CLN: Enforce deprecation of groupby.idxmin/idxmax with skipna=False not raising

doc/source/whatsnew/v3.0.0.rst

@@ -189,6 +189,7 @@ Other Deprecations
 
 Removal of prior version deprecations/changes
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+- :class:`.DataFrameGroupBy.idxmin`, :class:`.DataFrameGroupBy.idxmax`, :class:`.SeriesGroupBy.idxmin`, and :class:`.SeriesGroupBy.idxmax` will now raise a ``ValueError`` when used with ``skipna=False`` and an NA value is encountered (:issue:`10694`)
 - :func:`read_excel`, :func:`read_json`, :func:`read_html`, and :func:`read_xml` no longer accept raw string or byte representation of the data. That type of data must be wrapped in a :py:class:`StringIO` or :py:class:`BytesIO` (:issue:`53767`)
 - :meth:`Series.dt.to_pydatetime` now returns a :class:`Series` of :py:class:`datetime.datetime` objects (:issue:`52459`)
 - :meth:`SeriesGroupBy.agg` no longer pins the name of the group to the input passed to the provided ``func`` (:issue:`51703`)

pandas/core/groupby/generic.py

@@ -1179,8 +1179,7 @@ def idxmin(self, skipna: bool = True) -> Series:
         Parameters
         ----------
         skipna : bool, default True
-            Exclude NA/null values. If the entire Series is NA, the result
-            will be NA.
+            Exclude NA values.
 
         Returns
         -------
@@ -1190,7 +1189,7 @@ def idxmin(self, skipna: bool = True) -> Series:
         Raises
         ------
         ValueError
-            If the Series is empty.
+            If the Series is empty or skipna=False and any value is NA.
 
         See Also
         --------
@@ -1233,8 +1232,7 @@ def idxmax(self, skipna: bool = True) -> Series:
         Parameters
         ----------
         skipna : bool, default True
-            Exclude NA/null values. If the entire Series is NA, the result
-            will be NA.
+            Exclude NA values.
 
         Returns
         -------
@@ -1244,7 +1242,7 @@ def idxmax(self, skipna: bool = True) -> Series:
         Raises
         ------
         ValueError
-            If the Series is empty.
+            If the Series is empty or skipna=False and any value is NA.
 
         See Also
         --------
@@ -2165,13 +2163,10 @@ def idxmax(
         """
         Return index of first occurrence of maximum in each group.
 
-        NA/null values are excluded.
-
         Parameters
         ----------
         skipna : bool, default True
-            Exclude NA/null values. If an entire row/column is NA, the result
-            will be NA.
+            Exclude NA values.
         numeric_only : bool, default False
             Include only `float`, `int` or `boolean` data.
 
@@ -2185,7 +2180,7 @@ def idxmax(
         Raises
         ------
         ValueError
-            * If the row/column is empty
+            * If a column is empty or skipna=False and any value is NA.
 
         See Also
         --------
@@ -2230,13 +2225,10 @@ def idxmin(
         """
         Return index of first occurrence of minimum in each group.
 
-        NA/null values are excluded.
-
         Parameters
         ----------
         skipna : bool, default True
-            Exclude NA/null values. If an entire row/column is NA, the result
-            will be NA.
+            Exclude NA values.
         numeric_only : bool, default False
             Include only `float`, `int` or `boolean` data.
 
@@ -2250,7 +2242,7 @@ def idxmin(
         Raises
         ------
         ValueError
-            * If the row/column is empty
+            * If a column is empty or skipna=False and any value is NA.
 
         See Also
         --------

pandas/core/groupby/groupby.py

@@ -5553,15 +5553,11 @@ def _idxmax_idxmin(
                     f"Can't get {how} of an empty group due to unobserved categories. "
                     "Specify observed=True in groupby instead."
                 )
-        elif not skipna:
-            if self._obj_with_exclusions.isna().any(axis=None):
-                warnings.warn(
-                    f"The behavior of {type(self).__name__}.{how} with all-NA "
-                    "values, or any-NA and skipna=False, is deprecated. In a future "
-                    "version this will raise ValueError",
-                    FutureWarning,
-                    stacklevel=find_stack_level(),
-                )
+        elif not skipna and self._obj_with_exclusions.isna().any(axis=None):
+            raise ValueError(
+                f"{type(self).__name__}.{how} with skipna=False encountered an NA "
+                f"value."
+            )
 
         result = self._agg_general(
             numeric_only=numeric_only,

pandas/tests/groupby/test_reductions.py

@@ -291,16 +291,14 @@ def test_idxmin_idxmax_extremes_skipna(skipna, how, float_numpy_dtype):
     )
     gb = df.groupby("a")
 
-    warn = None if skipna else FutureWarning
-    msg = f"The behavior of DataFrameGroupBy.{how} with all-NA values"
-    with tm.assert_produces_warning(warn, match=msg):
-        result = getattr(gb, how)(skipna=skipna)
-    if skipna:
-        values = [1, 3, 4, 6, np.nan]
-    else:
-        values = np.nan
+    if not skipna:
+        msg = f"DataFrameGroupBy.{how} with skipna=False"
+        with pytest.raises(ValueError, match=msg):
+            getattr(gb, how)(skipna=skipna)
+        return
+    result = getattr(gb, how)(skipna=skipna)
     expected = DataFrame(
-        {"b": values}, index=pd.Index(range(1, 6), name="a", dtype="intp")
+        {"b": [1, 3, 4, 6, np.nan]}, index=pd.Index(range(1, 6), name="a", dtype="intp")
     )
     tm.assert_frame_equal(result, expected)
 

pandas/tests/groupby/transform/test_transform.py

@@ -1525,10 +1525,11 @@ def test_idxmin_idxmax_transform_args(how, skipna, numeric_only):
     # GH#55268 - ensure *args are passed through when calling transform
     df = DataFrame({"a": [1, 1, 1, 2], "b": [3.0, 4.0, np.nan, 6.0], "c": list("abcd")})
     gb = df.groupby("a")
-    warn = None if skipna else FutureWarning
-    msg = f"The behavior of DataFrameGroupBy.{how} with .* any-NA and skipna=False"
-    with tm.assert_produces_warning(warn, match=msg):
+    if skipna:
         result = gb.transform(how, skipna, numeric_only)
-    with tm.assert_produces_warning(warn, match=msg):
         expected = gb.transform(how, skipna=skipna, numeric_only=numeric_only)
-    tm.assert_frame_equal(result, expected)
+        tm.assert_frame_equal(result, expected)
+    else:
+        msg = f"DataFrameGroupBy.{how} with skipna=False encountered an NA value"
+        with pytest.raises(ValueError, match=msg):
+            gb.transform(how, skipna, numeric_only)