Using python comparison operators when SparseArrays of uneven length raises uninformative error

[dburkhardt]

Code Sample, a copy-pastable example if possible

I first discovered this issue attempting a comparison of the following form

Case 1

>>> import pandas as pd
>>> import numpy as np

>>> a = pd.Series(pd.SparseArray(np.arange(10)))
>>> b = pd.Series(np.arange(11))
>>> (a == 5) & (b == 5)

which raises the following uninformative AttirbuteError

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-78-bb3d3268cfcc> in <module>
----> 1 (a == 5) & (b == 5)

/usr/lib/python3.8/site-packages/pandas/core/ops/__init__.py in wrapper(self, other)
   1319         #   integer dtypes.  Otherwise these are boolean ops
   1320         filler = fill_int if is_self_int_dtype and is_other_int_dtype else fill_bool
-> 1321         res_values = na_op(self.values, ovalues)
   1322         unfilled = self._constructor(res_values, index=self.index, name=res_name)
   1323         filled = filler(unfilled)

/usr/lib/python3.8/site-packages/pandas/core/ops/__init__.py in na_op(x, y)
   1252     def na_op(x, y):
   1253         try:
-> 1254             result = op(x, y)
   1255         except TypeError:
   1256             assert not isinstance(y, (list, ABCSeries, ABCIndexClass))

/usr/lib/python3.8/site-packages/pandas/core/arrays/sparse.py in cmp_method(self, other)
   1821 
   1822             if isinstance(other, SparseArray):
-> 1823                 return _sparse_array_op(self, other, op, op_name)
   1824             else:
   1825                 with np.errstate(all="ignore"):

/usr/lib/python3.8/site-packages/pandas/core/arrays/sparse.py in _sparse_array_op(left, right, op, name)
    493             right_sp_values = right.sp_values
    494 
--> 495         sparse_op = getattr(splib, opname)
    496 
    497         with np.errstate(all="ignore"):

AttributeError: module 'pandas._libs.sparse' has no attribute 'sparse_and_object'

Note, if a and b are of the same length, this code runs fine:
Case 2

a = pd.Series(pd.SparseArray(np.arange(10)))
b = pd.Series(np.arange(10))

(a == 5) & (b == 5)

returns

0    False
1    False
2    False
3    False
4    False
5     True
6    False
7    False
8    False
9    False
dtype: Sparse[bool, False]

and if they are both non-sparse, the code evaluates fine.
Case 3

a = pd.Series(np.arange(10))
b = pd.Series(np.arange(11))

(a == 5) & (b == 5)

0     False
1     False
2     False
3     False
4     False
5      True
6     False
7     False
8     False
9     False
10    False
dtype: bool

Expected Output

I expect one of two behaviors:

1. An error message that states the two arrays must be of even length in Case 1
2. The code in Case 1 to return the same output as in Case 3

Output of pd.show_versions()

[paste the output of pd.show_versions() here below this line]

INSTALLED VERSIONS

commit : None
python : 3.8.1.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.7-arch1-1
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8

pandas : 0.25.3
numpy : 1.18.0
pytz : 2019.3
dateutil : 2.8.1
pip : 19.2.3
setuptools : 42.0.2
Cython : 0.29.14
pytest : 5.3.2
hypothesis : 4.54.2
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.4.2
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.11.1
pandas_datareader: 0.8.1
bs4 : 4.8.2
bottleneck : 1.3.1
fastparquet : None
gcsfs : None
lxml.etree : 4.4.2
matplotlib : 3.0.3
numexpr : 2.7.0
odfpy : None
openpyxl : 3.0.1
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.12
tables : 3.6.1
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : None

[dburkhardt]

Also I should note, I would be happy to help submit a bugfix here. I haven't contributed to pandas before, and I'm not super familiar with the SpareArray code (which I understand is under active development). If someone can tell me what the correct behavior should be here and point me in the right direction to fix it, I'm happy to help out. Not a problem if it's better fixed by one of the core devs though.