def agg_dict_like(self) -> DataFrame | Series:
"""
Compute aggregation in the case of a dict-like argument.
Returns
-------
Result of aggregation.
"""
from pandas import Index
from pandas.core.reshape.concat import concat
obj = self.obj
arg = cast(AggFuncTypeDict, self.f)
if not isinstance(obj, SelectionMixin):
# i.e. obj is Series or DataFrame
selected_obj = obj
selection = None
else:
selected_obj = obj._selected_obj
selection = obj._selection
arg = self.normalize_dictlike_arg("agg", selected_obj, arg)
if selected_obj.ndim == 1:
# key only used for output
colg = obj._gotitem(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: Iterable[Hashable]
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
if selected_obj.ndim == 2:
# keys are columns, so we can preserve names
ktu = Index(keys_to_use)
ktu._set_names(selected_obj.columns.names)
keys_to_use = ktu
axis = 0 if isinstance(obj, ABCSeries) else 1
result = concat({k: results[k]
for k in keys_to_use},
axis=axis,
keys=keys_to_use)
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
