def _unstack_multiple(data, clocs):
if len(clocs) == 0:
return data
# NOTE: This doesn't deal with hierarchical columns yet
index = data.index
clocs = [index._get_level_number(i) for i in clocs]
rlocs = [i for i in range(index.nlevels) if i not in clocs]
clevels = [index.levels[i] for i in clocs]
clabels = [index.labels[i] for i in clocs]
cnames = [index.names[i] for i in clocs]
rlevels = [index.levels[i] for i in rlocs]
rlabels = [index.labels[i] for i in rlocs]
rnames = [index.names[i] for i in rlocs]
shape = [len(x) for x in clevels]
group_index = get_group_index(clabels, shape)
comp_ids, obs_ids = _compress_group_index(group_index, sort=False)
recons_labels = decons_group_index(obs_ids, shape)
dummy_index = MultiIndex(levels=rlevels + [obs_ids],
labels=rlabels + [comp_ids],
names=rnames + ['__placeholder__'])
if isinstance(data, Series):
dummy = Series(data.values, index=dummy_index)
unstacked = dummy.unstack('__placeholder__')
new_levels = clevels
new_names = cnames
new_labels = recons_labels
else:
if isinstance(data.columns, MultiIndex):
result = data
for i in range(len(clocs)):
val = clocs[i]
result = result.unstack(val)
clocs = [val if i > val else val - 1 for val in clocs]
return result
dummy = DataFrame(data.values, index=dummy_index, columns=data.columns)
unstacked = dummy.unstack('__placeholder__')
if isinstance(unstacked, Series):
unstcols = unstacked.index
else:
unstcols = unstacked.columns
new_levels = [unstcols.levels[0]] + clevels
new_names = [data.columns.name] + cnames
new_labels = [unstcols.labels[0]]
for rec in recons_labels:
new_labels.append(rec.take(unstcols.labels[-1]))
new_columns = MultiIndex(levels=new_levels,
labels=new_labels,
names=new_names)
if isinstance(unstacked, Series):
unstacked.index = new_columns
else:
unstacked.columns = new_columns
return unstacked
def _unstack_multiple(data, clocs):
if len(clocs) == 0:
return data
# NOTE: This doesn't deal with hierarchical columns yet
index = data.index
clocs = [index._get_level_number(i) for i in clocs]
rlocs = [i for i in range(index.nlevels) if i not in clocs]
clevels = [index.levels[i] for i in clocs]
clabels = [index.labels[i] for i in clocs]
cnames = [index.names[i] for i in clocs]
rlevels = [index.levels[i] for i in rlocs]
rlabels = [index.labels[i] for i in rlocs]
rnames = [index.names[i] for i in rlocs]
shape = [len(x) for x in clevels]
group_index = get_group_index(clabels, shape)
comp_ids, obs_ids = _compress_group_index(group_index, sort=False)
recons_labels = decons_group_index(obs_ids, shape)
dummy_index = MultiIndex(levels=rlevels + [obs_ids],
labels=rlabels + [comp_ids],
names=rnames + ['__placeholder__'])
if isinstance(data, Series):
dummy = Series(data.values, index=dummy_index)
unstacked = dummy.unstack('__placeholder__')
new_levels = clevels
new_names = cnames
new_labels = recons_labels
else:
if isinstance(data.columns, MultiIndex):
result = data
for i in range(len(clocs)):
val = clocs[i]
result = result.unstack(val)
clocs = [val if i > val else val - 1 for val in clocs]
return result
dummy = DataFrame(data.values, index=dummy_index,
columns=data.columns)
unstacked = dummy.unstack('__placeholder__')
if isinstance(unstacked, Series):
unstcols = unstacked.index
else:
unstcols = unstacked.columns
new_levels = [unstcols.levels[0]] + clevels
new_names = [data.columns.name] + cnames
new_labels = [unstcols.labels[0]]
for rec in recons_labels:
new_labels.append(rec.take(unstcols.labels[-1]))
new_columns = MultiIndex(levels=new_levels, labels=new_labels,
names=new_names)
if isinstance(unstacked, Series):
unstacked.index = new_columns
else:
unstacked.columns = new_columns
return unstacked
def testit(label_list, shape):
group_index = get_group_index(label_list, shape, sort=True, xnull=True)
label_list2 = decons_group_index(group_index, shape)
for a, b in zip(label_list, label_list2):
assert (np.array_equal(a, b))
