BUG: idxmax/min (and argmax/min) for Series with underlying ExtensionArray

doc/source/whatsnew/v1.3.0.rst

@@ -301,8 +301,9 @@ Sparse
 
 ExtensionArray
 ^^^^^^^^^^^^^^
+
 - Bug in :meth:`DataFrame.where` when ``other`` is a :class:`Series` with ExtensionArray dtype (:issue:`38729`)
--
+- Bug in :meth:`Series.idxmax`, :meth:`Series.idxmin` and ``argmax/min`` fail when the underlying data is :class:`ExtensionArray` (:issue:`32749`, :issue:`33719`, :issue:`36566`)
 -
 
 Other

pandas/core/base.py

@@ -715,9 +715,17 @@ def argmax(self, axis=None, skipna: bool = True, *args, **kwargs) -> int:
         the minimum cereal calories is the first element,
         since series is zero-indexed.
         """
+        delegate = self._values
         nv.validate_minmax_axis(axis)
-        nv.validate_argmax_with_skipna(skipna, args, kwargs)
-        return nanops.nanargmax(self._values, skipna=skipna)
+        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()
+        else:
+            return nanops.nanargmax(delegate, skipna=skipna)
 
     def min(self, axis=None, skipna: bool = True, *args, **kwargs):
         """
@@ -765,9 +773,17 @@ def min(self, axis=None, skipna: bool = True, *args, **kwargs):
 
     @doc(argmax, op="min", oppose="max", value="smallest")
     def argmin(self, axis=None, skipna=True, *args, **kwargs) -> int:
+        delegate = self._values
         nv.validate_minmax_axis(axis)
-        nv.validate_argmax_with_skipna(skipna, args, kwargs)
-        return nanops.nanargmin(self._values, skipna=skipna)
+        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()
+        else:
+            return nanops.nanargmin(delegate, skipna=skipna)
 
     def tolist(self):
         """

pandas/core/series.py

@@ -2076,8 +2076,7 @@ def idxmin(self, axis=0, skipna=True, *args, **kwargs):
         >>> s.idxmin(skipna=False)
         nan
         """
-        skipna = nv.validate_argmin_with_skipna(skipna, args, kwargs)
-        i = nanops.nanargmin(self._values, skipna=skipna)
+        i = self.argmin(None, skipna=skipna)
         if i == -1:
             return np.nan
         return self.index[i]
@@ -2147,8 +2146,7 @@ def idxmax(self, axis=0, skipna=True, *args, **kwargs):
         >>> s.idxmax(skipna=False)
         nan
         """
-        skipna = nv.validate_argmax_with_skipna(skipna, args, kwargs)
-        i = nanops.nanargmax(self._values, skipna=skipna)
+        i = self.argmax(None, skipna=skipna)
         if i == -1:
             return np.nan
         return self.index[i]

pandas/tests/extension/base/__init__.py

@@ -58,6 +58,7 @@ class TestMyDtype(BaseDtypeTests):
 )
 from .printing import BasePrintingTests  # noqa
 from .reduce import (  # noqa
+    BaseArgReduceTests,
     BaseBooleanReduceTests,
     BaseNoReduceTests,
     BaseNumericReduceTests,

pandas/tests/extension/base/reduce.py

@@ -68,3 +68,11 @@ def test_reduce_series(self, data, all_boolean_reductions, skipna):
         op_name = all_boolean_reductions
         s = pd.Series(data)
         self.check_reduce(s, op_name, skipna)
+
+
+class BaseArgReduceTests(BaseReduceTests):
+    @pytest.mark.parametrize("skipna", [True, False])
+    @pytest.mark.parametrize("op_name", ["argmin", "argmax", "idxmin", "idxmax"])
+    def test_reduce_series(self, data, op_name, skipna):
+        s = pd.Series(data)
+        self.check_reduce(s, op_name, skipna)

pandas/tests/extension/decimal/test_decimal.py

@@ -166,6 +166,10 @@ class TestBooleanReduce(Reduce, base.BaseBooleanReduceTests):
     pass
 
 
+class TestArgReduce(base.BaseArgReduceTests):
+    pass
+
+
 class TestMethods(BaseDecimal, base.BaseMethodsTests):
     @pytest.mark.parametrize("dropna", [True, False])
     @pytest.mark.xfail(reason="value_counts not implemented yet.")

pandas/tests/extension/test_boolean.py

@@ -381,6 +381,10 @@ class TestBooleanReduce(base.BaseBooleanReduceTests):
     pass
 
 
+class TestArgReduce(base.BaseArgReduceTests):
+    pass
+
+
 class TestPrinting(base.BasePrintingTests):
     pass
 

pandas/tests/extension/test_datetime.py

@@ -4,6 +4,7 @@
 from pandas.core.dtypes.dtypes import DatetimeTZDtype
 
 import pandas as pd
+import pandas._testing as tm
 from pandas.core.arrays import DatetimeArray
 from pandas.tests.extension import base
 
@@ -223,3 +224,16 @@ class TestGroupby(BaseDatetimeTests, base.BaseGroupbyTests):
 
 class TestPrinting(BaseDatetimeTests, base.BasePrintingTests):
     pass
+
+
+class TestArgReduce(base.BaseArgReduceTests):
+    def check_reduce(self, s, op_name, skipna):
+        result = getattr(s, op_name)(skipna=skipna)
+        if not skipna and s.isna().any():
+            if op_name in ["argmin", "argmax"]:
+                expected = -1
+            else:
+                expected = np.nan
+        else:
+            expected = getattr(s.dropna().astype("int64"), op_name)(skipna=skipna)
+        tm.assert_almost_equal(result, expected)

pandas/tests/extension/test_floating.py

@@ -215,6 +215,10 @@ class TestBooleanReduce(base.BaseBooleanReduceTests):
     pass
 
 
+class TestArgReduce(base.BaseArgReduceTests):
+    pass
+
+
 class TestPrinting(base.BasePrintingTests):
     pass
 

pandas/tests/extension/test_integer.py

@@ -249,6 +249,10 @@ class TestBooleanReduce(base.BaseBooleanReduceTests):
     pass
 
 
+class TestArgReduce(base.BaseArgReduceTests):
+    pass
+
+
 class TestPrinting(base.BasePrintingTests):
     pass
 

pandas/tests/extension/test_numpy.py

@@ -315,6 +315,11 @@ class TestBooleanReduce(BaseNumPyTests, base.BaseBooleanReduceTests):
     pass
 
 
+@skip_nested
+class TestArgReduce(base.BaseArgReduceTests):
+    pass
+
+
 class TestMissing(BaseNumPyTests, base.BaseMissingTests):
     @skip_nested
     def test_fillna_scalar(self, data_missing):