BUG: Binary operations (__and__, __or__, etc.) do not take parameter `axis` and fail

[YarShev]

• I have checked that this issue has not already been reported.

• I have confirmed this bug exists on the latest version of pandas.

• (optional) I have confirmed this bug exists on the master branch of pandas.

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Note: Please read this guide detailing how to provide the necessary information for us to reproduce your bug.

Code Sample, a copy-pastable example

# Your code here
import pandas as pd
import numpy as np
df = pd.DataFrame([1, 1])
a = np.array([0, 1])
df.__and__(a, axis=0)
TypeError: new_method() got an unexpected keyword argument 'axis'

Problem description

This worked in pandas 1.1.5

Expected Output

df.__and__(a, axis=0)
   0
0  0
1  1

Output of pd.show_versions()

pandas : 1.2.1
numpy : 1.19.5
pytz : 2020.5
dateutil : 2.8.1
pip : 20.3.3
setuptools : 51.1.2.post20210112
Cython : None
pytest : 6.2.1
hypothesis : None
sphinx : None
blosc : None
feather : 0.4.1
xlsxwriter : None
lxml.etree : 4.6.2
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.12.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : None
fsspec : 0.8.3
fastparquet : None
gcsfs : None
matplotlib : 3.2.2
numexpr : 2.7.2
odfpy : None
openpyxl : 3.0.6
pandas_gbq : None
pyarrow : 2.0.0
pyxlsb : None
s3fs : 0.5.2
scipy : 1.6.0
sqlalchemy : 1.3.22
tables : 3.6.1
tabulate : None
xarray : 0.16.2
xlrd : 2.0.1
xlwt : None
numba : None

[phofl]

first bad commit is 7f1c3b1 cc @jbrockmendel

[simonjayhawkins]

first bad commit is 7f1c3b1 cc @jbrockmendel

#36843

[simonjayhawkins]

It appears this was an intentional change #36796 (comment)

[simonjayhawkins]

@jorisvandenbossche potential discussion item for dev meeting tomorrow.

[jorisvandenbossche]

The dunder methods are not meant to call directly like that, so we shouldn't guarantee any stability here IMO

[jorisvandenbossche]

Alternative solution for when the right array operand is not exactly shape-aligned to avoid the axis:

Reshape (and potentially broadcast) the array so it automatically aligns on the index dimension (axis=0):

In [5]: df & a.reshape(-1, 1)
Out[5]: 
   0
0  0
1  1

You need to do the same when using ufuncs like np.logical_and (those also don't have an axis keyword, and need correctly shaped arrays to ensure shapes are matching / broadcasting is possible).