BUG: "IndexingError: Too many indexers" when accessing a None value using .loc through a MultiIndex

Steps to reproduce

print(pd.Series(
    [None],
    pd.MultiIndex.from_arrays([['Level1'], ['Level2']]))
    .loc[('Level1', 'Level2')])

Expected output

None

Actual output

/usr/local/lib/python3.6/dist-packages/pandas/core/indexing.py in __getitem__(self, key)
   1760                 except (KeyError, IndexError, AttributeError):
   1761                     pass
-> 1762             return self._getitem_tuple(key)
   1763         else:
   1764             # we by definition only have the 0th axis

/usr/local/lib/python3.6/dist-packages/pandas/core/indexing.py in _getitem_tuple(self, tup)
   1275 
   1276         # no multi-index, so validate all of the indexers
-> 1277         self._has_valid_tuple(tup)
   1278 
   1279         # ugly hack for GH #836

/usr/local/lib/python3.6/dist-packages/pandas/core/indexing.py in _has_valid_tuple(self, key)
    699         for i, k in enumerate(key):
    700             if i >= self.ndim:
--> 701                 raise IndexingError("Too many indexers")
    702             try:
    703                 self._validate_key(k, i)

IndexingError: Too many indexers

Additional information

If any value other than None (even np.nan) is used, the code behaves correctly.

If a single index level is used, the code behaves correctly.

Workaround

Seems to work if .loc(axis=0)[('Level1', 'Level2')] is used instead.

Output of pd.show_versions()

INSTALLED VERSIONS

commit : None python : 3.8.3.candidate.1 python-bits : 64 OS : Linux OS-release : 5.6.0-1-amd64 machine : x86_64 processor : byteorder : little LC_ALL : None LANG : en_GB.UTF-8 LOCALE : en_GB.UTF-8

pandas : 1.0.3 numpy : 1.18.2 pytz : 2019.3 dateutil : 2.8.1 pip : 20.0.2 setuptools : 46.1.3 Cython : None pytest : 4.6.9 hypothesis : None sphinx : None blosc : None feather : None xlsxwriter : None lxml.etree : 4.5.0 html5lib : None pymysql : None psycopg2 : None jinja2 : 2.11.1 IPython : 7.13.0 pandas_datareader: None bs4 : None bottleneck : None fastparquet : None gcsfs : None lxml.etree : 4.5.0 matplotlib : None numexpr : None odfpy : None openpyxl : None pandas_gbq : None pyarrow : None pytables : None pytest : 4.6.9 pyxlsb : None s3fs : None scipy : 1.4.1 sqlalchemy : None tables : None tabulate : None xarray : None xlrd : None xlwt : None xlsxwriter : None numba : None

[jorisvandenbossche]

The problem, I think, is that False in principle can also be a valid result (so similar problem as with None).

So I think we need another way: either by raising an error and catching that in the layer above, or either with a custom object like no_result = object() and then if result != no_result: return result

[jorisvandenbossche]

@pedrooa Sure, that would be very welcome!