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REF: _setitem_with_indexer_split_path #37521

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Merged
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Oct 31, 2020
Merged

REF: _setitem_with_indexer_split_path #37521

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142 changes: 71 additions & 71 deletions pandas/core/indexing.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1625,94 +1625,94 @@ def _setitem_with_indexer(self, indexer, value):
self._setitem_with_indexer_missing(indexer, value)
return

# set
item_labels = self.obj._get_axis(info_axis)

# align and set the values
if take_split_path:
# We have to operate column-wise
self._setitem_with_indexer_split_path(indexer, value)
else:
self._setitem_single_block(indexer, value)

# Above we only set take_split_path to True for 2D cases
assert self.ndim == 2
assert info_axis == 1
def _setitem_with_indexer_split_path(self, indexer, value):
"""
Setitem column-wise.
"""
# Above we only set take_split_path to True for 2D cases
assert self.ndim == 2

if not isinstance(indexer, tuple):
indexer = _tuplify(self.ndim, indexer)
if not isinstance(indexer, tuple):
indexer = _tuplify(self.ndim, indexer)

if isinstance(value, ABCSeries):
value = self._align_series(indexer, value)
if isinstance(value, ABCSeries):
value = self._align_series(indexer, value)

info_idx = indexer[info_axis]
if is_integer(info_idx):
info_idx = [info_idx]
labels = item_labels[info_idx]
info_idx = indexer[1]
if is_integer(info_idx):
info_idx = [info_idx]
labels = self.obj.columns[info_idx]

# Ensure we have something we can iterate over
ilocs = self._ensure_iterable_column_indexer(indexer[1])
# Ensure we have something we can iterate over
ilocs = self._ensure_iterable_column_indexer(indexer[1])

plane_indexer = indexer[:1]
lplane_indexer = length_of_indexer(plane_indexer[0], self.obj.index)
# lplane_indexer gives the expected length of obj[indexer[0]]
plane_indexer = indexer[:1]
lplane_indexer = length_of_indexer(plane_indexer[0], self.obj.index)
# lplane_indexer gives the expected length of obj[indexer[0]]

if len(labels) == 1:
# We can operate on a single column
if len(labels) == 1:
# We can operate on a single column

# require that we are setting the right number of values that
# we are indexing
if is_list_like_indexer(value) and 0 != lplane_indexer != len(value):
# Exclude zero-len for e.g. boolean masking that is all-false
raise ValueError(
"cannot set using a multi-index "
"selection indexer with a different "
"length than the value"
)

# we need an iterable, with a ndim of at least 1
# eg. don't pass through np.array(0)
if is_list_like_indexer(value) and getattr(value, "ndim", 1) > 0:

# we have an equal len Frame
if isinstance(value, ABCDataFrame):
self._setitem_with_indexer_frame_value(indexer, value)

# we have an equal len ndarray/convertible to our labels
# hasattr first, to avoid coercing to ndarray without reason.
# But we may be relying on the ndarray coercion to check ndim.
# Why not just convert to an ndarray earlier on if needed?
elif np.ndim(value) == 2:
self._setitem_with_indexer_2d_value(indexer, value)

elif (
len(labels) == 1
and lplane_indexer == len(value)
and not is_scalar(plane_indexer[0])
):
# we have an equal len list/ndarray
# We only get here with len(labels) == len(ilocs) == 1
self._setitem_single_column(ilocs[0], value, plane_indexer)
# require that we are setting the right number of values that
# we are indexing
if is_list_like_indexer(value) and 0 != lplane_indexer != len(value):
# Exclude zero-len for e.g. boolean masking that is all-false
raise ValueError(
"cannot set using a multi-index "
"selection indexer with a different "
"length than the value"
)

elif lplane_indexer == 0 and len(value) == len(self.obj.index):
# We get here in one case via .loc with a all-False mask
pass
# we need an iterable, with a ndim of at least 1
# eg. don't pass through np.array(0)
if is_list_like_indexer(value) and getattr(value, "ndim", 1) > 0:

# we have an equal len Frame
if isinstance(value, ABCDataFrame):
self._setitem_with_indexer_frame_value(indexer, value)

# we have an equal len ndarray/convertible to our labels
# hasattr first, to avoid coercing to ndarray without reason.
# But we may be relying on the ndarray coercion to check ndim.
# Why not just convert to an ndarray earlier on if needed?
elif np.ndim(value) == 2:
self._setitem_with_indexer_2d_value(indexer, value)

elif (
len(labels) == 1
and lplane_indexer == len(value)
and not is_scalar(plane_indexer[0])
):
# we have an equal len list/ndarray
# We only get here with len(labels) == len(ilocs) == 1
self._setitem_single_column(ilocs[0], value, plane_indexer)

else:
# per-label values
if len(ilocs) != len(value):
raise ValueError(
"Must have equal len keys and value "
"when setting with an iterable"
)
elif lplane_indexer == 0 and len(value) == len(self.obj.index):
# We get here in one case via .loc with a all-False mask
pass

for loc, v in zip(ilocs, value):
self._setitem_single_column(loc, v, plane_indexer)
else:
# per-label values
if len(ilocs) != len(value):
raise ValueError(
"Must have equal len keys and value "
"when setting with an iterable"
)

# scalar value
for loc in ilocs:
self._setitem_single_column(loc, value, plane_indexer)

for loc, v in zip(ilocs, value):
self._setitem_single_column(loc, v, plane_indexer)
else:
self._setitem_single_block(indexer, value)

# scalar value
for loc in ilocs:
self._setitem_single_column(loc, value, plane_indexer)

def _setitem_with_indexer_2d_value(self, indexer, value):
# We get here with np.ndim(value) == 2, excluding DataFrame,
Expand Down