BUG: dot on Arrow Dataframes/Series produces a Numpy object result

[ldouteau]

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

# Dataframe
cols = ["a", "b"]
df_a = pd.DataFrame(
    [[1, 2], [3, 4], [5, 6]], columns=cols, dtype="float[pyarrow]"
)
df_b = pd.DataFrame([[1, 0], [0, 1]], index=cols, dtype="float[pyarrow]")
df_c = df_a.dot(df_b)

print("input")
print(df_a.dtypes)
print(df_b.dtypes)
print("output")
print(df_c.dtypes)

# Series
s_a = pd.Series([1, 2], index=cols, dtype="float[pyarrow]")
s_b = df_a.dot(s_a)

print("input")
print(s_a.dtypes)
print("output")
print(s_b.dtypes)

Issue Description

When applying dot onto 2 elements that use Arrow contents, the result produced contains Numpy objects. This applies to DataFrame and Series

Expected Behavior

I would expect result DataFrame/Series to contain Arrow elements. My current workaround is to cast it back to Arrow using .astype("float[pyarrow]").

I'm usure if this function should be supported or not. I didn't find any documentation about it, maybe I missed it. Is there any place to track the integration of Arrow into Pandas ?

Installed Versions

Tested latest version 2.0.3 & main branch. Details provided

INSTALLED VERSIONS ------------------ commit : 0f43794 python : 3.11.3.final.0 python-bits : 64 OS : Windows OS-release : 10 Version : 10.0.22621 machine : AMD64 processor : Intel64 Family 6 Model 140 Stepping 1, GenuineIntel byteorder : little LC_ALL : None LANG : en_US.UTF-8 LOCALE : fr_FR.cp1252

pandas : 2.0.3
numpy : 1.24.3
pytz : 2023.3
dateutil : 2.8.2
setuptools : 65.5.0
pip : 23.1.2
Cython : None
pytest : 7.3.1
hypothesis : None
sphinx : 7.0.1
blosc : None
feather : None
xlsxwriter : 3.1.2
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.1.2
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
brotli : None
fastparquet : None
fsspec : None
gcsfs : None
matplotlib : 3.7.1
numba : None
numexpr : None
odfpy : None
openpyxl : 3.1.2
pandas_gbq : None
pyarrow : 12.0.0
pyreadstat : None
pyxlsb : None
s3fs : None
scipy : 1.10.1
snappy : None
sqlalchemy : None
tables : None
tabulate : 0.9.0
xarray : 2023.5.0
xlrd : None
zstandard : None
tzdata : 2023.3
qtpy : None
pyqt5 : None

INSTALLED VERSIONS

commit : 4da9cb6
python : 3.11.3.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.22621
machine : AMD64
processor : Intel64 Family 6 Model 140 Stepping 1, GenuineIntel
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : fr_FR.cp1252

pandas : 2.1.0.dev0+1122.g4da9cb69f8
numpy : 2.0.0.dev0+358.g2f375c0f9
pytz : 2023.3
dateutil : 2.8.2
setuptools : 65.5.0
pip : 23.1.2
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
brotli : None
fastparquet : None
fsspec : None
gcsfs : None
matplotlib : None
numba : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 12.0.1
pyreadstat : None
pyxlsb : None
s3fs : None
scipy : None
snappy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
zstandard : None
tzdata : 2023.3
qtpy : None
pyqt5 : None

[VigneshRamanathan101]

I am 1st time contributor and I would like to contribute to this.

Please assign it to me.

[phofl]

This is non trivial, I’d recommend looking into
Something else.

[mcgeestocks]

Take