CLN/PERF: Simplify argmin/argmax

pandas/core/arrays/_mixins.py

@@ -210,15 +210,15 @@ def argmin(self, axis: AxisInt = 0, skipna: bool = True):  # type: ignore[overri
         # override base class by adding axis keyword
         validate_bool_kwarg(skipna, "skipna")
         if not skipna and self._hasna:
-            raise NotImplementedError
+            raise ValueError("Encountered an NA value with skipna=False")
         return nargminmax(self, "argmin", axis=axis)
 
     # Signature of "argmax" incompatible with supertype "ExtensionArray"
     def argmax(self, axis: AxisInt = 0, skipna: bool = True):  # type: ignore[override]
         # override base class by adding axis keyword
         validate_bool_kwarg(skipna, "skipna")
         if not skipna and self._hasna:
-            raise NotImplementedError
+            raise ValueError("Encountered an NA value with skipna=False")
         return nargminmax(self, "argmax", axis=axis)
 
     def unique(self) -> Self:

pandas/core/arrays/base.py

@@ -885,7 +885,7 @@ def argmin(self, skipna: bool = True) -> int:
         # 2. argmin itself : total control over sorting.
         validate_bool_kwarg(skipna, "skipna")
         if not skipna and self._hasna:
-            raise NotImplementedError
+            raise ValueError("Encountered an NA value with skipna=False")
         return nargminmax(self, "argmin")
 
     def argmax(self, skipna: bool = True) -> int:
@@ -919,7 +919,7 @@ def argmax(self, skipna: bool = True) -> int:
         # 2. argmax itself : total control over sorting.
         validate_bool_kwarg(skipna, "skipna")
         if not skipna and self._hasna:
-            raise NotImplementedError
+            raise ValueError("Encountered an NA value with skipna=False")
         return nargminmax(self, "argmax")
 
     def interpolate(

pandas/core/arrays/sparse/array.py

@@ -1623,13 +1623,13 @@ def _argmin_argmax(self, kind: Literal["argmin", "argmax"]) -> int:
     def argmax(self, skipna: bool = True) -> int:
         validate_bool_kwarg(skipna, "skipna")
         if not skipna and self._hasna:
-            raise NotImplementedError
+            raise ValueError("Encountered an NA value with skipna=False")
         return self._argmin_argmax("argmax")
 
     def argmin(self, skipna: bool = True) -> int:
         validate_bool_kwarg(skipna, "skipna")
         if not skipna and self._hasna:
-            raise NotImplementedError
+            raise ValueError("Encountered an NA value with skipna=False")
         return self._argmin_argmax("argmin")
 
     # ------------------------------------------------------------------------

pandas/core/base.py

@@ -53,7 +53,6 @@
 
 from pandas.core import (
     algorithms,
-    nanops,
     ops,
 )
 from pandas.core.accessor import DirNamesMixin
@@ -731,43 +730,17 @@ def argmax(
         the minimum cereal calories is the first element,
         since series is zero-indexed.
         """
-        delegate = self._values
         nv.validate_minmax_axis(axis)
         skipna = nv.validate_argmax_with_skipna(skipna, args, kwargs)
-
-        if skipna and len(delegate) > 0 and isna(delegate).all():
-            raise ValueError("Encountered all NA values")
-        elif not skipna and isna(delegate).any():
-            raise ValueError("Encountered an NA value with skipna=False")
-
-        if isinstance(delegate, ExtensionArray):
-            return delegate.argmax()
-        else:
-            result = nanops.nanargmax(delegate, skipna=skipna)
-            # error: Incompatible return value type (got "Union[int, ndarray]", expected
-            # "int")
-            return result  # type: ignore[return-value]
+        return self.array.argmax(skipna=skipna)
 
     @doc(argmax, op="min", oppose="max", value="smallest")
     def argmin(
         self, axis: AxisInt | None = None, skipna: bool = True, *args, **kwargs
     ) -> int:
-        delegate = self._values
         nv.validate_minmax_axis(axis)
-        skipna = nv.validate_argmin_with_skipna(skipna, args, kwargs)
-
-        if skipna and len(delegate) > 0 and isna(delegate).all():
-            raise ValueError("Encountered all NA values")
-        elif not skipna and isna(delegate).any():
-            raise ValueError("Encountered an NA value with skipna=False")
-
-        if isinstance(delegate, ExtensionArray):
-            return delegate.argmin()
-        else:
-            result = nanops.nanargmin(delegate, skipna=skipna)
-            # error: Incompatible return value type (got "Union[int, ndarray]", expected
-            # "int")
-            return result  # type: ignore[return-value]
+        skipna = nv.validate_argmax_with_skipna(skipna, args, kwargs)
+        return self.array.argmin(skipna=skipna)
 
     def tolist(self) -> list:
         """

pandas/core/indexes/base.py

@@ -6976,10 +6976,11 @@ def argmin(self, axis=None, skipna: bool = True, *args, **kwargs) -> int:
 
         if not self._is_multi and self.hasnans:
             # Take advantage of cache
-            if self._isnan.all():
-                raise ValueError("Encountered all NA values")
-            elif not skipna:
+            if not skipna:
                 raise ValueError("Encountered an NA value with skipna=False")
+            elif self._isnan.all():
+                raise ValueError("Encountered all NA values")
+
         return super().argmin(skipna=skipna)
 
     @Appender(IndexOpsMixin.argmax.__doc__)
@@ -6989,10 +6990,10 @@ def argmax(self, axis=None, skipna: bool = True, *args, **kwargs) -> int:
 
         if not self._is_multi and self.hasnans:
             # Take advantage of cache
-            if self._isnan.all():
-                raise ValueError("Encountered all NA values")
-            elif not skipna:
+            if not skipna:
                 raise ValueError("Encountered an NA value with skipna=False")
+            elif self._isnan.all():
+                raise ValueError("Encountered all NA values")
         return super().argmax(skipna=skipna)
 
     def min(self, axis=None, skipna: bool = True, *args, **kwargs):

pandas/core/nanops.py

@@ -1439,20 +1439,15 @@ def _maybe_arg_null_out(
         return result
 
     if axis is None or not getattr(result, "ndim", False):
-        if skipna:
-            if mask.all():
-                raise ValueError("Encountered all NA values")
-        else:
-            if mask.any():
-                raise ValueError("Encountered an NA value with skipna=False")
+        if skipna and mask.all():
+            raise ValueError("Encountered all NA values")
+        elif not skipna and mask.any():
+            raise ValueError("Encountered an NA value with skipna=False")
     else:
-        na_mask = mask.all(axis)
-        if na_mask.any():
+        if skipna and mask.all(axis).any():
             raise ValueError("Encountered all NA values")
-        elif not skipna:
-            na_mask = mask.any(axis)
-            if na_mask.any():
-                raise ValueError("Encountered an NA value with skipna=False")
+        elif not skipna and mask.any(axis).any():
+            raise ValueError("Encountered an NA value with skipna=False")
     return result
 
 

pandas/tests/extension/base/methods.py

@@ -191,10 +191,10 @@ def test_argmax_argmin_no_skipna_notimplemented(self, data_missing_for_sorting):
         # GH#38733
         data = data_missing_for_sorting
 
-        with pytest.raises(NotImplementedError, match=""):
+        with pytest.raises(ValueError, match="Encountered an NA value"):
             data.argmin(skipna=False)
 
-        with pytest.raises(NotImplementedError, match=""):
+        with pytest.raises(ValueError, match="Encountered an NA value"):
             data.argmax(skipna=False)
 
     @pytest.mark.parametrize(

pandas/tests/frame/test_reductions.py

@@ -1066,7 +1066,7 @@ def test_idxmin(self, float_frame, int_frame, skipna, axis):
         frame.iloc[15:20, -2:] = np.nan
         for df in [frame, int_frame]:
             if (not skipna or axis == 1) and df is not int_frame:
-                if axis == 1:
+                if skipna:
                     msg = "Encountered all NA values"
                 else:
                     msg = "Encountered an NA value"
@@ -1116,7 +1116,7 @@ def test_idxmax(self, float_frame, int_frame, skipna, axis):
         frame.iloc[15:20, -2:] = np.nan
         for df in [frame, int_frame]:
             if (skipna is False or axis == 1) and df is frame:
-                if axis == 1:
+                if skipna:
                     msg = "Encountered all NA values"
                 else:
                     msg = "Encountered an NA value"

pandas/tests/reductions/test_reductions.py

@@ -171,9 +171,9 @@ def test_argminmax(self):
             obj.argmin()
         with pytest.raises(ValueError, match="Encountered all NA values"):
             obj.argmax()
-        with pytest.raises(ValueError, match="Encountered all NA values"):
+        with pytest.raises(ValueError, match="Encountered an NA value"):
             obj.argmin(skipna=False)
-        with pytest.raises(ValueError, match="Encountered all NA values"):
+        with pytest.raises(ValueError, match="Encountered an NA value"):
             obj.argmax(skipna=False)
 
         obj = Index([NaT, datetime(2011, 11, 1), datetime(2011, 11, 2), NaT])
@@ -189,9 +189,9 @@ def test_argminmax(self):
             obj.argmin()
         with pytest.raises(ValueError, match="Encountered all NA values"):
             obj.argmax()
-        with pytest.raises(ValueError, match="Encountered all NA values"):
+        with pytest.raises(ValueError, match="Encountered an NA value"):
             obj.argmin(skipna=False)
-        with pytest.raises(ValueError, match="Encountered all NA values"):
+        with pytest.raises(ValueError, match="Encountered an NA value"):
             obj.argmax(skipna=False)
 
     @pytest.mark.parametrize("op, expected_col", [["max", "a"], ["min", "b"]])
@@ -856,7 +856,8 @@ def test_idxmin(self):
 
         # all NaNs
         allna = string_series * np.nan
-        with pytest.raises(ValueError, match="Encountered all NA values"):
+        msg = "attempt to get argmin of an empty sequence"
+        with pytest.raises(ValueError, match=msg):
             allna.idxmin()
 
         # datetime64[ns]
@@ -888,7 +889,8 @@ def test_idxmax(self):
 
         # all NaNs
         allna = string_series * np.nan
-        with pytest.raises(ValueError, match="Encountered all NA values"):
+        msg = "attempt to get argmax of an empty sequence"
+        with pytest.raises(ValueError, match=msg):
             allna.idxmax()
 
         s = Series(date_range("20130102", periods=6))
@@ -1143,14 +1145,17 @@ def test_idxminmax_object_dtype(self, using_infer_string):
         if not using_infer_string:
             # attempting to compare np.nan with string raises
             ser3 = Series(["foo", "foo", "bar", "bar", None, np.nan, "baz"])
-            msg = "'>' not supported between instances of 'float' and 'str'"
-            with pytest.raises(TypeError, match=msg):
-                ser3.idxmax()
+            result = ser3.idxmax()
+            expected = 0
+            assert result == expected
+
             with pytest.raises(ValueError, match="Encountered an NA value"):
                 ser3.idxmax(skipna=False)
-            msg = "'<' not supported between instances of 'float' and 'str'"
-            with pytest.raises(TypeError, match=msg):
-                ser3.idxmin()
+
+            result = ser3.idxmin()
+            expected = 2
+            assert result == expected
+
             with pytest.raises(ValueError, match="Encountered an NA value"):
                 ser3.idxmin(skipna=False)