BUG: "boolean value of NA is ambiguous" when doing iloc on nullable integer series

[MarcoGorelli]

Pandas version checks

• I have checked that this issue has not already been reported.

• I have confirmed this bug exists on the latest version of pandas.

• I have confirmed this bug exists on the main branch of pandas.

Reproducible Example

import pandas as pd

ser = pd.Series([4,6,9,None,10,13,15],index=[6,1,5,0,3,2,4],dtype='Int64')
indices = pd.Series([6,1,5,0,3,2,4],index=[6,1,5,0,3,2,4],dtype='int64')
values = pd.Series([4,6,9,None,10,13,15],index=[4,1,2,6,0,5,3],dtype='Int64')

ser.iloc[indices] = values
print(ser)

Issue Description

Traceback (most recent call last):
  File "/home/marcogorelli/scratch/t.py", line 8, in <module>
    ser.iloc[indices] = values
    ~~~~~~~~^^^^^^^^^
  File "/home/marcogorelli/scratch/.venv/lib/python3.12/site-packages/pandas/core/indexing.py", line 911, in __setitem__
    iloc._setitem_with_indexer(indexer, value, self.name)
  File "/home/marcogorelli/scratch/.venv/lib/python3.12/site-packages/pandas/core/indexing.py", line 1944, in _setitem_with_indexer
    self._setitem_single_block(indexer, value, name)
  File "/home/marcogorelli/scratch/.venv/lib/python3.12/site-packages/pandas/core/indexing.py", line 2218, in _setitem_single_block
    self.obj._mgr = self.obj._mgr.setitem(indexer=indexer, value=value)
                    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/marcogorelli/scratch/.venv/lib/python3.12/site-packages/pandas/core/internals/managers.py", line 415, in setitem
    return self.apply("setitem", indexer=indexer, value=value)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/marcogorelli/scratch/.venv/lib/python3.12/site-packages/pandas/core/internals/managers.py", line 363, in apply
    applied = getattr(b, f)(**kwargs)
              ^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/marcogorelli/scratch/.venv/lib/python3.12/site-packages/pandas/core/internals/blocks.py", line 2129, in setitem
    values[indexer] = value
    ~~~~~~^^^^^^^^^
  File "/home/marcogorelli/scratch/.venv/lib/python3.12/site-packages/pandas/core/arrays/masked.py", line 320, in __setitem__
    value, mask = self._coerce_to_array(value, dtype=self.dtype)
                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/marcogorelli/scratch/.venv/lib/python3.12/site-packages/pandas/core/arrays/numeric.py", line 272, in _coerce_to_array
    values, mask, _, _ = _coerce_to_data_and_mask(
                         ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/marcogorelli/scratch/.venv/lib/python3.12/site-packages/pandas/core/arrays/numeric.py", line 209, in _coerce_to_data_and_mask
    if int(values[idx]) != original[idx]:
       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "pandas/_libs/missing.pyx", line 392, in pandas._libs.missing.NAType.__bool__
TypeError: boolean value of NA is ambiguous

Note that for Int64[pyarrow] or 'int64`, it'"s fine

Expected Behavior

6    <NA>
1       6
5      13
0      10
3      15
2       9
4       4
dtype: Int

Installed Versions

INSTALLED VERSIONS

commit : 4665c10
python : 3.12.5
python-bits : 64
OS : Linux
OS-release : 6.6.87.2-microsoft-standard-WSL2
Version : #1 SMP PREEMPT_DYNAMIC Thu Jun 5 18:30:46 UTC 2025
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : C.UTF-8
LOCALE : C.UTF-8

pandas : 2.3.2
numpy : 2.3.3
pytz : 2025.2
dateutil : 2.9.0.post0
pip : 25.0.1
Cython : None
sphinx : None
IPython : 9.2.0
adbc-driver-postgresql: None
adbc-driver-sqlite : None
bs4 : 4.13.4
blosc : None
bottleneck : None
dataframe-api-compat : None
fastparquet : None
fsspec : 2025.9.0
html5lib : None
hypothesis : None
gcsfs : 2025.2.0
jinja2 : 3.1.6
lxml.etree : None
matplotlib : 3.10.3
numba : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
psycopg2 : None
pymysql : None
pyarrow : 21.0.0
pyreadstat : None
pytest : 8.3.3
python-calamine : None
pyxlsb : None
s3fs : None
scipy : 1.16.0
sqlalchemy : None
tables : None
tabulate : 0.9.0
xarray : None
xlrd : None
xlsxwriter : None
zstandard : None
tzdata : 2025.2
qtpy : None
pyqt5 : None

[athithiyarajk]

The check: if int(values[idx]) != original[idx] is the problem.

What's Happening

When a value in the pandas Series is missing (like a None value), it's treated as an internal pandas._libs.missing.NAType.

When you compare anything to that missing value (value != NAType), the comparison itself returns NAType, not a standard Python True or False.

The Python if statement then tries to evaluate this NAType as a boolean, which it can't do, throwing the exception.

The Proposed Solution

My proposal is to check the result of the comparison right before the if statement runs.

If the result is an object of type pandas._libs.missing.NAType, we override it and assign np.False_ instead.

This way, the if condition always receives a valid boolean. Crucially, assigning False handles the missing value case correctly: we assume a missing value is not a difference, and the if block is skipped.

I'll proceed with implementing a focused check for NAType to patch this up if this sounds good, please assign this issue to me.