BUG: GroupBy.idxmax/idxmin with EA dtypes

doc/source/whatsnew/v1.3.0.rst

@@ -339,6 +339,7 @@ Groupby/resample/rolling
 - Fixed bug in :meth:`DataFrameGroupBy.sum` and :meth:`SeriesGroupBy.sum` causing loss of precision through using Kahan summation (:issue:`38778`)
 - Fixed bug in :meth:`DataFrameGroupBy.cumsum`, :meth:`SeriesGroupBy.cumsum`, :meth:`DataFrameGroupBy.mean` and :meth:`SeriesGroupBy.mean` causing loss of precision through using Kahan summation (:issue:`38934`)
 - Bug in :meth:`.Resampler.aggregate` and :meth:`DataFrame.transform` raising ``TypeError`` instead of ``SpecificationError`` when missing keys had mixed dtypes (:issue:`39025`)
+- Bug in :meth:`GroupBy.idxmin` and :meth:`GroupBy.idxmax` with ``ExtendionDtype`` columns (:issue:`38733`)
 
 Reshaping
 ^^^^^^^^^

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():
+            raise NotImplementedError
         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():
+            raise NotImplementedError
         return nargminmax(self, "argmax")
 
     def fillna(self, value=None, method=None, limit=None):

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)