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CLN: _wrap_applied_output (#36160)
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‎pandas/core/groupby/generic.py

+91-100
Original file line numberDiff line numberDiff line change
@@ -1192,113 +1192,104 @@ def _wrap_applied_output(self, keys, values, not_indexed_same=False):
11921192
return self.obj._constructor()
11931193
elif isinstance(first_not_none, DataFrame):
11941194
return self._concat_objects(keys, values, not_indexed_same=not_indexed_same)
1195-
else:
1196-
key_index = self.grouper.result_index if self.as_index else None
1197-
1198-
if isinstance(first_not_none, Series):
1199-
# this is to silence a DeprecationWarning
1200-
# TODO: Remove when default dtype of empty Series is object
1201-
kwargs = first_not_none._construct_axes_dict()
1202-
backup = create_series_with_explicit_dtype(
1203-
dtype_if_empty=object, **kwargs
1204-
)
1205-
1206-
values = [x if (x is not None) else backup for x in values]
12071195

1208-
v = values[0]
1209-
1210-
if not isinstance(v, (np.ndarray, Index, Series)) and self.as_index:
1211-
# values are not series or array-like but scalars
1212-
# self._selection_name not passed through to Series as the
1213-
# result should not take the name of original selection
1214-
# of columns
1215-
return self.obj._constructor_sliced(values, index=key_index)
1196+
key_index = self.grouper.result_index if self.as_index else None
1197+
1198+
if isinstance(first_not_none, Series):
1199+
# this is to silence a DeprecationWarning
1200+
# TODO: Remove when default dtype of empty Series is object
1201+
kwargs = first_not_none._construct_axes_dict()
1202+
backup = create_series_with_explicit_dtype(dtype_if_empty=object, **kwargs)
1203+
1204+
values = [x if (x is not None) else backup for x in values]
1205+
1206+
v = values[0]
1207+
1208+
if not isinstance(v, (np.ndarray, Index, Series)) and self.as_index:
1209+
# values are not series or array-like but scalars
1210+
# self._selection_name not passed through to Series as the
1211+
# result should not take the name of original selection
1212+
# of columns
1213+
return self.obj._constructor_sliced(values, index=key_index)
1214+
1215+
if isinstance(v, Series):
1216+
all_indexed_same = all_indexes_same((x.index for x in values))
1217+
1218+
# GH3596
1219+
# provide a reduction (Frame -> Series) if groups are
1220+
# unique
1221+
if self.squeeze:
1222+
applied_index = self._selected_obj._get_axis(self.axis)
1223+
singular_series = len(values) == 1 and applied_index.nlevels == 1
1224+
1225+
# assign the name to this series
1226+
if singular_series:
1227+
values[0].name = keys[0]
1228+
1229+
# GH2893
1230+
# we have series in the values array, we want to
1231+
# produce a series:
1232+
# if any of the sub-series are not indexed the same
1233+
# OR we don't have a multi-index and we have only a
1234+
# single values
1235+
return self._concat_objects(
1236+
keys, values, not_indexed_same=not_indexed_same
1237+
)
12161238

1239+
# still a series
1240+
# path added as of GH 5545
1241+
elif all_indexed_same:
1242+
from pandas.core.reshape.concat import concat
1243+
1244+
return concat(values)
1245+
1246+
if not all_indexed_same:
1247+
# GH 8467
1248+
return self._concat_objects(keys, values, not_indexed_same=True)
1249+
1250+
# Combine values
1251+
# vstack+constructor is faster than concat and handles MI-columns
1252+
stacked_values = np.vstack([np.asarray(v) for v in values])
1253+
1254+
if self.axis == 0:
1255+
index = key_index
1256+
columns = v.index.copy()
1257+
if columns.name is None:
1258+
# GH6124 - propagate name of Series when it's consistent
1259+
names = {v.name for v in values}
1260+
if len(names) == 1:
1261+
columns.name = list(names)[0]
12171262
else:
1218-
if isinstance(v, Series):
1219-
all_indexed_same = all_indexes_same((x.index for x in values))
1220-
1221-
# GH3596
1222-
# provide a reduction (Frame -> Series) if groups are
1223-
# unique
1224-
if self.squeeze:
1225-
applied_index = self._selected_obj._get_axis(self.axis)
1226-
singular_series = (
1227-
len(values) == 1 and applied_index.nlevels == 1
1228-
)
1229-
1230-
# assign the name to this series
1231-
if singular_series:
1232-
values[0].name = keys[0]
1233-
1234-
# GH2893
1235-
# we have series in the values array, we want to
1236-
# produce a series:
1237-
# if any of the sub-series are not indexed the same
1238-
# OR we don't have a multi-index and we have only a
1239-
# single values
1240-
return self._concat_objects(
1241-
keys, values, not_indexed_same=not_indexed_same
1242-
)
1243-
1244-
# still a series
1245-
# path added as of GH 5545
1246-
elif all_indexed_same:
1247-
from pandas.core.reshape.concat import concat
1248-
1249-
return concat(values)
1250-
1251-
if not all_indexed_same:
1252-
# GH 8467
1253-
return self._concat_objects(keys, values, not_indexed_same=True)
1254-
1255-
# Combine values
1256-
# vstack+constructor is faster than concat and handles MI-columns
1257-
stacked_values = np.vstack([np.asarray(v) for v in values])
1258-
1259-
if self.axis == 0:
1260-
index = key_index
1261-
columns = v.index.copy()
1262-
if columns.name is None:
1263-
# GH6124 - propagate name of Series when it's consistent
1264-
names = {v.name for v in values}
1265-
if len(names) == 1:
1266-
columns.name = list(names)[0]
1267-
else:
1268-
index = v.index
1269-
columns = key_index
1270-
stacked_values = stacked_values.T
1271-
1272-
result = self.obj._constructor(
1273-
stacked_values, index=index, columns=columns
1274-
)
1263+
index = v.index
1264+
columns = key_index
1265+
stacked_values = stacked_values.T
12751266

1276-
elif not self.as_index:
1277-
# We add grouping column below, so create a frame here
1278-
result = DataFrame(
1279-
values, index=key_index, columns=[self._selection]
1280-
)
1281-
else:
1282-
# GH#1738: values is list of arrays of unequal lengths
1283-
# fall through to the outer else clause
1284-
# TODO: sure this is right? we used to do this
1285-
# after raising AttributeError above
1286-
return self.obj._constructor_sliced(
1287-
values, index=key_index, name=self._selection_name
1288-
)
1267+
result = self.obj._constructor(stacked_values, index=index, columns=columns)
12891268

1290-
# if we have date/time like in the original, then coerce dates
1291-
# as we are stacking can easily have object dtypes here
1292-
so = self._selected_obj
1293-
if so.ndim == 2 and so.dtypes.apply(needs_i8_conversion).any():
1294-
result = _recast_datetimelike_result(result)
1295-
else:
1296-
result = result._convert(datetime=True)
1269+
elif not self.as_index:
1270+
# We add grouping column below, so create a frame here
1271+
result = DataFrame(values, index=key_index, columns=[self._selection])
1272+
else:
1273+
# GH#1738: values is list of arrays of unequal lengths
1274+
# fall through to the outer else clause
1275+
# TODO: sure this is right? we used to do this
1276+
# after raising AttributeError above
1277+
return self.obj._constructor_sliced(
1278+
values, index=key_index, name=self._selection_name
1279+
)
1280+
1281+
# if we have date/time like in the original, then coerce dates
1282+
# as we are stacking can easily have object dtypes here
1283+
so = self._selected_obj
1284+
if so.ndim == 2 and so.dtypes.apply(needs_i8_conversion).any():
1285+
result = _recast_datetimelike_result(result)
1286+
else:
1287+
result = result._convert(datetime=True)
12971288

1298-
if not self.as_index:
1299-
self._insert_inaxis_grouper_inplace(result)
1289+
if not self.as_index:
1290+
self._insert_inaxis_grouper_inplace(result)
13001291

1301-
return self._reindex_output(result)
1292+
return self._reindex_output(result)
13021293

13031294
def _transform_general(
13041295
self, func, *args, engine="cython", engine_kwargs=None, **kwargs

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