BUG: `str` dtype does not return pyarrow boolean (`bool[pyarrow]`) output for elementwise checks (equality, etc.)

[mattharrison]

Pandas version checks

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• 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

# Load a string column with pyarrow backend
make = pd.Series(["Alfa Romeo", "Ford", "Subaru"], dtype="str")

# Elementwise equality check
result = make == "Alfa Romeo"
print(result.dtype)  # Expected: boolean[pyarrow], Actual: bool

Issue Description

When using the new str dtype (backed by pyarrow) in pandas 3.x, elementwise operations such as equality checks (==) do not return a Series with pyarrow boolean dtype. Instead, the output is a Series with numpy boolean dtype. This is inconsistent with other pyarrow-backed dtypes, which return pyarrow booleans for such operations.

Expected Behavior

This works if casting to string[pyarrow]

import pandas as pd

# Load a string column with pyarrow backend
make = pd.Series(["Alfa Romeo", "Ford", "Subaru"], dtype="str")

# Elementwise equality check
result = make.astype('string[pyarrow]') == "Alfa Romeo"
print(result.dtype)  # Got: boolean[pyarrow]

Installed Versions

INSTALLED VERSIONS

commit : 1a3230d
python : 3.11.13
python-bits : 64
OS : Darwin
OS-release : 24.6.0
Version : Darwin Kernel Version 24.6.0: Mon Jul 14 11:30:40 PDT 2025; root:xnu-11417.140.69~1/RELEASE_ARM64_T6041
machine : arm64
processor : arm
byteorder : little
LC_ALL : None
LANG : None
LOCALE : en_US.UTF-8

pandas : 3.0.0rc0
numpy : 2.4.0rc1
dateutil : 2.9.0.post0
pip : None
Cython : None
sphinx : None
IPython : 9.8.0
adbc-driver-postgresql: None
adbc-driver-sqlite : None
bs4 : 4.14.3
bottleneck : None
fastparquet : None
fsspec : None
html5lib : None
hypothesis : None
gcsfs : None
jinja2 : 3.1.6
lxml.etree : None
matplotlib : 3.10.7
numba : None
numexpr : None
odfpy : None
openpyxl : None
psycopg2 : None
pymysql : None
pyarrow : 22.0.0
pyiceberg : None
pyreadstat : None
pytest : None
python-calamine : None
pytz : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlsxwriter : None
zstandard : None
qtpy : None
pyqt5 : None

[mattharrison]

Here's another related issue, let me know if I should file a separate bug:

>>> age = pd.Series(['0-10', '11-15', '11-15', '61-65', '46-50'])
>>> age.str.split('-')
0     [0, 10]
1    [11, 15]
2    [11, 15]
3    [61, 65]
4    [46, 50]
dtype: object

if we use the pd.ArrowDtype(pa.string()) type, this returns list<item: string>[pyarrow] instead of object.

[jorisvandenbossche]

This is intentional, because the new str dtype is a default dtype (and agnostic to it being backed by pyarrow, which is not required), while the ArrowDtype dtypes are opt-in dtypes.

The reasoning is that when you use the default dtypes, standard operations should only result in other default dtypes, and you only get the optional (experimental) ArrowDtypes by an explicit action to get those (e.g. by reading with specifying dtype_backend keyword, or with convert_dtypes()).

See the https://pandas.pydata.org/pdeps/0014-string-dtype.html#missing-value-semantics section in the PDEP

[rhshadrach]

Closing for now, can reopen if there is any more discussion @mattharrison.