BUG: ArrowDtype `__from_arrow__` ignores its dtype but converts data to its mapped ArrowDtype

[chelsea-lin]

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Reproducible Example

import pyarrow as pa
import pandas as pd
from datetime import date, timedelta

# Create a Date32Array (date32[day]) in PyArrow
date_array = pa.array([date.today() - timedelta(days=i) for i in range(5)], type=pa.date32())

# Create a PyArrow Table
table = pa.Table.from_arrays([date_array], schema=pa.schema([('date', pa.date32())]))

# Convert PyArrow Table to pandas DataFrame using types_mapper
df = table.to_pandas(types_mapper={pa.date32(): pd.ArrowDtype(pa.date64())}.get)

# Display the pandas DataFrame
>>> df.dtypes
date    date32[day][pyarrow]
dtype: object

## ERROR: we expect an `date64[ms][pyarrow]` dtype here.

Issue Description

Here is the call stack of above example:

• init (pandas/core/arrays/arrow/array.py:208)

    def __init__(self, values: pa.Array | pa.ChunkedArray, *, dtype: ArrowDtype | None = None) -> None:
        ...
        elif isinstance(values, pa.ChunkedArray):
            self._data = values
        ...
        self._dtype = ArrowDtype(self._data.type)

Here, self._data.type is DataType(date32[day]) so mapped to ArrowDtype as date32[day][pyarrow]. Although we expect it map to date64[ms][pyarrow]

• from_arrow (pandas/core/arrays/arrow/dtype.py:204)

    def __from_arrow__(self, array: pa.Array | pa.ChunkedArray):
        """
        Construct IntegerArray/FloatingArray from pyarrow Array/ChunkedArray.
        """
        array_class = self.construct_array_type()
        return array_class(array)

Here, the self is a date64[ms][pyarrow] ArrowDtype and array is pyarrow DataType(date32[day])

• _reconstruct_block (pyarrow/pandas_compat.py:780)
• (pyarrow/pandas_compat.py:1171)
• _table_to_blocks (pyarrow/pandas_compat.py:1171)
• table_to_blockmanager (pyarrow/pandas_compat.py:820)
• table.to_pandas(types_mapper={pa.date32(): pd.ArrowDtype(pa.date64())}.get)

Expected Behavior

Instead of getting ArrowDtype from data, can ArrowDtype.__from_arrow__ pass itself to ArrowExtensionArray.__init__. In this way, the data can convert to be an expected pandas dtype.

Installed Versions

INSTALLED VERSIONS

commit : fe415f5
python : 3.9.16.final.0
python-bits : 64
OS : Linux
OS-release : 5.19.11-1rodete1-amd64
Version : #1 SMP PREEMPT_DYNAMIC Debian 5.19.11-1rodete1 (2022-10-31)
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8

pandas : 2.1.0.dev0+444.gfe415f553f
numpy : 1.24.2
pytz : 2023.3
dateutil : 2.8.2
setuptools : 67.6.0
pip : 23.0.1
Cython : None
pytest : 7.2.2
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : 8.12.0
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 : 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 : 2023.3
qtpy : None
pyqt5 : None