def _compute(self, arrays, dates, assets, mask):
data = arrays[0]
groupby_expr = self.inputs[1]
if groupby_expr.dtype == int64_dtype:
group_labels = arrays[1]
null_label = self.inputs[1].missing_value
elif groupby_expr.dtype == categorical_dtype:
# Coerce our LabelArray into an isomorphic array of ints. This is
# necessary because np.where doesn't know about LabelArrays or the
# void dtype.
group_labels = arrays[1].as_int_array()
null_label = arrays[1].missing_value_code
else:
raise TypeError(
"Unexpected groupby dtype: %s." % groupby_expr.dtype
)
# Make a copy with the null code written to masked locations.
group_labels = where(mask, group_labels, null_label)
return where(
group_labels != null_label,
naive_grouped_rowwise_apply(
data=data,
group_labels=group_labels,
func=self._transform,
),
self.missing_value,
)
def _compute(self, arrays, dates, assets, mask):
data = arrays[0]
groupby_expr = self.inputs[1]
if groupby_expr.dtype == int64_dtype:
group_labels = arrays[1]
null_label = self.inputs[1].missing_value
elif groupby_expr.dtype == categorical_dtype:
# Coerce our LabelArray into an isomorphic array of ints. This is
# necessary because np.where doesn't know about LabelArrays or the
# void dtype.
group_labels = arrays[1].as_int_array()
null_label = arrays[1].missing_value_code
else:
raise TypeError("Unexpected groupby dtype: %s." %
groupby_expr.dtype)
# Make a copy with the null code written to masked locations.
group_labels = where(mask, group_labels, null_label)
return where(
group_labels != null_label,
naive_grouped_rowwise_apply(
data=data,
group_labels=group_labels,
func=self._transform,
),
self.missing_value,
)
def _compute(self, arrays, dates, assets, mask):
data = arrays[0]
null_group_value = self.inputs[1].missing_value
group_labels = where(
mask,
arrays[1],
null_group_value,
)
return where(
group_labels != null_group_value,
naive_grouped_rowwise_apply(
data=data,
group_labels=group_labels,
func=self._transform,
),
self.missing_value,
)
def _compute(self, arrays, dates, assets, mask):
data = arrays[0]
null_group_value = self.inputs[1].missing_value
group_labels = where(
mask,
arrays[1],
null_group_value,
)
return where(
group_labels != null_group_value,
naive_grouped_rowwise_apply(
data=data,
group_labels=group_labels,
func=self._transform,
),
self.missing_value,
)