BUG: Pyarrow scalars inferred as object dtype with arrow backend enabled whwn assigning a new column

[rohanjain101]

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

>>> pd.options.mode.dtype_backend = 'pyarrow'
>>> ser = pd.Series([1,2,3], dtype="int64[pyarrow]")
>>> df2 = pd.DataFrame({"A": ser})
>>> scalar = pa.scalar(23)
>>> scalar
<pyarrow.Int64Scalar: 23>
>>> df2["B"] = scalar
>>> df2["B"].dtype
dtype('O')
>>>

Issue Description

Since "B" is a brand new column, the dtype should be "int64[pyarrow]", not object.

Expected Behavior

With numpy scalars, the type inferred is a numpy type, so with a pyarrow scalar, I expect the type to be an arrow type, in this case, "int64[pyarrow]", not object.

Installed Versions

Details

pd.show_versions()

INSTALLED VERSIONS

commit : 1a2e300
python : 3.8.10.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.22621
machine : AMD64
processor : Intel64 Family 6 Model 85 Stepping 7, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : English_United States.1252

pandas : 2.0.0rc0
numpy : 1.24.2
pytz : 2022.7.1
dateutil : 2.8.2
setuptools : 67.2.0
pip : 22.3.1
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 : 2023.2.0
fsspec : 2023.3.0
gcsfs : None
matplotlib : None
numba : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 11.0.0
pyreadstat : None
pyxlsb : None
s3fs : None
scipy : None
snappy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
zstandard : None
tzdata : None
qtpy : None
pyqt5 : None

[mroeschke]

Thanks for the report. I'd venture if/when pandas takes on pyarrow as a required dependency we can always check for pyarrow objects without also checking if a user has pyarrow installed first #50285

[mroeschke]

Noting that replacing an existing arrow backed column with a pa.scalar works

In [2]: import pyarrow as pa

In [3]: arr = pd.arrays.ArrowExtensionArray(pa.array([1, 2], type=pa.int8()))

In [4]: arr[1] = pa.scalar(23)

In [5]: arr
Out[5]:
<ArrowExtensionArray>
[1, 23]
Length: 2, dtype: int8[pyarrow]

In [6]: ser = Series(arr)

In [7]: ser[1] = pa.scalar(24)

In [8]: ser
Out[8]:
0     1
1    24
dtype: int8[pyarrow]