def get_new_values(self):
values = self.values
# place the values
length, width = self.full_shape
stride = values.shape[1]
result_width = width * stride
result_shape = (length, result_width)
mask = self.mask
mask_all = mask.all()
# we can simply reshape if we don't have a mask
if mask_all and len(values):
new_values = (self.sorted_values.reshape(
length, width, stride).swapaxes(1, 2).reshape(result_shape))
new_mask = np.ones(result_shape, dtype=bool)
return new_values, new_mask
# if our mask is all True, then we can use our existing dtype
if mask_all:
dtype = values.dtype
new_values = np.empty(result_shape, dtype=dtype)
else:
dtype, fill_value = maybe_promote(values.dtype, self.fill_value)
new_values = np.empty(result_shape, dtype=dtype)
new_values.fill(fill_value)
new_mask = np.zeros(result_shape, dtype=bool)
name = np.dtype(dtype).name
sorted_values = self.sorted_values
# we need to convert to a basic dtype
# and possibly coerce an input to our output dtype
# e.g. ints -> floats
if needs_i8_conversion(values):
sorted_values = sorted_values.view("i8")
new_values = new_values.view("i8")
elif is_bool_dtype(values):
sorted_values = sorted_values.astype("object")
new_values = new_values.astype("object")
else:
sorted_values = sorted_values.astype(name, copy=False)
# fill in our values & mask
libreshape.unstack(
sorted_values,
mask.view("u1"),
stride,
length,
width,
new_values,
new_mask.view("u1"),
)
# reconstruct dtype if needed
if needs_i8_conversion(values):
new_values = new_values.view(values.dtype)
return new_values, new_mask
def get_new_values(self, values, fill_value=None):
if values.ndim == 1:
values = values[:, np.newaxis]
sorted_values = self._make_sorted_values(values)
# place the values
length, width = self.full_shape
stride = values.shape[1]
result_width = width * stride
result_shape = (length, result_width)
mask = self.mask
mask_all = self.mask_all
# we can simply reshape if we don't have a mask
if mask_all and len(values):
# TODO: Under what circumstances can we rely on sorted_values
# matching values? When that holds, we can slice instead
# of take (in particular for EAs)
new_values = (
sorted_values.reshape(length, width, stride)
.swapaxes(1, 2)
.reshape(result_shape)
)
new_mask = np.ones(result_shape, dtype=bool)
return new_values, new_mask
# if our mask is all True, then we can use our existing dtype
if mask_all:
dtype = values.dtype
new_values = np.empty(result_shape, dtype=dtype)
name = np.dtype(dtype).name
else:
dtype, fill_value = maybe_promote(values.dtype, fill_value)
if isinstance(dtype, ExtensionDtype):
# GH#41875
cls = dtype.construct_array_type()
new_values = cls._empty(result_shape, dtype=dtype)
new_values[:] = fill_value
name = dtype.name
else:
new_values = np.empty(result_shape, dtype=dtype)
new_values.fill(fill_value)
name = np.dtype(dtype).name
new_mask = np.zeros(result_shape, dtype=bool)
# we need to convert to a basic dtype
# and possibly coerce an input to our output dtype
# e.g. ints -> floats
if needs_i8_conversion(values.dtype):
sorted_values = sorted_values.view("i8")
new_values = new_values.view("i8")
elif is_bool_dtype(values.dtype):
sorted_values = sorted_values.astype("object")
new_values = new_values.astype("object")
else:
sorted_values = sorted_values.astype(name, copy=False)
# fill in our values & mask
libreshape.unstack(
sorted_values,
mask.view("u1"),
stride,
length,
width,
new_values,
new_mask.view("u1"),
)
# reconstruct dtype if needed
if needs_i8_conversion(values.dtype):
# view as datetime64 so we can wrap in DatetimeArray and use
# DTA's view method
new_values = new_values.view("M8[ns]")
new_values = ensure_wrapped_if_datetimelike(new_values)
new_values = new_values.view(values.dtype)
return new_values, new_mask