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Reproducible Example

import pandas as pd
s=pd.Series([1,2,3,4,5])
s
t=s[-1::-1]
t
s + t
s <= t

Issue Description

Code above produces

>>> import pandas as pd
>>> s=pd.Series([1,2,3,4,5])
>>> s
0    1
1    2
2    3
3    4
4    5
dtype: int64
>>> t=s[-1::-1]
>>> t
4    5
3    4
2    3
1    2
0    1
dtype: int64
>>> s + t
0     2
1     4
2     6
3     8
4    10
dtype: int64
>>> s <= t
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "C:\Anaconda3\envs\pandasstubs\lib\site-packages\pandas\core\ops\common.py", line 70, in new_method
    return method(self, other)
  File "C:\Anaconda3\envs\pandasstubs\lib\site-packages\pandas\core\arraylike.py", line 52, in __le__
    return self._cmp_method(other, operator.le)
  File "C:\Anaconda3\envs\pandasstubs\lib\site-packages\pandas\core\series.py", line 5617, in _cmp_method
    raise ValueError("Can only compare identically-labeled Series objects")
ValueError: Can only compare identically-labeled Series objects

Expected Behavior

s<=t should produce

0    True
1    True
2    True
3    True
4    True
dtype: bool

Question is why we align the indices in arithmetic, but not in a logical operation. The two series are identically labeled, but just not in the same order, so the error message is misleading.

Installed Versions

INSTALLED VERSIONS ------------------ commit : f4ca4d3d0ea6a907262f8c842c691115b13d4cb7 python : 3.8.12.final.0 python-bits : 64 OS : Windows OS-release : 10 Version : 10.0.19043 machine : AMD64 processor : Intel64 Family 6 Model 158 Stepping 13, GenuineIntel byteorder : little LC_ALL : None LANG : None LOCALE : English_United States.1252 pandas : 1.5.0.dev0+1050.gf4ca4d3d0e numpy : 1.22.4 pytz : 2022.1 dateutil : 2.8.2 setuptools : 60.9.3 pip : 22.0.3 Cython : 0.29.30 pytest : 7.1.2 hypothesis : 6.46.11 sphinx : 4.3.2 blosc : None feather : None xlsxwriter : 3.0.3 lxml.etree : 4.8.0 html5lib : 1.1 pymysql : None psycopg2 : None jinja2 : 3.1.2 IPython : 8.4.0 pandas_datareader: None bs4 : 4.11.1 bottleneck : 1.3.4 brotli : fastparquet : 0.8.1 fsspec : 2021.11.0 gcsfs : 2021.11.0 matplotlib : 3.5.1 numba : 0.53.1 numexpr : 2.8.0 odfpy : None openpyxl : 3.0.9 pandas_gbq : None pyarrow : 7.0.0 pyreadstat : 1.1.7 pyxlsb : None s3fs : 2021.11.0 scipy : 1.8.1 snappy : sqlalchemy : 1.4.37 tables : 3.7.0 tabulate : 0.8.9 xarray : 2022.3.0 xlrd : 2.0.1 xlwt : 1.3.0 zstandard : None

Comment From: CloseChoice

Please note that 

s.le(t)
#0    True
#1    True
#2    True
#3    True
#4    True
#dtype: bool

works fine and gives the expected result. I would naively think that logical comparisons match the behaviour of the overloaded double-underscore methods. The documentation also states this equivalence:

Equivalent to series <= other, but with support to substitute a fill_value for missing data in either one of the inputs.

Edit: This is no regression, at least until v1.1.0

Edit2: Also note that the exact same behaviour appears for <, >, >=, ==, != while their counterparts .lt, .gt, .ge, .eq, .ne all work fine.

Comment From: Dr-Irv

Broadcasting also doesn't work for comparisons and is inconsistent as well:

>>> s = pd.Series([1,2,3,4], index=pd.MultiIndex.from_product([["a", "b"], ["x", "y"]], names=["ab", "xy"]))
>>> s
ab  xy
a   x     1
    y     2
b   x     3
    y     4
dtype: int64
>>> t = pd.Series([0, 5], index=pd.Index(["a", "b"], name="ab"))
>>> t
ab
a    0
b    5
dtype: int64
>>> t < s
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "C:\Anaconda3\envs\pandasstubs\lib\site-packages\pandas\core\ops\common.py", line 70, in new_method
    return method(self, other)
  File "C:\Anaconda3\envs\pandasstubs\lib\site-packages\pandas\core\arraylike.py", line 48, in __lt__
    return self._cmp_method(other, operator.lt)
  File "C:\Anaconda3\envs\pandasstubs\lib\site-packages\pandas\core\series.py", line 5617, in _cmp_method
    raise ValueError("Can only compare identically-labeled Series objects")
ValueError: Can only compare identically-labeled Series objects
>>> t.lt(s)
ab  xy
a   x      True
    y      True
b   x     False
    y     False
dtype: bool

Comment From: simonjayhawkins

somewhat related https://github.com/pandas-dev/pandas/issues/36941#issuecomment-705004487 and https://github.com/pandas-dev/pandas/issues/20442#issuecomment-375116149

it maybe that we have tests for the None case that break when sharing code.

since the ValueError("Can only compare identically-labeled Series objects") is explicitly raised, the issue in the OP could be considered not a bug but an enhancement request.

so we could add more logic to do the alignment in the existing code (-1). Or share the working code so that it is consistent (+1).

If the latter, I think can probably close as a duplicate.

Comment From: Dr-Irv

so we could add more logic to do the alignment in the existing code (-1). Or share the working code so that it is consistent (+1).

I think we should do the alignment. As shown in https://github.com/pandas-dev/pandas/issues/47554#issuecomment-1171592691 , the difference in behavior between Series.le() and Series.__le__() seems inconsistent

Comment From: kathryn1229

take