Revert "TST: fix groupby-empty xfails (pandas-dev#44092)"

This reverts commit 884d00f.

Author: simonjayhawkins

pandas/core/groupby/generic.py

@@ -1622,7 +1622,6 @@ def func(df):
             result = [index[i] if i >= 0 else np.nan for i in indices]
             return df._constructor_sliced(result, index=res.index)
 
-        func.__name__ = "idxmax"
         result = self._python_apply_general(func, self._obj_with_exclusions)
         self._maybe_warn_numeric_only_depr("idxmax", result, numeric_only)
         return result
@@ -1658,7 +1657,6 @@ def func(df):
             result = [index[i] if i >= 0 else np.nan for i in indices]
             return df._constructor_sliced(result, index=res.index)
 
-        func.__name__ = "idxmin"
         result = self._python_apply_general(func, self._obj_with_exclusions)
         self._maybe_warn_numeric_only_depr("idxmin", result, numeric_only)
         return result

pandas/core/groupby/ops.py

@@ -809,18 +809,6 @@ def apply(
                 mutated = True
             result_values.append(res)
 
-        # getattr pattern for __name__ is needed for functools.partial objects
-        if len(group_keys) == 0 and getattr(f, "__name__", None) not in [
-            "idxmin",
-            "idxmax",
-            "nanargmin",
-            "nanargmax",
-        ]:
-            # If group_keys is empty, then no function calls have been made,
-            #  so we will not have raised even if this is an invalid dtype.
-            #  So do one dummy call here to raise appropriate TypeError.
-            f(data.iloc[:0])
-
         return result_values, mutated
 
     @cache_readonly

pandas/tests/groupby/test_groupby.py

@@ -1922,23 +1922,17 @@ def test_pivot_table_values_key_error():
 )
 @pytest.mark.filterwarnings("ignore:Dropping invalid columns:FutureWarning")
 @pytest.mark.filterwarnings("ignore:.*Select only valid:FutureWarning")
-def test_empty_groupby(columns, keys, values, method, op, request, using_array_manager):
+def test_empty_groupby(columns, keys, values, method, op, request):
     # GH8093 & GH26411
     override_dtype = None
 
     if (
         isinstance(values, Categorical)
         and not isinstance(columns, list)
-        and op in ["sum", "prod", "skew", "mad"]
+        and op in ["sum", "prod"]
     ):
         # handled below GH#41291
-
-        if using_array_manager and op == "mad":
-            right_msg = "Cannot interpret 'CategoricalDtype.* as a data type"
-            msg = "Regex pattern \"'Categorical' does not implement.*" + right_msg
-            mark = pytest.mark.xfail(raises=AssertionError, match=msg)
-            request.node.add_marker(mark)
-
+        pass
     elif (
         isinstance(values, Categorical)
         and len(keys) == 1
@@ -1957,7 +1951,11 @@ def test_empty_groupby(columns, keys, values, method, op, request, using_array_m
             raises=TypeError, match="'Categorical' does not implement"
         )
         request.node.add_marker(mark)
-    elif isinstance(values, Categorical) and len(keys) == 1 and op in ["sum", "prod"]:
+    elif (
+        isinstance(values, Categorical)
+        and len(keys) == 1
+        and op in ["mad", "min", "max", "sum", "prod", "skew"]
+    ):
         mark = pytest.mark.xfail(
             raises=AssertionError, match="(DataFrame|Series) are different"
         )
@@ -1971,20 +1969,6 @@ def test_empty_groupby(columns, keys, values, method, op, request, using_array_m
             raises=AssertionError, match="(DataFrame|Series) are different"
         )
         request.node.add_marker(mark)
-
-    elif (
-        op == "mad"
-        and not isinstance(columns, list)
-        and isinstance(values, pd.DatetimeIndex)
-        and values.tz is not None
-        and using_array_manager
-    ):
-        mark = pytest.mark.xfail(
-            raises=TypeError,
-            match=r"Cannot interpret 'datetime64\[ns, US/Eastern\]' as a data type",
-        )
-        request.node.add_marker(mark)
-
     elif isinstance(values, BooleanArray) and op in ["sum", "prod"]:
         # We expect to get Int64 back for these
         override_dtype = "Int64"
@@ -2015,29 +1999,19 @@ def get_result():
 
     if columns == "C":
         # i.e. SeriesGroupBy
-        if op in ["prod", "sum", "skew"]:
+        if op in ["prod", "sum"]:
             # ops that require more than just ordered-ness
             if df.dtypes[0].kind == "M":
                 # GH#41291
                 # datetime64 -> prod and sum are invalid
-                if op == "skew":
-                    msg = "does not support reduction 'skew'"
-                else:
-                    msg = "datetime64 type does not support"
+                msg = "datetime64 type does not support"
                 with pytest.raises(TypeError, match=msg):
                     get_result()
 
                 return
-        if op in ["prod", "sum", "skew", "mad"]:
-            if isinstance(values, Categorical):
+            elif isinstance(values, Categorical):
                 # GH#41291
-                if op == "mad":
-                    # mad calls mean, which Categorical doesn't implement
-                    msg = "does not support reduction 'mean'"
-                elif op == "skew":
-                    msg = f"does not support reduction '{op}'"
-                else:
-                    msg = "category type does not support"
+                msg = "category type does not support"
                 with pytest.raises(TypeError, match=msg):
                     get_result()
 
@@ -2084,34 +2058,6 @@ def get_result():
                 tm.assert_equal(result, expected)
                 return
 
-        if (
-            op in ["mad", "min", "max", "skew"]
-            and isinstance(values, Categorical)
-            and len(keys) == 1
-        ):
-            # Categorical doesn't implement, so with numeric_only=True
-            #  these are dropped and we get an empty DataFrame back
-            result = get_result()
-            expected = df.set_index(keys)[[]]
-
-            # with numeric_only=True, these are dropped, and we get
-            # an empty DataFrame back
-            if len(keys) != 1:
-                # Categorical is special without 'observed=True'
-                lev = Categorical([0], dtype=values.dtype)
-                mi = MultiIndex.from_product([lev, lev], names=keys)
-                expected = DataFrame([], columns=[], index=mi)
-            else:
-                # all columns are dropped, but we end up with one row
-                # Categorical is special without 'observed=True'
-                lev = Categorical([0], dtype=values.dtype)
-                ci = Index(lev, name=keys[0])
-                expected = DataFrame([], columns=[], index=ci)
-            # expected = df.set_index(keys)[columns]
-
-            tm.assert_equal(result, expected)
-            return
-
     result = get_result()
     expected = df.set_index(keys)[columns]
     if override_dtype is not None: