Fix where kludge

Author: jbrockmendel

pandas/core/arrays/arrow/array.py

@@ -407,6 +407,8 @@ def _from_sequence_of_strings(
                 mask = strings.is_null()
                 scalars = pa.array(scalars, mask=np.array(mask), type=pa_type)
                 # TODO: could we just do strings.cast(pa_type)?
+            elif isinstance(strings, (pa.Array, pa.ChunkedArray)):
+                scalars = strings.cast(pa_type)
             elif mask is not None:
                 scalars = pa.array(scalars, mask=mask.view(bool), type=pa_type)
 

pandas/core/generic.py

@@ -10089,23 +10089,6 @@ def where(
                         stacklevel=2,
                     )
 
-        if other is lib.no_default:
-            if self.ndim == 1:
-                if isinstance(self.dtype, ExtensionDtype):
-                    other = self.dtype.na_value
-                else:
-                    other = np.nan
-            else:
-                if self._mgr.nblocks == 1 and isinstance(
-                    self._mgr.blocks[0].values.dtype, ExtensionDtype
-                ):
-                    # FIXME: checking this is kludgy!
-                    other = self._mgr.blocks[0].values.dtype.na_value
-                else:
-                    # FIXME: the same problem we had with Series will now
-                    #  show up column-by-column!
-                    other = np.nan
-
         other = common.apply_if_callable(other, self)
         return self._where(cond, other, inplace=inplace, axis=axis, level=level)
 

pandas/tests/extension/test_arrow.py

@@ -1562,8 +1562,11 @@ def test_to_numpy_int_with_na():
     data = [1, None]
     arr = pd.array(data, dtype="int64[pyarrow]")
     result = arr.to_numpy()
-    expected = np.array([1, np.nan])
-    assert isinstance(result[0], float)
+    if using_pyarrow_strict_nans():
+        expected = np.array([1, pd.NA], dtype=object)
+    else:
+        expected = np.array([1, np.nan])
+        assert isinstance(result[0], float)
     tm.assert_numpy_array_equal(result, expected)