CLN: Enforce deprecation of DataFrame.idxmin/max with EAs and axis=1 (#62201)

Author: rhshadrach

pandas/core/frame.py

@@ -71,7 +71,6 @@
 )
 from pandas.util._exceptions import (
     find_stack_level,
-    rewrite_warning,
 )
 from pandas.util._validators import (
     validate_ascending,
@@ -11926,25 +11925,13 @@ def _get_data() -> DataFrame:
                     row_index = np.tile(np.arange(nrows), ncols)
                     col_index = np.repeat(np.arange(ncols), nrows)
                     ser = Series(arr, index=col_index, copy=False)
-                    # GroupBy will raise a warning with SeriesGroupBy as the object,
-                    # likely confusing users
-                    with rewrite_warning(
-                        target_message=(
-                            f"The behavior of SeriesGroupBy.{name} with all-NA values"
-                        ),
-                        target_category=FutureWarning,
-                        new_message=(
-                            f"The behavior of {type(self).__name__}.{name} with all-NA "
-                            "values, or any-NA and skipna=False, is deprecated. In "
-                            "a future version this will raise ValueError"
-                        ),
-                    ):
-                        result = ser.groupby(row_index).agg(name, **kwds)
+                    if name == "all":
+                        # Behavior here appears incorrect; preserving
+                        # for backwards compatibility for now.
+                        # See https://github.com/pandas-dev/pandas/issues/57171
+                        skipna = True
+                    result = ser.groupby(row_index).agg(name, **kwds, skipna=skipna)
                     result.index = df.index
-                    if not skipna and name not in ("any", "all"):
-                        mask = df.isna().to_numpy(dtype=np.bool_).any(axis=1)
-                        other = -1 if name in ("idxmax", "idxmin") else lib.no_default
-                        result = result.mask(mask, other)
                     return result
 
             df = df.T
@@ -13258,13 +13245,11 @@ def idxmin(
         # indices will always be np.ndarray since axis is not N
 
         if (indices == -1).any():
-            warnings.warn(
-                f"The behavior of {type(self).__name__}.idxmin 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(),
-            )
+            if skipna:
+                msg = "Encountered all NA values"
+            else:
+                msg = "Encountered an NA values with skipna=False"
+            raise ValueError(msg)
 
         index = data._get_axis(axis)
         result = algorithms.take(
@@ -13365,13 +13350,11 @@ def idxmax(
         # indices will always be 1d array since axis is not None
 
         if (indices == -1).any():
-            warnings.warn(
-                f"The behavior of {type(self).__name__}.idxmax 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(),
-            )
+            if skipna:
+                msg = "Encountered all NA values"
+            else:
+                msg = "Encountered an NA values with skipna=False"
+            raise ValueError(msg)
 
         index = data._get_axis(axis)
         result = algorithms.take(

pandas/core/groupby/groupby.py

@@ -5703,10 +5703,7 @@ def _idxmax_idxmin(
                     "Specify observed=True in groupby instead."
                 )
         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."
-            )
+            raise ValueError(f"{how} with skipna=False encountered an NA value.")
 
         result = self._agg_general(
             numeric_only=numeric_only,
@@ -5724,8 +5721,7 @@ def _wrap_idxmax_idxmin(
             result = res.astype(index.dtype)
         elif skipna and res.lt(0).any(axis=None):
             raise ValueError(
-                f"{type(self).__name__}.{how} with skipna=True encountered all NA "
-                f"values in a group."
+                f"{how} with skipna=True encountered all NA values in a group."
             )
         else:
             if isinstance(index, MultiIndex):

pandas/tests/frame/test_reductions.py

@@ -2160,9 +2160,7 @@ def test_numeric_ea_axis_1(method, skipna, min_count, any_numeric_ea_dtype):
         kwargs["min_count"] = min_count
 
     if not skipna and method in ("idxmax", "idxmin"):
-        # GH#57745 - EAs use groupby for axis=1 which still needs a proper deprecation.
-        msg = f"The behavior of DataFrame.{method} with all-NA values"
-        with tm.assert_produces_warning(FutureWarning, match=msg):
+        with pytest.raises(ValueError, match="encountered an NA value"):
             getattr(df, method)(axis=1, **kwargs)
         with pytest.raises(ValueError, match="Encountered an NA value"):
             getattr(expected_df, method)(axis=1, **kwargs)

pandas/tests/groupby/test_reductions.py

@@ -291,7 +291,7 @@ def test_idxmin_idxmax_extremes_skipna(skipna, how, float_numpy_dtype):
     gb = df.groupby("a")
 
     if not skipna:
-        msg = f"DataFrameGroupBy.{how} with skipna=False"
+        msg = f"{how} with skipna=False"
         with pytest.raises(ValueError, match=msg):
             getattr(gb, how)(skipna=skipna)
         return

pandas/tests/groupby/transform/test_transform.py

@@ -1494,7 +1494,7 @@ def test_idxmin_idxmax_transform_args(how, skipna, numeric_only):
         expected = gb.transform(how, skipna=skipna, numeric_only=numeric_only)
         tm.assert_frame_equal(result, expected)
     else:
-        msg = f"DataFrameGroupBy.{how} with skipna=False encountered an NA value"
+        msg = f"{how} with skipna=False encountered an NA value"
         with pytest.raises(ValueError, match=msg):
             gb.transform(how, skipna, numeric_only)