REF: Move agg helpers into apply (#39498)

Author: rhshadrach

pandas/core/aggregation.py

@@ -27,18 +27,16 @@
     AggFuncType,
     AggFuncTypeBase,
     AggFuncTypeDict,
-    AggObjType,
     Axis,
     FrameOrSeries,
     FrameOrSeriesUnion,
 )
 
-from pandas.core.dtypes.cast import is_nested_object
 from pandas.core.dtypes.common import is_dict_like, is_list_like
-from pandas.core.dtypes.generic import ABCDataFrame, ABCNDFrame, ABCSeries
+from pandas.core.dtypes.generic import ABCDataFrame, ABCSeries
 
 from pandas.core.algorithms import safe_sort
-from pandas.core.base import DataError, SpecificationError
+from pandas.core.base import SpecificationError
 import pandas.core.common as com
 from pandas.core.indexes.api import Index
 
@@ -532,215 +530,3 @@ def transform_str_or_callable(
         return obj.apply(func, args=args, **kwargs)
     except Exception:
         return func(obj, *args, **kwargs)
-
-
-def agg_list_like(
-    obj: AggObjType,
-    arg: List[AggFuncTypeBase],
-    _axis: int,
-) -> FrameOrSeriesUnion:
-    """
-    Compute aggregation in the case of a list-like argument.
-
-    Parameters
-    ----------
-    obj : Pandas object to compute aggregation on.
-    arg : list
-        Aggregations to compute.
-    _axis : int, 0 or 1
-        Axis to compute aggregation on.
-
-    Returns
-    -------
-    Result of aggregation.
-    """
-    from pandas.core.reshape.concat import concat
-
-    if _axis != 0:
-        raise NotImplementedError("axis other than 0 is not supported")
-
-    if obj._selected_obj.ndim == 1:
-        selected_obj = obj._selected_obj
-    else:
-        selected_obj = obj._obj_with_exclusions
-
-    results = []
-    keys = []
-
-    # degenerate case
-    if selected_obj.ndim == 1:
-        for a in arg:
-            colg = obj._gotitem(selected_obj.name, ndim=1, subset=selected_obj)
-            try:
-                new_res = colg.aggregate(a)
-
-            except TypeError:
-                pass
-            else:
-                results.append(new_res)
-
-                # make sure we find a good name
-                name = com.get_callable_name(a) or a
-                keys.append(name)
-
-    # multiples
-    else:
-        for index, col in enumerate(selected_obj):
-            colg = obj._gotitem(col, ndim=1, subset=selected_obj.iloc[:, index])
-            try:
-                new_res = colg.aggregate(arg)
-            except (TypeError, DataError):
-                pass
-            except ValueError as err:
-                # cannot aggregate
-                if "Must produce aggregated value" in str(err):
-                    # raised directly in _aggregate_named
-                    pass
-                elif "no results" in str(err):
-                    # raised directly in _aggregate_multiple_funcs
-                    pass
-                else:
-                    raise
-            else:
-                results.append(new_res)
-                keys.append(col)
-
-    # if we are empty
-    if not len(results):
-        raise ValueError("no results")
-
-    try:
-        return concat(results, keys=keys, axis=1, sort=False)
-    except TypeError as err:
-
-        # we are concatting non-NDFrame objects,
-        # e.g. a list of scalars
-
-        from pandas import Series
-
-        result = Series(results, index=keys, name=obj.name)
-        if is_nested_object(result):
-            raise ValueError(
-                "cannot combine transform and aggregation operations"
-            ) from err
-        return result
-
-
-def agg_dict_like(
-    obj: AggObjType,
-    arg: AggFuncTypeDict,
-    _axis: int,
-) -> FrameOrSeriesUnion:
-    """
-    Compute aggregation in the case of a dict-like argument.
-
-    Parameters
-    ----------
-    obj : Pandas object to compute aggregation on.
-    arg : dict
-        label-aggregation pairs to compute.
-    _axis : int, 0 or 1
-        Axis to compute aggregation on.
-
-    Returns
-    -------
-    Result of aggregation.
-    """
-    is_aggregator = lambda x: isinstance(x, (list, tuple, dict))
-
-    if _axis != 0:  # pragma: no cover
-        raise ValueError("Can only pass dict with axis=0")
-
-    selected_obj = obj._selected_obj
-
-    # if we have a dict of any non-scalars
-    # eg. {'A' : ['mean']}, normalize all to
-    # be list-likes
-    # Cannot use arg.values() because arg may be a Series
-    if any(is_aggregator(x) for _, x in arg.items()):
-        new_arg: AggFuncTypeDict = {}
-        for k, v in arg.items():
-            if not isinstance(v, (tuple, list, dict)):
-                new_arg[k] = [v]
-            else:
-                new_arg[k] = v
-
-            # the keys must be in the columns
-            # for ndim=2, or renamers for ndim=1
-
-            # ok for now, but deprecated
-            # {'A': { 'ra': 'mean' }}
-            # {'A': { 'ra': ['mean'] }}
-            # {'ra': ['mean']}
-
-            # not ok
-            # {'ra' : { 'A' : 'mean' }}
-            if isinstance(v, dict):
-                raise SpecificationError("nested renamer is not supported")
-            elif isinstance(selected_obj, ABCSeries):
-                raise SpecificationError("nested renamer is not supported")
-            elif (
-                isinstance(selected_obj, ABCDataFrame) and k not in selected_obj.columns
-            ):
-                raise KeyError(f"Column '{k}' does not exist!")
-
-        arg = new_arg
-
-    else:
-        # deprecation of renaming keys
-        # GH 15931
-        keys = list(arg.keys())
-        if isinstance(selected_obj, ABCDataFrame) and len(
-            selected_obj.columns.intersection(keys)
-        ) != len(keys):
-            cols = list(
-                safe_sort(
-                    list(set(keys) - set(selected_obj.columns.intersection(keys))),
-                )
-            )
-            raise SpecificationError(f"Column(s) {cols} do not exist")
-
-    from pandas.core.reshape.concat import concat
-
-    if selected_obj.ndim == 1:
-        # key only used for output
-        colg = obj._gotitem(obj._selection, ndim=1)
-        results = {key: colg.agg(how) for key, how in arg.items()}
-    else:
-        # key used for column selection and output
-        results = {key: obj._gotitem(key, ndim=1).agg(how) for key, how in arg.items()}
-
-    # set the final keys
-    keys = list(arg.keys())
-
-    # Avoid making two isinstance calls in all and any below
-    is_ndframe = [isinstance(r, ABCNDFrame) for r in results.values()]
-
-    # combine results
-    if all(is_ndframe):
-        keys_to_use = [k for k in keys if not results[k].empty]
-        # Have to check, if at least one DataFrame is not empty.
-        keys_to_use = keys_to_use if keys_to_use != [] else keys
-        axis = 0 if isinstance(obj, ABCSeries) else 1
-        result = concat({k: results[k] for k in keys_to_use}, axis=axis)
-    elif any(is_ndframe):
-        # There is a mix of NDFrames and scalars
-        raise ValueError(
-            "cannot perform both aggregation "
-            "and transformation operations "
-            "simultaneously"
-        )
-    else:
-        from pandas import Series
-
-        # we have a dict of scalars
-        # GH 36212 use name only if obj is a series
-        if obj.ndim == 1:
-            obj = cast("Series", obj)
-            name = obj.name
-        else:
-            name = None
-
-        result = Series(results, name=name)
-
-    return result