BUG: Cannot construct DataFrame with dict as data and numeric dictionary ArrowDtype series as column

[eirki]

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
import pyarrow as pa

col_names = pd.Series([3, 4]).astype("category").astype(pd.ArrowDtype(pa.dictionary(pa.int8(), pa.int8())))
pd.DataFrame({3: [1,2]}, columns=col_names)

Issue Description

Constructing a DataFrame using a dict and Series with a numeric dictionary ArrowDtype to indicate columns results in an empty DataFrame.

Using other dtypes seems to work perfectly, e.g. category type:

col_names = pd.Series([3, 4]).astype("category")
pd.DataFrame({3: [1,2]}, columns=col_names)

Output:

	3	4
0	1	nan
1	2	nan

Using a dictionary ArrowDtype with string categories also works:

col_names = pd.Series(["3","4"]).astype("category").astype(pd.ArrowDtype(pa.dictionary(pa.int64(), pa.utf8())))
pd.DataFrame({"3": [1,2]}, columns=col_names)

Output:

	"3"	"4"
0	1	nan
1	2	nan

This bug might seem esoteric, but it bit me when trying to .stack a DataFrame with multiindex colums composed of numeric dictionary ArrowDtype:

col_level_1 = (
    pd.Series([3, 4])
    .astype("category")
    .astype(pd.ArrowDtype(pa.dictionary(pa.int64(), pa.int64())))
)
col_level_2 = (
    pd.Series([5, 6])
    .astype("category")
    .astype(pd.ArrowDtype(pa.dictionary(pa.int64(), pa.int64())))
)
multicol = pd.MultiIndex.from_arrays([col_level_1, col_level_2])
df = pd.DataFrame([[1, 2], [2, 4]], columns=multicol)

df.stack()

Output:

Empty DataFrame
Columns: [3, 4]
Index: []

Expected Behavior

A DataFrame with the data provided to the constructor:

	3	4
0	1	nan
1	2	nan

Installed Versions

INSTALLED VERSIONS

commit : c65c8dd
python : 3.11.3.final.0
python-bits : 64
OS : Linux
OS-release : 5.19.0-43-generic
Version : #44~22.04.1-Ubuntu SMP PREEMPT_DYNAMIC Mon May 22 13:39:36 UTC 2
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8

pandas : 2.1.0.dev0+942.gc65c8ddd73
numpy : 2.0.0.dev0+84.g828fba29e
pytz : 2023.3
dateutil : 2.8.2
setuptools : 65.5.0
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 : None
fsspec : None
gcsfs : None
matplotlib : None
numba : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 12.0.0
pyreadstat : None
pyxlsb : None
s3fs : None
scipy : None
snappy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
zstandard : None
tzdata : 2023.3
qtpy : None
pyqt5 : None

[topper-123]

Yeah, looks like it should behave the same as the other dtypes. A PR would be welcome.