Interestingly this seems to work fine until version 1.3.5. I wouldn't mind taking a better look into what changed between 1.3.5 and 1.4.0 to cause this behaviour if no one has any immediate ideas.

@AKuederle
This behaviour can be reproduced on the main branch of pandas.

df.loc[("A", ["a", "b"]), :] 

and

df.loc[indexer_tuple(first="A", second=["a", "b"]), :]

will start to diverge here:

This method explicitly checks if key is a list-like indexer that is not a NamedTuple. This is the implementation:

This causes the former to move to "nested tuple slicing", while the latter moves to "straight lookup". However, straight lookup fails, as Indexer(first='A', second=['a', 'b']) is clearly not a label present in the axis.

So, my question is, why are NamedTuples not allowed to be used in anything other than straight lookups? Perhaps it's simply an oversight, or perhaps there's some reason that I can't think of.
Changing is_list_like_indexer to is_list_like will cause all of your test cases to work perfectly.

@jmoro0408

Interestingly this seems to work fine until version 1.3.5. I wouldn't mind taking a better look into what changed between 1.3.5 and 1.4.0 to cause this behaviour if no one has any immediate ideas.

In version v1.3.5, the following line still exists just before the aforementioned is_list_like_indexer:

This method was deprecated in #42476, and this line was simply removed, which caused the bug.

A similar bug exists with iloc and MultiIndex:

import pandas as pd
import numpy as np

# A df with two level MultiIndex
df = pd.DataFrame(np.arange(120).reshape(6, -1), index=pd.MultiIndex.from_product([["A", "B"], ["a", "b", "c"]], names=["first", "second"]))

# Indexing with a normal tuple works as expected:
# normal subset
print(df.iloc[1, 2])  # <- Works
# complicated subset
print(df.iloc[(1, [2, 3])])  # <- Works

# Now the same with a named tuple
from collections import namedtuple

indexer_tuple = namedtuple("Indexer", df.index.names)

print(df.iloc[tuple(indexer_tuple(first=1, second=[2, 3]))]) # <- Works
print(df.iloc[indexer_tuple(first=1, second=[2, 3])])  # <- DOES NOT WORK!

22
2    22
3    23
Name: (A, b), dtype: int64
2    22
3    23
Name: (A, b), dtype: int64
Traceback (most recent call last):
  File "/workspaces/pandas/demo.py", line 30, in <module>
  File "/workspaces/pandas/pandas/core/indexing.py", line 1074, in __getitem__
    return self._getitem_axis(maybe_callable, axis=axis)
  File "/workspaces/pandas/pandas/core/indexing.py", line 1620, in _getitem_axis
    raise TypeError("Cannot index by location index with a non-integer key")
TypeError: Cannot index by location index with a non-integer key

This again originates from a _getitem_axis method, but now on _iLocIndexer instead of _LocIndexer.

There might be more cases where NamedTuples are not supported. Perhaps a better fix is to convert all NamedTuples to regular tuples at the start of a .loc, .iloc etc. call.

Converting is not an option, NamedTuples are valid Index Elements. Hence this could be a label based lookup.

is the iloc case also a regression?

You're right, initial conversion would not work then.

I believe the iloc case was always broken; I know it is broken on main, v1.4.2 and v1.2.0.

Let’s open a different issue then with a broader discussion. I am a bit hesitant here, since I am not a fan of value dependent behavior.

the loc case worked accidentally on the 1.3.x branch

the loc case worked accidentally on the 1.3.x branch

Indeed.

I'll open one

Iloc is probably easy, because it’s always positional, loc and regular getitem is tricky

This method was deprecated in #42476, and this line was simply removed, which caused the bug.

indeed. from bisect, first bad commit: [9de158f] BUG: string slicing on MultiIndex DatetimeIndex level (#42476)