BUG: StringArray with NA behaves different to numpy object array for elementwise comparisons #62802
Description
Pandas version checks
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I have confirmed this bug exists on the latest version of pandas.
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I have confirmed this bug exists on the main branch of pandas.
Reproducible Example
This is for pandas '2.1.1', the latest version raises an error instead.
>>> import pandas as pd >>> import numpy >>> str_arr = pd.array(['a', 'b', 'c', 'foo', 'd', 'bar', None], dtype="string") >>> str_arr <StringArray> ['a', 'b', 'c', 'foo', 'd', 'bar', <NA>] Length: 7, dtype: string >>> b2 = np.asarray(str_arr) >>> b2 array(['a', 'b', 'c', 'foo', 'd', 'bar', <NA>], dtype=object) >>> b2 == a_ext <stdin>:1: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison FalseI'd like an elementwise comparison and not a scalar return value.
Issue Description
When I use a string array with NA values to perform elementwise comparison, the result is a scalar instead of an elementwise matrix.
Here's how this used to work when I used numpy object dtype:
>>> import numpy as np >>> >>> a = np.asarray(['foo', 'bar', 'baz']) >>> b = np.asarray(['a', 'b', 'c', 'foo', 'd', 'bar', None]) >>> b array(['a', 'b', 'c', 'foo', 'd', 'bar', None], dtype=object) >>> >>> # Find matches of `b` in `a` >>> a_ext = a[:, np.newaxis] >>> b == a_ext array([[False, False, False, True, False, False, False], [False, False, False, False, False, True, False], [False, False, False, False, False, False, False]])But when I use a pandas string type as a source for b (rather than object dtype), the result is a scalar boolean (in 2.1.1) or a TypeError (in 2.3.3).
>>> import pandas as pd >>> >>> str_arr = pd.array(['a', 'b', 'c', 'foo', 'd', 'bar', None], dtype="string") >>> str_arr <StringArray> ['a', 'b', 'c', 'foo', 'd', 'bar', <NA>] Length: 7, dtype: string >>> b2 = np.asarray(str_arr) >>> b2 array(['a', 'b', 'c', 'foo', 'd', 'bar', <NA>], dtype=object) >>> pd.__version__ '2.1.1' >>> np.__version__ '1.24.3' >>> b2 == a_ext <stdin>:1: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison False or >>> pd.__version__ '2.3.3' >>> np.__version__ '2.3.4' >>> b2 == a_ext Traceback (most recent call last): File "<python-input-8>", line 8, in <module> b2 == a_ext File "pandas/_libs/missing.pyx", line 392, in pandas._libs.missing.NAType.__bool__ TypeError: boolean value of NA is ambiguousWorkaround: replace NA with None in b2:
>>> b2[pd.isnull(b2)] = None >>> b2 == a_ext array([[False, False, False, True, False, False, False], [False, False, False, False, False, True, False], [False, False, False, False, False, False, False]]) Expected Behavior
An array is returned.
Installed Versions
pd.show_versions()
INSTALLED VERSIONS
commit : e86ed37
python : 3.11.14.final.0
python-bits : 64
OS : Linux
OS-release : 6.16.12-200.fc42.x86_64
Version : #1 SMP PREEMPT_DYNAMIC Sun Oct 12 16:31:16 UTC 2025
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : en_AU.UTF-8
LOCALE : en_AU.UTF-8
pandas : 2.1.1
numpy : 1.24.3
pytz : 2020.4
dateutil : 2.8.2
setuptools : 69.2.0
pip : 24.2
Cython : 0.29.34
pytest : 7.3.1
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : 0.9.6
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : 2.9.6
jinja2 : 2.11.2
IPython : None
pandas_datareader : None
bs4 : None
bottleneck : 1.3.5
dataframe-api-compat: None
fastparquet : None
fsspec : None
gcsfs : None
matplotlib : None
numba : None
numexpr : 2.8.4
odfpy : None
openpyxl : 3.1.3
pandas_gbq : None
pyarrow : 11.0.0
pyreadstat : None
pyxlsb : None
s3fs : None
scipy : 1.10.1
sqlalchemy : 1.3.23
tables : 3.8.0
tabulate : None
xarray : None
xlrd : 2.0.1
zstandard : None
tzdata : 2025.2
qtpy : None
pyqt5 : None