update ufunc test

Author: jbrockmendel

pandas/core/arrays/arrow/array.py

@@ -50,16 +50,10 @@
     pandas_dtype,
 )
 from pandas.core.dtypes.dtypes import DatetimeTZDtype
-from pandas.core.dtypes.generic import (
-    ABCDataFrame,
-    ABCIndex,
-    ABCSeries,
-)
 from pandas.core.dtypes.missing import isna
 
 from pandas.core import (
     algorithms as algos,
-    arraylike,
     missing,
     ops,
     roperator,
@@ -758,39 +752,6 @@ def __array__(
 
         return self.to_numpy(dtype=dtype, copy=copy)
 
-    def __array_ufunc__(self, ufunc: np.ufunc, method: str, *inputs, **kwargs):
-        if any(
-            isinstance(other, (ABCSeries, ABCIndex, ABCDataFrame)) for other in inputs
-        ):
-            return NotImplemented
-
-        result = arraylike.maybe_dispatch_ufunc_to_dunder_op(
-            self, ufunc, method, *inputs, **kwargs
-        )
-        if result is not NotImplemented:
-            return result
-
-        if "out" in kwargs:
-            return arraylike.dispatch_ufunc_with_out(
-                self, ufunc, method, *inputs, **kwargs
-            )
-
-        if method == "reduce":
-            result = arraylike.dispatch_reduction_ufunc(
-                self, ufunc, method, *inputs, **kwargs
-            )
-            if result is not NotImplemented:
-                return result
-
-        if self.dtype.kind == "f":
-            # e.g. test_log_arrow_backed_missing_value
-            new_inputs = [
-                x if x is not self else x.to_numpy(na_value=np.nan) for x in inputs
-            ]
-            return getattr(ufunc, method)(*new_inputs, **kwargs)
-
-        return arraylike.default_array_ufunc(self, ufunc, method, *inputs, **kwargs)
-
     def __invert__(self) -> Self:
         # This is a bit wise op for integer types
         if pa.types.is_integer(self._pa_array.type):

pandas/tests/series/test_npfuncs.py

@@ -41,6 +41,12 @@ def test_numpy_argwhere(index):
 def test_log_arrow_backed_missing_value(using_nan_is_na):
     # GH#56285
     ser = Series([1, 2, None], dtype="float64[pyarrow]")
-    result = np.log(ser)
-    expected = np.log(Series([1, 2, None], dtype="float64"))
-    tm.assert_series_equal(result, expected)
+    if using_nan_is_na:
+        result = np.log(ser)
+        expected = np.log(Series([1, 2, None], dtype="float64"))
+        tm.assert_series_equal(result, expected)
+    else:
+        # we get cast to object which raises
+        msg = "loop of ufunc does not support argument"
+        with pytest.raises(TypeError, match=msg):
+            np.log(ser)