fix failures

Author: topper-123

pandas/core/arrays/masked.py

@@ -42,13 +42,10 @@
 from pandas.core.dtypes.common import (
     is_bool,
     is_dtype_equal,
-    is_float_dtype,
     is_integer_dtype,
     is_list_like,
     is_scalar,
-    is_signed_integer_dtype,
     is_string_dtype,
-    is_unsigned_integer_dtype,
     pandas_dtype,
 )
 from pandas.core.dtypes.dtypes import BaseMaskedDtype
@@ -1112,19 +1109,19 @@ def _wrap_min_count_reduction_result(
         self, name: str, result, skipna, min_count, **kwargs
     ):
         if min_count == 0 and isinstance(result, np.ndarray):
-            return self._maybe_mask_result(result, np.zeros(1, dtype=bool))
+            return self._maybe_mask_result(result, np.zeros(result.shape, dtype=bool))
         return self._wrap_reduction_result(name, result, skipna, **kwargs)
 
     def _wrap_na_result(self, name):
         mask = np.ones(1, dtype=bool)
 
-        if is_float_dtype(self.dtype):
+        if self.dtype.kind == "f":
             np_dtype = np.float64
         elif name in ["mean", "median", "var", "std", "skew"]:
             np_dtype = np.float64
-        elif is_signed_integer_dtype(self.dtype):
+        elif self.dtype.kind == "i":
             np_dtype = np.int64
-        elif is_unsigned_integer_dtype(self.dtype):
+        elif self.dtype.kind == "u":
             np_dtype = np.uint64
         else:
             raise TypeError(self.dtype)

pandas/core/arrays/sparse/array.py

@@ -1368,7 +1368,9 @@ def nonzero(self) -> tuple[npt.NDArray[np.int32]]:
     # Reductions
     # ------------------------------------------------------------------------
 
-    def _reduce(self, name: str, *, skipna: bool = True, **kwargs):
+    def _reduce(
+        self, name: str, *, skipna: bool = True, keepdims: bool = False, **kwargs
+    ):
         method = getattr(self, name, None)
 
         if method is None:

pandas/tests/extension/base/dim2.py

@@ -197,6 +197,8 @@ def test_reductions_2d_axis0(self, data, method):
         if method in ["std", "var"]:
             # pass ddof=0 so we get all-zero std instead of all-NA std
             kwargs["ddof"] = 0
+        elif method in ["prod", "sum"]:
+            kwargs["min_count"] = 1
 
         try:
             result = getattr(arr2d, method)(axis=0, **kwargs)

pandas/tests/groupby/test_apply.py

@@ -944,8 +944,7 @@ def test_apply_multi_level_name(category):
     if category:
         b = pd.Categorical(b, categories=[1, 2, 3])
         expected_index = pd.CategoricalIndex([1, 2, 3], categories=[1, 2, 3], name="B")
-        # GH#40669 - summing an empty frame gives float dtype
-        expected_values = [20.0, 25.0, 0.0]
+        expected_values = [20, 25, 0]
     else:
         expected_index = Index([1, 2], name="B")
         expected_values = [20, 25]