REF: standardize __array_ufunc__ patterns

pandas/core/arrays/categorical.py

@@ -85,7 +85,10 @@
     notna,
 )
 
-from pandas.core import ops
+from pandas.core import (
+    arraylike,
+    ops,
+)
 from pandas.core.accessor import (
     PandasDelegate,
     delegate_names,
@@ -1516,6 +1519,14 @@ def __array_ufunc__(self, ufunc: np.ufunc, method: str, *inputs, **kwargs):
         if result is not NotImplemented:
             return result
 
+        if method == "reduce":
+            # e.g. TestCategoricalAnalytics::test_min_max_ordered
+            result = arraylike.dispatch_reduction_ufunc(
+                self, ufunc, method, *inputs, **kwargs
+            )
+            if result is not NotImplemented:
+                return result
+
         # for all other cases, raise for now (similarly as what happens in
         # Series.__array_prepare__)
         raise TypeError(

pandas/core/arrays/numpy_.py

@@ -1,7 +1,5 @@
 from __future__ import annotations
 
-import numbers
-
 import numpy as np
 
 from pandas._libs import lib
@@ -130,8 +128,6 @@ def dtype(self) -> PandasDtype:
     def __array__(self, dtype: NpDtype | None = None) -> np.ndarray:
         return np.asarray(self._ndarray, dtype=dtype)
 
-    _HANDLED_TYPES = (np.ndarray, numbers.Number)
-
     def __array_ufunc__(self, ufunc: np.ufunc, method: str, *inputs, **kwargs):
         # Lightly modified version of
         # https://numpy.org/doc/stable/reference/generated/numpy.lib.mixins.NDArrayOperatorsMixin.html

pandas/core/arrays/sparse/array.py

@@ -73,6 +73,7 @@
     notna,
 )
 
+from pandas.core import arraylike
 import pandas.core.algorithms as algos
 from pandas.core.arraylike import OpsMixin
 from pandas.core.arrays import ExtensionArray
@@ -1415,7 +1416,9 @@ def any(self, axis=0, *args, **kwargs):
 
         return values.any().item()
 
-    def sum(self, axis: int = 0, min_count: int = 0, *args, **kwargs) -> Scalar:
+    def sum(
+        self, axis: int = 0, min_count: int = 0, skipna: bool = True, *args, **kwargs
+    ) -> Scalar:
         """
         Sum of non-NA/null values
 
@@ -1437,6 +1440,11 @@ def sum(self, axis: int = 0, min_count: int = 0, *args, **kwargs) -> Scalar:
         nv.validate_sum(args, kwargs)
         valid_vals = self._valid_sp_values
         sp_sum = valid_vals.sum()
+        has_na = self.sp_index.ngaps > 0 and not self._null_fill_value
+
+        if has_na and not skipna:
+            return na_value_for_dtype(self.dtype.subtype, compat=False)
+
         if self._null_fill_value:
             if check_below_min_count(valid_vals.shape, None, min_count):
                 return na_value_for_dtype(self.dtype.subtype, compat=False)
@@ -1589,6 +1597,21 @@ def __array_ufunc__(self, ufunc: np.ufunc, method: str, *inputs, **kwargs):
         if result is not NotImplemented:
             return result
 
+        if "out" in kwargs:
+            # e.g. tests.arrays.sparse.test_arithmetics.test_ndarray_inplace
+            res = arraylike.dispatch_ufunc_with_out(
+                self, ufunc, method, *inputs, **kwargs
+            )
+            return res
+
+        if method == "reduce":
+            result = arraylike.dispatch_reduction_ufunc(
+                self, ufunc, method, *inputs, **kwargs
+            )
+            if result is not NotImplemented:
+                # e.g. tests.series.test_ufunc.TestNumpyReductions
+                return result
+
         if len(inputs) == 1:
             # No alignment necessary.
             sp_values = getattr(ufunc, method)(self.sp_values, **kwargs)
@@ -1611,7 +1634,8 @@ def __array_ufunc__(self, ufunc: np.ufunc, method: str, *inputs, **kwargs):
                 sp_values, self.sp_index, SparseDtype(sp_values.dtype, fill_value)
             )
 
-        result = getattr(ufunc, method)(*(np.asarray(x) for x in inputs), **kwargs)
+        new_inputs = tuple(np.asarray(x) for x in inputs)
+        result = getattr(ufunc, method)(*new_inputs, **kwargs)
         if out:
             if len(out) == 1:
                 out = out[0]

pandas/core/indexes/base.py

@@ -878,6 +878,12 @@ def __array_ufunc__(self, ufunc: np.ufunc, method: str_t, *inputs, **kwargs):
         if result is not NotImplemented:
             return result
 
+        if "out" in kwargs:
+            # e.g. test_dti_isub_tdi
+            return arraylike.dispatch_ufunc_with_out(
+                self, ufunc, method, *inputs, **kwargs
+            )
+
         if method == "reduce":
             result = arraylike.dispatch_reduction_ufunc(
                 self, ufunc, method, *inputs, **kwargs

pandas/tests/arithmetic/test_datetime64.py

@@ -2107,7 +2107,7 @@ def test_dti_isub_tdi(self, tz_naive_fixture):
         np.subtract(out, tdi, out=out)
         tm.assert_datetime_array_equal(out, expected._data)
 
-        msg = "cannot subtract .* from a TimedeltaArray"
+        msg = "cannot subtract a datelike from a TimedeltaArray"
         with pytest.raises(TypeError, match=msg):
             tdi -= dti
 
@@ -2116,11 +2116,9 @@ def test_dti_isub_tdi(self, tz_naive_fixture):
         result -= tdi.values
         tm.assert_index_equal(result, expected)
 
-        msg = "cannot subtract DatetimeArray from ndarray"
         with pytest.raises(TypeError, match=msg):
             tdi.values -= dti
 
-        msg = "cannot subtract a datelike from a TimedeltaArray"
         with pytest.raises(TypeError, match=msg):
             tdi._values -= dti
 

pandas/tests/arrays/categorical/test_analytics.py

@@ -29,20 +29,32 @@ def test_min_max_not_ordered_raises(self, aggregation):
         with pytest.raises(TypeError, match=msg):
             agg_func()
 
-    def test_min_max_ordered(self):
+        ufunc = np.minimum if aggregation == "min" else np.maximum
+        with pytest.raises(TypeError, match=msg):
+            ufunc.reduce(cat)
+
+    def test_min_max_ordered(self, index_or_series_or_array):
         cat = Categorical(["a", "b", "c", "d"], ordered=True)
-        _min = cat.min()
-        _max = cat.max()
+        obj = index_or_series_or_array(cat)
+        _min = obj.min()
+        _max = obj.max()
         assert _min == "a"
         assert _max == "d"
 
+        assert np.minimum.reduce(obj) == "a"
+        assert np.maximum.reduce(obj) == "d"
+        # TODO: raises if we pass axis=0  (on Index and Categorical, not Series)
+
         cat = Categorical(
             ["a", "b", "c", "d"], categories=["d", "c", "b", "a"], ordered=True
         )
-        _min = cat.min()
-        _max = cat.max()
+        obj = index_or_series_or_array(cat)
+        _min = obj.min()
+        _max = obj.max()
         assert _min == "d"
         assert _max == "a"
+        assert np.minimum.reduce(obj) == "d"
+        assert np.maximum.reduce(obj) == "a"
 
     @pytest.mark.parametrize(
         "categories,expected",

pandas/tests/extension/decimal/array.py

@@ -25,6 +25,7 @@
     is_list_like,
     is_scalar,
 )
+from pandas.core import arraylike
 from pandas.core.arraylike import OpsMixin
 from pandas.core.arrays import (
     ExtensionArray,
@@ -121,6 +122,13 @@ def __array_ufunc__(self, ufunc: np.ufunc, method: str, *inputs, **kwargs):
         inputs = tuple(x._data if isinstance(x, DecimalArray) else x for x in inputs)
         result = getattr(ufunc, method)(*inputs, **kwargs)
 
+        if method == "reduce":
+            result = arraylike.dispatch_reduction_ufunc(
+                self, ufunc, method, *inputs, **kwargs
+            )
+            if result is not NotImplemented:
+                return result
+
         def reconstruct(x):
             if isinstance(x, (decimal.Decimal, numbers.Number)):
                 return x