BUG: ArrowDtype Series: Broadcasting Error with Single-Value Arrays in Arithmetic Operations

[hombit]

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 numpy as np
import pandas as pd
import pyarrow as pa

series = pd.Series([1.0, 2.0, 3.0], dtype=pd.ArrowDtype(pa.float64()))
series / np.array([2.0])
# ArrowInvalid: Array arguments must all be the same length

Issue Description

When performing arithmetic operations between a Series of ArrowDtype and a single-value array, the array is not broadcasted to match the series shape. Instead, it raises an ArrowInvalid error. This behavior is inconsistent, as the same operation works correctly with both default and numpy-nullable dtypes.

A solution could be casting a single-element array to a pa.Scalar in ArrowExtensionArray._evaluate_op_method:

pandas/pandas/core/arrays/arrow/array.py

Line 755 in 2a10e04

def _evaluate_op_method(self, other, op, arrow_funcs) -> Self:

Expected Behavior

The code doesn't fail, and behavior to be consistent over different dtype back-ends

Installed Versions

Pandas v2.2.3:

INSTALLED VERSIONS
------------------
commit                : 0691c5cf90477d3503834d983f69350f250a6ff7
python                : 3.12.7
python-bits           : 64
OS                    : Darwin
OS-release            : 24.0.0
Version               : Darwin Kernel Version 24.0.0: Tue Sep 24 23:37:36 PDT 2024; root:xnu-11215.1.12~1/RELEASE_ARM64_T6020
machine               : arm64
processor             : arm
byteorder             : little
LC_ALL                : en_US.UTF-8
LANG                  : en_US.UTF-8
LOCALE                : en_US.UTF-8
pandas                : 2.2.3
numpy                 : 1.26.4
pytz                  : 2024.1
dateutil              : 2.9.0.post0
pip                   : 24.2
Cython                : None
sphinx                : 7.4.7
IPython               : 8.26.0
adbc-driver-postgresql: None
adbc-driver-sqlite    : None
bs4                   : 4.12.3
blosc                 : None
bottleneck            : None
dataframe-api-compat  : None
fastparquet           : None
fsspec                : 2024.6.1
html5lib              : None
hypothesis            : None
gcsfs                 : None
jinja2                : 3.1.4
lxml.etree            : None
matplotlib            : 3.8.4
numba                 : 0.60.0
numexpr               : None
odfpy                 : None
openpyxl              : None
pandas_gbq            : None
psycopg2              : None
pymysql               : None
pyarrow               : 16.1.0
pyreadstat            : None
pytest                : 8.3.2
python-calamine       : None
pyxlsb                : None
s3fs                  : 2024.6.1
scipy                 : 1.14.0
sqlalchemy            : None
tables                : None
tabulate              : 0.9.0
xarray                : None
xlrd                  : None
xlsxwriter            : None
zstandard             : None
tzdata                : 2024.1
qtpy                  : None
pyqt5                 : None

Nightly Pandas:

INSTALLED VERSIONS
------------------
commit                : 2a10e04a099d5f1633abcdfbb2dd9fdf09142f8d
python                : 3.12.7
python-bits           : 64
OS                    : Darwin
OS-release            : 24.0.0
Version               : Darwin Kernel Version 24.0.0: Tue Sep 24 23:37:36 PDT 2024; root:xnu-11215.1.12~1/RELEASE_ARM64_T6020
machine               : arm64
processor             : arm
byteorder             : little
LC_ALL                : en_US.UTF-8
LANG                  : en_US.UTF-8
LOCALE                : en_US.UTF-8

pandas                : 3.0.0.dev0+1579.g2a10e04a09
numpy                 : 2.2.0.dev0+git20241015.961b70f
dateutil              : 2.9.0.post0
pip                   : 24.2
Cython                : None
sphinx                : None
IPython               : 8.28.0
adbc-driver-postgresql: None
adbc-driver-sqlite    : None
bs4                   : None
blosc                 : None
bottleneck            : 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
psycopg2              : None
pymysql               : None
pyarrow               : 17.0.0
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
tzdata                : 2024.2
qtpy                  : None
pyqt5                 : None

[rhshadrach]

Agreed this is inconsistent, and the fix seems reasonable. cc @WillAyd @jorisvandenbossche

[WillAyd]

I think this is fine to support, although its probably better changing upstream in Arrow than implementing a fix here directly

[hombit]

@WillAyd it doesn't look like Arrow has an idea of array broadcasting, do you think I should open a feature issue there? Do you think it is likely to be implemented?

[WillAyd]

Probably worth asking there first. If it doesn't make sense can come back here for implementation