BUG: GroupBy.idxmax/idxmin with EA dtypes

pandas/core/arrays/base.py

@@ -30,7 +30,7 @@
 from pandas.compat.numpy import function as nv
 from pandas.errors import AbstractMethodError
 from pandas.util._decorators import Appender, Substitution
-from pandas.util._validators import validate_fillna_kwargs
+from pandas.util._validators import validate_bool_kwarg, validate_fillna_kwargs
 
 from pandas.core.dtypes.cast import maybe_cast_to_extension_array
 from pandas.core.dtypes.common import (
@@ -596,7 +596,7 @@ def argsort(
             mask=np.asarray(self.isna()),
         )
 
-    def argmin(self):
+    def argmin(self, skipna: bool = True) -> int:
         """
         Return the index of minimum value.
 
@@ -611,9 +611,12 @@ def argmin(self):
         --------
         ExtensionArray.argmax
         """
+        validate_bool_kwarg(skipna, "skipna")
+        if not skipna and self.isna().any():
+            return -1
         return nargminmax(self, "argmin")
 
-    def argmax(self):
+    def argmax(self, skipna: bool = True) -> int:
         """
         Return the index of maximum value.
 
@@ -628,6 +631,9 @@ def argmax(self):
         --------
         ExtensionArray.argmin
         """
+        validate_bool_kwarg(skipna, "skipna")
+        if not skipna and self.isna().any():
+            return -1
         return nargminmax(self, "argmax")
 
     def fillna(self, value=None, method=None, limit=None):

pandas/core/base.py

@@ -726,10 +726,7 @@ def argmax(self, axis=None, skipna: bool = True, *args, **kwargs) -> int:
         skipna = nv.validate_argmax_with_skipna(skipna, args, kwargs)
 
         if isinstance(delegate, ExtensionArray):
-            if not skipna and delegate.isna().any():
-                return -1
-            else:
-                return delegate.argmax()
+            return delegate.argmax(skipna=skipna)
         else:
             return nanops.nanargmax(delegate, skipna=skipna)
 
@@ -784,10 +781,7 @@ def argmin(self, axis=None, skipna=True, *args, **kwargs) -> int:
         skipna = nv.validate_argmin_with_skipna(skipna, args, kwargs)
 
         if isinstance(delegate, ExtensionArray):
-            if not skipna and delegate.isna().any():
-                return -1
-            else:
-                return delegate.argmin()
+            return delegate.argmin(skipna=skipna)
         else:
             return nanops.nanargmin(delegate, skipna=skipna)
 

pandas/tests/extension/base/methods.py

@@ -107,6 +107,21 @@ def test_argmin_argmax_all_na(self, method, data, na_value):
         with pytest.raises(ValueError, match=err_msg):
             getattr(data_na, method)()
 
+    @pytest.mark.parametrize(
+        "op_name, skipna, expected",
+        [
+            ("argmax", True, 0),
+            ("argmin", True, 2),
+            ("argmax", False, -1),
+            ("argmin", False, -1),
+        ],
+    )
+    def test_argmin_argmax_skipna(
+        self, op_name, skipna, expected, data_missing_for_sorting
+    ):
+        result = getattr(data_missing_for_sorting, op_name)(skipna=skipna)
+        tm.assert_almost_equal(result, expected)
+
     @pytest.mark.parametrize(
         "op_name, skipna, expected",
         [

pandas/tests/groupby/test_function.py

@@ -531,10 +531,16 @@ def test_idxmin_idxmax_returns_int_types(func, values):
         }
     )
     df["c_date"] = pd.to_datetime(df["c_date"])
+    df["c_date_tz"] = df["c_date"].dt.tz_localize("US/Pacific")
+    df["c_timedelta"] = df["c_date"] - df["c_date"].iloc[0]
+    df["c_period"] = df["c_date"].dt.to_period("W")
 
     result = getattr(df.groupby("name"), func)()
 
     expected = DataFrame(values, index=Index(["A", "B"], name="name"))
+    expected["c_date_tz"] = expected["c_date"]
+    expected["c_timedelta"] = expected["c_date"]
+    expected["c_period"] = expected["c_date"]
 
     tm.assert_frame_equal(result, expected)