def get_new_columns(self):
if self.value_columns is None:
if self.lift == 0:
return self.removed_level
lev = self.removed_level
return lev.insert(0, _get_na_value(lev.dtype.type))
stride = len(self.removed_level) + self.lift
width = len(self.value_columns)
propagator = np.repeat(np.arange(width), stride)
if isinstance(self.value_columns, MultiIndex):
new_levels = self.value_columns.levels + (self.removed_level, )
new_names = self.value_columns.names + (self.removed_name, )
new_labels = [
lab.take(propagator) for lab in self.value_columns.labels
]
else:
new_levels = [self.value_columns, self.removed_level]
new_names = [self.value_columns.name, self.removed_name]
new_labels = [propagator]
new_labels.append(np.tile(np.arange(stride) - self.lift, width))
return MultiIndex(levels=new_levels,
labels=new_labels,
names=new_names,
verify_integrity=False)
def get_new_columns(self):
if self.value_columns is None:
if self.lift == 0:
return self.removed_level
lev = self.removed_level
return lev.insert(0, _get_na_value(lev.dtype.type))
stride = len(self.removed_level) + self.lift
width = len(self.value_columns)
propagator = np.repeat(np.arange(width), stride)
if isinstance(self.value_columns, MultiIndex):
new_levels = self.value_columns.levels + (self.removed_level,)
new_names = self.value_columns.names + (self.removed_name,)
new_labels = [lab.take(propagator)
for lab in self.value_columns.labels]
else:
new_levels = [self.value_columns, self.removed_level]
new_names = [self.value_columns.name, self.removed_name]
new_labels = [propagator]
new_labels.append(np.tile(np.arange(stride) - self.lift, width))
return MultiIndex(levels=new_levels, labels=new_labels,
names=new_names, verify_integrity=False)
def get_new_index(self):
result_labels = [lab.take(self.compressor) for lab in self.sorted_labels[:-1]]
# construct the new index
if len(self.new_index_levels) == 1:
lev, lab = self.new_index_levels[0], result_labels[0]
if (lab == -1).any():
lev = lev.insert(len(lev), _get_na_value(lev.dtype.type))
return lev.take(lab)
return MultiIndex(
levels=self.new_index_levels, labels=result_labels, names=self.new_index_names, verify_integrity=False
)
def get_new_index(self):
result_labels = [lab.take(self.compressor)
for lab in self.sorted_labels[:-1]]
# construct the new index
if len(self.new_index_levels) == 1:
lev, lab = self.new_index_levels[0], result_labels[0]
if (lab == -1).any():
lev = lev.insert(len(lev), _get_na_value(lev.dtype.type))
return lev.take(lab)
return MultiIndex(levels=self.new_index_levels, labels=result_labels,
names=self.new_index_names, verify_integrity=False)
def _make_new_index(lev, lab):
from pandas.core.index import Index, _get_na_value
nan = _get_na_value(lev.dtype.type)
vals = lev.values.astype('object')
vals = np.insert(vals, 0, nan) if lab is None else \
np.insert(vals, len(vals), nan).take(lab)
try:
vals = vals.astype(lev.dtype, subok=False, copy=False)
except ValueError:
return Index(vals, **lev._get_attributes_dict())
return lev._shallow_copy(vals)
def _make_new_index(lev, lab):
from pandas.core.index import Index, _get_na_value
nan = _get_na_value(lev.dtype.type)
vals = lev.values.astype('object')
vals = np.insert(vals, 0, nan) if lab is None else \
np.insert(vals, len(vals), nan).take(lab)
try:
vals = vals.astype(lev.dtype, subok=False, copy=False)
except ValueError:
return Index(vals, **lev._get_attributes_dict())
return lev._shallow_copy(vals)
def _make_index_array_level(lev, lab):
""" create the combined index array, preserving nans, return an array """
mask = lab == -1
if not mask.any():
return lev
l = np.arange(len(lab))
mask_labels = np.empty(len(mask[mask]), dtype=object)
mask_labels.fill(_get_na_value(lev.dtype.type))
mask_indexer = com._ensure_int64(l[mask])
labels = lev
labels_indexer = com._ensure_int64(l[~mask])
new_labels = np.empty(tuple([len(lab)]), dtype=object)
new_labels[labels_indexer] = labels
new_labels[mask_indexer] = mask_labels
return new_labels
def _make_index_array_level(lev, lab):
""" create the combined index array, preserving nans, return an array """
mask = lab == -1
if not mask.any():
return lev
l = np.arange(len(lab))
mask_labels = np.empty(len(mask[mask]), dtype=object)
mask_labels.fill(_get_na_value(lev.dtype.type))
mask_indexer = com._ensure_int64(l[mask])
labels = lev
labels_indexer = com._ensure_int64(l[~mask])
new_labels = np.empty(tuple([len(lab)]), dtype=object)
new_labels[labels_indexer] = labels
new_labels[mask_indexer] = mask_labels
return new_labels
def get_new_columns(self):
if self.value_columns is None:
if self.lift == 0:
return self.removed_level
lev = self.removed_level
return lev.insert(0, _get_na_value(lev.dtype.type))
stride = len(self.removed_level) + self.lift
width = len(self.value_columns)
propagator = np.repeat(np.arange(width), stride)
if isinstance(self.value_columns, MultiIndex):
new_levels = self.value_columns.levels + (
self.removed_level_full, )
new_names = self.value_columns.names + (self.removed_name, )
new_labels = [
lab.take(propagator) for lab in self.value_columns.labels
]
else:
new_levels = [self.value_columns, self.removed_level_full]
new_names = [self.value_columns.name, self.removed_name]
new_labels = [propagator]
# The two indices differ only if the unstacked level had unused items:
if len(self.removed_level_full) != len(self.removed_level):
# In this case, we remap the new labels to the original level:
repeater = self.removed_level_full.get_indexer(self.removed_level)
if self.lift:
repeater = np.insert(repeater, 0, -1)
else:
# Otherwise, we just use each level item exactly once:
repeater = np.arange(stride) - self.lift
# The entire level is then just a repetition of the single chunk:
new_labels.append(np.tile(repeater, width))
return MultiIndex(levels=new_levels,
labels=new_labels,
names=new_names,
verify_integrity=False)
def get_new_columns(self):
if self.value_columns is None:
if self.lift == 0:
return self.removed_level
lev = self.removed_level
return lev.insert(0, _get_na_value(lev.dtype.type))
stride = len(self.removed_level) + self.lift
width = len(self.value_columns)
propagator = np.repeat(np.arange(width), stride)
if isinstance(self.value_columns, MultiIndex):
new_levels = self.value_columns.levels + (self.removed_level_full,)
new_names = self.value_columns.names + (self.removed_name,)
new_labels = [lab.take(propagator)
for lab in self.value_columns.labels]
else:
new_levels = [self.value_columns, self.removed_level_full]
new_names = [self.value_columns.name, self.removed_name]
new_labels = [propagator]
# The two indices differ only if the unstacked level had unused items:
if len(self.removed_level_full) != len(self.removed_level):
# In this case, we remap the new labels to the original level:
repeater = self.removed_level_full.get_indexer(self.removed_level)
if self.lift:
repeater = np.insert(repeater, 0, -1)
else:
# Otherwise, we just use each level item exactly once:
repeater = np.arange(stride) - self.lift
# The entire level is then just a repetition of the single chunk:
new_labels.append(np.tile(repeater, width))
return MultiIndex(levels=new_levels, labels=new_labels,
names=new_names, verify_integrity=False)
