@@ -1192,113 +1192,104 @@ def _wrap_applied_output(self, keys, values, not_indexed_same=False):
1192 1192
return self.obj._constructor()
1193 1193
elif isinstance(first_not_none, DataFrame):
1194 1194
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]
1207 1195 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 +
)
1216 1238 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]
1217 1262
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
1275 1266 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)
1289 1268 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)
1297 1288 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)
1300 1291 1301 -
return self._reindex_output(result)
1292 +
return self._reindex_output(result)
1302 1293 1303 1294
def _transform_general(
1304 1295
self, func, *args, engine="cython", engine_kwargs=None, **kwargs
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