BUG: Trying to add an object with __iter__ but no __len__ using .loc[row, col] fails

[mgunyho]

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Reproducible Example

import pandas as pd

class MyWeirdObject:
    def __iter__(self):
        raise TypeError("actually not iterable!")

# this is OK
df = pd.DataFrame({
    "foo": [MyWeirdObject()],
    "bar": [123],
})

# this works OK
df.loc[1] = [MyWeirdObject(), 456]

# this fails
df.loc[2, "foo"] = MyWeirdObject()

Issue Description

Assigning an object with .loc[row, col] = ... fails with TypeError: object of type ... has no len() when the object implements __iter__ but not __len__.

The exception is raised here:

pandas/pandas/core/indexing.py

Line 2005 in 1b5b02c

elif len(ilocs) == 1 and lplane_indexer == len(value) and not is_scalar(pi):

The root cause is that is_list_like_indexer calls is_list_like which checks if the object implements __iter__ but not __len__.

In general, iterators may not have a length defined, but for me, this came up when I was using TypedUnits:

from tunits.units import MHz
df.loc[3, "foo"] = 1 * MHz  # raises exception

TypedUnits values with units implement __iter__ which just raises an exception, which is due to a quirk in Python - if __iter__ is not defined, iter(obj) will instead do __getitem__(0), __getitem__(1), ... until IndexError is raised, but here the library authors want to avoid the call to __getitem__.

(While browsing around, I found at least

pandas/pandas/core/indexing.py

Line 2305 in 1b5b02c

if is_list_like_indexer(value):

where similarly len() is called after checking is_list_like.

Expected Behavior

Assignment should not fail with object of type ... has no len(). In my case, it should be treated as a scalar value, although I'm not sure about general iterators with no length defined.

Installed Versions

INSTALLED VERSIONS

commit : 9c8bc3e
python : 3.13.5
python-bits : 64
OS : Linux
OS-release : 6.17.7-200.fc42.x86_64
Version : #1 SMP PREEMPT_DYNAMIC Sun Nov 2 17:43:34 UTC 2025
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8

pandas : 2.3.3
numpy : 2.3.5
pytz : 2025.2
dateutil : 2.9.0.post0
pip : None
Cython : 3.2.1
sphinx : None
IPython : 9.7.0
adbc-driver-postgresql: None
adbc-driver-sqlite : None
bs4 : None
blosc : None
bottleneck : None
dataframe-api-compat : None
fastparquet : None
fsspec : None
html5lib : None
hypothesis : None
gcsfs : None
jinja2 : None
lxml.etree : None
matplotlib : None
numba : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
psycopg2 : None
pymysql : None
pyarrow : None
pyreadstat : None
pytest : None
python-calamine : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlsxwriter : None
zstandard : None
tzdata : 2025.2
qtpy : None
pyqt5 : None

[rhshadrach]

I think the primary object we identify as list-like that doesn't have a length is a generator.

from pandas.core.dtypes.common import is_list_like

gen = (e for e in [1, 2, 3])
print(is_list_like(gen))
# True

I'd be quite hesitant on changing this behavior without a solid use-case of "yea, this should work". Can you give any more details about your object?

[mgunyho]

As I mentioned, my original issue was with adding objects from the TypedUnits library to an existing dataframe. To me it sounds reasonable that is_list_like() returns True even for things with no length (athough the naming is a bit vague), but there should be an additional or different check before calling len().