cdd2e
python : 3.12.5.final.0
python-bits : 64
OS : Linux
OS-release : 5.19.0-46-generic
Version : #47~22.04.1-Ubuntu SMP PREEMPT_DYNAMIC Wed Jun 21 15:35:31 UTC 2
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8

pandas : 2.2.2
numpy : 2.0.0
pytz : 2023.3.post1
dateutil : 2.8.2
setuptools : 69.0.3
pip : 24.0
Cython : None
pytest : 7.4.3
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 5.2.2
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.1.2
IPython : 8.18.1
pandas_datareader : None
adbc-driver-postgresql: None
adbc-driver-sqlite : None
bs4 : 4.12.3
bottleneck : None
dataframe-api-compat : None
fastparquet : None
fsspec : 2023.10.0
gcsfs : None
matplotlib : 3.9.1
numba : 0.60.0
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 17.0.0
pyreadstat : None
python-calamine : None
pyxlsb : None
s3fs : None
scipy : 1.14.0
sqlalchemy : None
tables : None
tabulate : None
xarray : 2024.1.1
xlrd : None
zstandard : None
tzdata : 2023.3
qtpy : None
pyqt5 : None

[rhshadrach]

Thanks for the report. Note all numbers here are within int64 limits, and NumPy creates the array here as int64. Also you get the expected result by specifying dtype="uint64" in the NumPy array construction. My guess is pandas is converting to floats to do the comparison.

When given an array of int64 to test against, we need to handle the difference in dtypes (uint64 vs int64) and the fact that int64 could hold negatives. Perhaps we could strip out the negative values, then convert everything to uint64? I worry this would become a complex operation.

Further investigations and suggestions are welcome.