BUG: __from_arrow__ not respecting explicit dtype (#52547)

* BUG: __from_arrow__ not respecting explicit dtype

* Skip for arrow 7

* Add test

* Remove skip

* Skip

* Skip

Author: phofl

doc/source/whatsnew/v2.0.1.rst

@@ -27,6 +27,7 @@ Bug fixes
 - Bug in :meth:`Series.describe` not returning :class:`ArrowDtype` with ``pyarrow.float64`` type with numeric data (:issue:`52427`)
 - Fixed segfault in :meth:`Series.to_numpy` with ``null[pyarrow]`` dtype (:issue:`52443`)
 - Bug in :func:`pandas.testing.assert_series_equal` where ``check_dtype=False`` would still raise for datetime or timedelta types with different resolutions (:issue:`52449`)
+- Bug in :meth:`ArrowDtype.__from_arrow__` not respecting if dtype is explicitly given (:issue:`52533`)
 
 .. ---------------------------------------------------------------------------
 .. _whatsnew_201.other:

pandas/core/arrays/arrow/dtype.py

@@ -319,4 +319,5 @@ 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)
+        arr = array.cast(self.pyarrow_dtype, safe=True)
+        return array_class(arr)

pandas/tests/extension/test_arrow.py

@@ -1729,6 +1729,28 @@ def test_setitem_invalid_dtype(data):
         data[:] = fill_value
 
 
+@pytest.mark.skipif(pa_version_under8p0, reason="returns object with 7.0")
+def test_from_arrow_respecting_given_dtype():
+    date_array = pa.array(
+        [pd.Timestamp("2019-12-31"), pd.Timestamp("2019-12-31")], type=pa.date32()
+    )
+    result = date_array.to_pandas(
+        types_mapper={pa.date32(): ArrowDtype(pa.date64())}.get
+    )
+    expected = pd.Series(
+        [pd.Timestamp("2019-12-31"), pd.Timestamp("2019-12-31")],
+        dtype=ArrowDtype(pa.date64()),
+    )
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.skipif(pa_version_under8p0, reason="doesn't raise with 7")
+def test_from_arrow_respecting_given_dtype_unsafe():
+    array = pa.array([1.5, 2.5], type=pa.float64())
+    with pytest.raises(pa.ArrowInvalid, match="Float value 1.5 was truncated"):
+        array.to_pandas(types_mapper={pa.float64(): ArrowDtype(pa.int64())}.get)
+
+
 def test_round():
     dtype = "float64[pyarrow]"