@@ -2015,10 +2015,14 @@ def _transform(self, func, *args, engine=None, engine_kwargs=None, **kwargs):
20152015 with com .temp_setattr (self , "as_index" , True ):
20162016 # GH#49834 - result needs groups in the index for
20172017 # _wrap_transform_fast_result
2018- if engine is not None :
2019- kwargs ["engine" ] = engine
2020- kwargs ["engine_kwargs" ] = engine_kwargs
2021- result = getattr (self , func )(* args , ** kwargs )
2018+ if func in ["idxmin" , "idxmax" ]:
2019+ func = cast (Literal ["idxmin" , "idxmax" ], func )
2020+ result = self ._idxmax_idxmin (func , True , * args , ** kwargs )
2021+ else :
2022+ if engine is not None :
2023+ kwargs ["engine" ] = engine
2024+ kwargs ["engine_kwargs" ] = engine_kwargs
2025+ result = getattr (self , func )(* args , ** kwargs )
20222026
20232027 return self ._wrap_transform_fast_result (result )
20242028
@@ -5720,6 +5724,113 @@ def sample(
57205724 sampled_indices = np .concatenate (sampled_indices )
57215725 return self ._selected_obj .take (sampled_indices , axis = self .axis )
57225726
5727+ def _idxmax_idxmin (
5728+ self ,
5729+ how : Literal ["idxmax" , "idxmin" ],
5730+ ignore_unobserved : bool = False ,
5731+ axis : Axis | None | lib .NoDefault = lib .no_default ,
5732+ skipna : bool = True ,
5733+ numeric_only : bool = False ,
5734+ ):
5735+ """Compute idxmax/idxmin.
5736+
5737+ Parameters
5738+ ----------
5739+ how: {"idxmin", "idxmax"}
5740+ Whether to compute idxmin or idxmax.
5741+ axis : {{0 or 'index', 1 or 'columns'}}, default None
5742+ The axis to use. 0 or 'index' for row-wise, 1 or 'columns' for column-wise.
5743+ If axis is not provided, grouper's axis is used.
5744+ numeric_only : bool, default False
5745+ Include only float, int, boolean columns.
5746+ skipna : bool, default True
5747+ Exclude NA/null values. If an entire row/column is NA, the result
5748+ will be NA.
5749+ ignore_unobserved : bool, default False
5750+ When True and an unobserved group is encountered, do not raise. This used
5751+ for transform where unobserved groups do not play an impact on the result.
5752+
5753+ Returns
5754+ -------
5755+ Series or DataFrame
5756+ idxmax or idxmin for the groupby operation.
5757+ """
5758+ if axis is not lib .no_default :
5759+ if axis is None :
5760+ axis = self .axis
5761+ axis = self .obj ._get_axis_number (axis )
5762+ self ._deprecate_axis (axis , how )
5763+ else :
5764+ axis = self .axis
5765+
5766+ if not self .observed and any (
5767+ ping ._passed_categorical for ping in self .grouper .groupings
5768+ ):
5769+ expected_len = np .prod (
5770+ [len (ping .group_index ) for ping in self .grouper .groupings ]
5771+ )
5772+ if len (self .grouper .groupings ) == 1 :
5773+ result_len = len (self .grouper .groupings [0 ].grouping_vector .unique ())
5774+ else :
5775+ # result_index only contains observed groups in this case
5776+ result_len = len (self .grouper .result_index )
5777+ assert result_len <= expected_len
5778+ has_unobserved = result_len < expected_len
5779+
5780+ raise_err : bool | np .bool_ = not ignore_unobserved and has_unobserved
5781+ # Only raise an error if there are columns to compute; otherwise we return
5782+ # an empty DataFrame with an index (possibly including unobserved) but no
5783+ # columns
5784+ data = self ._obj_with_exclusions
5785+ if raise_err and isinstance (data , DataFrame ):
5786+ if numeric_only :
5787+ data = data ._get_numeric_data ()
5788+ raise_err = len (data .columns ) > 0
5789+ else :
5790+ raise_err = False
5791+ if raise_err :
5792+ raise ValueError (
5793+ f"Can't get { how }
5794+ "Specify observed=True in groupby instead."
5795+ )
5796+
5797+ try :
5798+ if self .obj .ndim == 1 :
5799+ result = self ._op_via_apply (how , skipna = skipna )
5800+ else :
5801+
5802+ def func (df ):
5803+ method = getattr (df , how )
5804+ return method (axis = axis , skipna = skipna , numeric_only = numeric_only )
5805+
5806+ func .__name__ = how
5807+ result = self ._python_apply_general (
5808+ func , self ._obj_with_exclusions , not_indexed_same = True
5809+ )
5810+ except ValueError as err :
5811+ name = "argmax" if how == "idxmax" else "argmin"
5812+ if f"attempt to get { name } in str (err ):
5813+ raise ValueError (
5814+ f"Can't get { how }
5815+ "Specify observed=True in groupby instead."
5816+ ) from None
5817+ raise
5818+
5819+ result = result .astype (self .obj .index .dtype ) if result .empty else result
5820+
5821+ if not skipna :
5822+ has_na_value = result .isnull ().any (axis = None )
5823+ if has_na_value :
5824+ warnings .warn (
5825+ f"The behavior of { type (self ).__name__ } { how }
5826+ "values, or any-NA and skipna=False, is deprecated. In a future "
5827+ "version this will raise ValueError" ,
5828+ FutureWarning ,
5829+ stacklevel = find_stack_level (),
5830+ )
5831+
5832+ return result
5833+
57235834
57245835@doc (GroupBy )
57255836def get_groupby (
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