Pandas BUG: pd.Series.isin returns inconsistent results for a passed iterable depending on the dtype

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Reproducible Example

import pandas as pd

df = pd.DataFrame({"int": [1, 2, 3], "float": [1.0, 2.0, 3.0]})
filter_ = [1, 2]

print('## Good')
print(df["int"].isin([1, 2]))
print(df["float"].isin([1, 2]))
print(df["int"].isin(i for i in [1.0, 2.0]))
print('## Bad')
print(df["float"].isin(i for i in [1.0, 2.0]))

Issue Description

Passing an iterator to pd.Series.isin does not seem to work reliably. Depending on the dtype (I checked int and float`), the behavior differs.

The int dtype filters as I would expect (even when passing floats) while the float dtype always returns False.

I was running into this issue in production where all but one column were ints.

Expected Behavior

The output is always

0     True
1     True
2    False

Or at least consistent independent of the type.

Instead the last case returns

0    False
1    False
2    False

Installed Versions

INSTALLED VERSIONS ------------------ commit : 8dab54d6573f7186ff0c3b6364d5e4dd635ff3e7 python : 3.10.5.final.0 python-bits : 64 OS : Darwin OS-release : 22.1.0 Version : Darwin Kernel Version 22.1.0: Sun Oct 9 20:14:54 PDT 2022; root:xnu-8792.41.9~2/RELEASE_X86_64 machine : x86_64 processor : i386 byteorder : little LC_ALL : None LANG : None LOCALE : None.UTF-8 pandas : 1.5.2 numpy : 1.23.5 pytz : 2022.6 dateutil : 2.8.2 setuptools : 65.6.3 pip : 22.3.1 Cython : None pytest : 7.2.0 hypothesis : None sphinx : None blosc : None feather : None xlsxwriter : None lxml.etree : 4.9.1 html5lib : 1.1 pymysql : None psycopg2 : 2.9.5 jinja2 : 3.1.2 IPython : 8.6.0 pandas_datareader: None bs4 : 4.11.1 bottleneck : None brotli : 1.0.9 fastparquet : None fsspec : 2022.11.0 gcsfs : 2022.11.0 matplotlib : 3.6.2 numba : None numexpr : 2.8.4 odfpy : None openpyxl : 3.0.10 pandas_gbq : None pyarrow : 10.0.1 pyreadstat : None pyxlsb : None s3fs : None scipy : 1.9.3 snappy : None sqlalchemy : 1.4.44 tables : None tabulate : 0.9.0 xarray : 0.21.1 xlrd : 2.0.1 xlwt : None zstandard : None tzdata : None

Comment From: phofl

Fixed on main, but needs tests

Comment From: luke396

import pandas as pd

df = pd.DataFrame({"int": [1, 2, 3], "float": [1.0, 2.0, 3.0]})
filter_ = [1, 2]

print('## Good')
print(df["int"].isin([1, 2]))
print(df["float"].isin([1, 2]))
print(df["int"].isin(i for i in [1.0, 2.0]))
print('## Bad')
print(df["float"].isin(i for i in [1.0, 2.0]))

I try it in pandas-dev (from here), the example works well.

But in another environment, pandas == 1.5.2 it works bad, just like the issues dercribe.