BUG: Inconsistent behavior with bitwise operations on Series with np.array vs. list

[Dr-Irv]

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

In [1]: import pandas as pd

In [2]: import numpy as np

In [3]: pd.__version__
Out[3]: '2.1.0.dev0+5.g8d2a4e11d1'

In [4]: s = pd.Series([1,2,3])

In [5]: s & 1
Out[5]:
0    1
1    0
2    1
dtype: int64

In [6]: s & np.array([1,2,3])
Out[6]:
0    1
1    2
2    3
dtype: int64

In [7]: s & [1, 2, 3]
Out[7]:
0    True
1    True
2    True
dtype: bool

In [8]: np.array([1,2,3]) & [1,2,3]
Out[8]: array([1, 2, 3])

Issue Description

If you have a Series of integers, and you do a bitwise operation using &, | or ^ ("and", "or", or "xor"), where the other argument is a list, you get different results than if the argument is a numpy array.

Note in the example above how s & np.array([1,2,3]) produces a Series of integers, while s & [1,2,3] produces a Series of bool

Also, note that numpy produces an array of integers, not bools.

Expected Behavior

The behavior should be consistent whether the operand is a numpy array or a list of integers.

Installed Versions

INSTALLED VERSIONS

commit : 8d2a4e1
python : 3.8.16.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19045
machine : AMD64
processor : Intel64 Family 6 Model 158 Stepping 13, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : English_United States.1252

pandas : 2.1.0.dev0+5.g8d2a4e11d1
numpy : 1.23.5
pytz : 2022.7.1
dateutil : 2.8.2
setuptools : 66.1.1
pip : 23.0
Cython : 0.29.32
pytest : 7.2.1
hypothesis : 6.68.1
sphinx : 4.5.0
blosc : None
feather : None
xlsxwriter : 3.0.8
lxml.etree : 4.9.2
html5lib : 1.1
pymysql : 1.0.2
psycopg2 : 2.9.3
jinja2 : 3.1.2
IPython : 8.10.0
pandas_datareader: None
bs4 : 4.11.2
bottleneck : 1.3.6
brotli :
fastparquet : 2023.2.0
fsspec : 2023.1.0
gcsfs : 2023.1.0
matplotlib : 3.6.3
numba : 0.56.4
numexpr : 2.8.3
odfpy : None
openpyxl : 3.1.0
pandas_gbq : None
pyarrow : 10.0.1
pyreadstat : 1.2.0
pyxlsb : 1.0.10
s3fs : 2023.1.0
scipy : 1.10.0
snappy :
sqlalchemy : 2.0.3
tables : 3.7.0
tabulate : 0.9.0
xarray : 2023.1.0
xlrd : 2.0.1
zstandard : 0.19.0
tzdata : None
qtpy : None
pyqt5 : None

[jbrockmendel]

yah this is weird. let's deprecate the heck out of it