def _reindex_columns(self, columns):
if len(columns) == 0:
return DataMatrix(index=self.index)
if not isinstance(columns, Index):
columns = Index(columns)
if self.objects is not None:
object_columns = columns.intersection(self.objects.columns)
columns = columns - object_columns
objects = self.objects._reindex_columns(object_columns)
else:
objects = None
if len(columns) > 0 and len(self.columns) == 0:
return DataMatrix(index=self.index, columns=columns,
objects=objects)
indexer, mask = common.get_indexer(self.columns, columns, None)
mat = self.values.take(indexer, axis=1)
notmask = -mask
if len(mask) > 0:
if notmask.any():
if issubclass(mat.dtype.type, np.int_):
mat = mat.astype(float)
elif issubclass(mat.dtype.type, np.bool_):
mat = mat.astype(float)
common.null_out_axis(mat, notmask, 1)
return DataMatrix(mat, index=self.index, columns=columns,
objects=objects)
def _reindex_index(self, index, method):
if index is self.index:
return self.copy()
if not isinstance(index, Index):
index = Index(index)
if len(self.index) == 0:
return DataMatrix(index=index, columns=self.columns)
indexer, mask = common.get_indexer(self.index, index, method)
mat = self.values.take(indexer, axis=0)
notmask = -mask
if len(index) > 0:
if notmask.any():
if issubclass(mat.dtype.type, np.int_):
mat = mat.astype(float)
elif issubclass(mat.dtype.type, np.bool_):
mat = mat.astype(float)
common.null_out_axis(mat, notmask, 0)
if self.objects is not None and len(self.objects.columns) > 0:
newObjects = self.objects.reindex(index)
else:
newObjects = None
return DataMatrix(mat,
index=index,
columns=self.columns,
objects=newObjects)
def _reindex_index(self, index, method):
if index is self.index:
return self.copy()
if not isinstance(index, Index):
index = Index(index)
if len(self.index) == 0:
return DataMatrix(index=index, columns=self.columns)
indexer, mask = common.get_indexer(self.index, index, method)
mat = self.values.take(indexer, axis=0)
notmask = -mask
if len(index) > 0:
if notmask.any():
if issubclass(mat.dtype.type, np.int_):
mat = mat.astype(float)
elif issubclass(mat.dtype.type, np.bool_):
mat = mat.astype(float)
common.null_out_axis(mat, notmask, 0)
if self.objects is not None and len(self.objects.columns) > 0:
newObjects = self.objects.reindex(index)
else:
newObjects = None
return DataMatrix(mat, index=index, columns=self.columns,
objects=newObjects)
def reindex_axis(self, indexer, notmask, needs_masking, axis=0):
"""
Reindex using pre-computed indexer information
"""
if self.values.size > 0:
new_values = self.values.take(indexer, axis=axis)
else:
shape = list(self.shape)
shape[axis] = len(indexer)
new_values = np.empty(shape)
new_values.fill(np.nan)
if needs_masking:
new_values = _cast_if_bool_int(new_values)
common.null_out_axis(new_values, notmask, axis)
return make_block(new_values, self.items, self.ref_items)
def _reindex_columns(self, columns):
if len(columns) == 0:
return DataMatrix(index=self.index)
if not isinstance(columns, Index):
columns = Index(columns)
if self.objects is not None:
object_columns = columns.intersection(self.objects.columns)
columns = columns - object_columns
objects = self.objects._reindex_columns(object_columns)
else:
objects = None
if len(columns) > 0 and len(self.columns) == 0:
return DataMatrix(index=self.index,
columns=columns,
objects=objects)
indexer, mask = common.get_indexer(self.columns, columns, None)
mat = self.values.take(indexer, axis=1)
notmask = -mask
if len(mask) > 0:
if notmask.any():
if issubclass(mat.dtype.type, np.int_):
mat = mat.astype(float)
elif issubclass(mat.dtype.type, np.bool_):
mat = mat.astype(float)
common.null_out_axis(mat, notmask, 1)
return DataMatrix(mat,
index=self.index,
columns=columns,
objects=objects)
