def block2d_to_blocknd(values, items, shape, labels, ref_items=None):
""" pivot to the labels shape """
from pandas.core.internals import make_block
panel_shape = (len(items), ) + shape
# TODO: lexsort depth needs to be 2!!
# Create observation selection vector using major and minor
# labels, for converting to panel format.
selector = factor_indexer(shape[1:], labels)
mask = np.zeros(np.prod(shape), dtype=bool)
mask.put(selector, True)
pvalues = np.empty(panel_shape, dtype=values.dtype)
if not issubclass(pvalues.dtype.type, (np.integer, np.bool_)):
pvalues.fill(np.nan)
elif not mask.all():
pvalues = com._maybe_upcast(pvalues)
pvalues.fill(np.nan)
values = values
for i in xrange(len(items)):
pvalues[i].flat[mask] = values[:, i]
if ref_items is None:
ref_items = items
return make_block(pvalues, items, ref_items)
def block2d_to_blocknd(values, items, shape, labels, ref_items=None):
""" pivot to the labels shape """
from pandas.core.internals import make_block
panel_shape = (len(items),) + shape
# TODO: lexsort depth needs to be 2!!
# Create observation selection vector using major and minor
# labels, for converting to panel format.
selector = factor_indexer(shape[1:], labels)
mask = np.zeros(np.prod(shape), dtype=bool)
mask.put(selector, True)
pvalues = np.empty(panel_shape, dtype=values.dtype)
if not issubclass(pvalues.dtype.type, (np.integer, np.bool_)):
pvalues.fill(np.nan)
elif not mask.all():
pvalues = com._maybe_upcast(pvalues)
pvalues.fill(np.nan)
values = values
for i in xrange(len(items)):
pvalues[i].flat[mask] = values[:, i]
if ref_items is None:
ref_items = items
return make_block(pvalues, items, ref_items)
def block2d_to_block3d(values, items, shape, major_labels, minor_labels,
ref_items=None):
"""
Developer method for pivoting DataFrame -> Panel. Used in HDFStore and
DataFrame.to_panel
"""
from pandas.core.internals import make_block
panel_shape = (len(items),) + shape
# TODO: lexsort depth needs to be 2!!
# Create observation selection vector using major and minor
# labels, for converting to panel format.
selector = minor_labels + shape[1] * major_labels
mask = np.zeros(np.prod(shape), dtype=bool)
mask.put(selector, True)
pvalues = np.empty(panel_shape, dtype=values.dtype)
if not issubclass(pvalues.dtype.type, (np.integer, np.bool_)):
pvalues.fill(np.nan)
elif not mask.all():
pvalues = com._maybe_upcast(pvalues)
pvalues.fill(np.nan)
values = values
for i in xrange(len(items)):
pvalues[i].flat[mask] = values[:, i]
if ref_items is None:
ref_items = items
return make_block(pvalues, items, ref_items)
def get_new_values(self):
values = self.values
# place the values
length, width = self.full_shape
stride = values.shape[1]
result_width = width * stride
new_values = np.empty((length, result_width), dtype=values.dtype)
new_mask = np.zeros((length, result_width), dtype=bool)
new_values = com._maybe_upcast(new_values)
new_values.fill(np.nan)
# is there a simpler / faster way of doing this?
for i in xrange(values.shape[1]):
chunk = new_values[:, i * width:(i + 1) * width]
mask_chunk = new_mask[:, i * width:(i + 1) * width]
chunk.flat[self.mask] = self.sorted_values[:, i]
mask_chunk.flat[self.mask] = True
new_values = new_values.take(self.unique_groups, axis=0)
new_mask = new_mask.take(self.unique_groups, axis=0)
return new_values, new_mask
def get_new_values(self):
values = self.values
# place the values
length, width = self.full_shape
stride = values.shape[1]
result_width = width * stride
new_values = np.empty((length, result_width), dtype=values.dtype)
new_mask = np.zeros((length, result_width), dtype=bool)
new_values = com._maybe_upcast(new_values)
new_values.fill(np.nan)
# is there a simpler / faster way of doing this?
for i in xrange(values.shape[1]):
chunk = new_values[:, i * width : (i + 1) * width]
mask_chunk = new_mask[:, i * width : (i + 1) * width]
chunk.flat[self.mask] = self.sorted_values[:, i]
mask_chunk.flat[self.mask] = True
new_values = new_values.take(self.unique_groups, axis=0)
new_mask = new_mask.take(self.unique_groups, axis=0)
return new_values, new_mask
def block2d_to_block3d(values, items, shape, major_labels, minor_labels,
ref_items=None):
"""
Developer method for pivoting DataFrame -> Panel. Used in HDFStore and
DataFrame.to_panel
"""
from pandas.core.internals import make_block
panel_shape = (len(items),) + shape
# TODO: lexsort depth needs to be 2!!
# Create observation selection vector using major and minor
# labels, for converting to panel format.
selector = minor_labels + shape[1] * major_labels
mask = np.zeros(np.prod(shape), dtype=bool)
mask.put(selector, True)
pvalues = np.empty(panel_shape, dtype=values.dtype)
if not issubclass(pvalues.dtype.type, (np.integer, np.bool_)):
pvalues.fill(np.nan)
elif not mask.all():
pvalues = com._maybe_upcast(pvalues)
pvalues.fill(np.nan)
values = values
for i in xrange(len(items)):
pvalues[i].flat[mask] = values[:, i]
if ref_items is None:
ref_items = items
return make_block(pvalues, items, ref_items)
