def flex_method_SERIES(op):
name = op.__name__.strip("_")
doc = make_flex_doc(name, "series")
@Appender(doc)
def flex_wrapper(self, other, level=None, fill_value=None, axis=0):
# validate axis
if axis is not None:
self._get_axis_number(axis)
res_name = get_op_result_name(self, other)
if isinstance(other, ABCSeries):
return self._binop(other, op, level=level, fill_value=fill_value)
elif isinstance(other, (np.ndarray, list, tuple)):
if len(other) != len(self):
raise ValueError("Lengths must be equal")
other = self._constructor(other, self.index)
result = self._binop(other, op, level=level, fill_value=fill_value)
result.name = res_name
return result
else:
if fill_value is not None:
self = self.fillna(fill_value)
return op(self, other)
flex_wrapper.__name__ = name
return flex_wrapper
def flex_arith_method_FRAME(op):
op_name = op.__name__.strip("_")
default_axis = "columns"
na_op = get_array_op(op)
doc = make_flex_doc(op_name, "dataframe")
@Appender(doc)
def f(self, other, axis=default_axis, level=None, fill_value=None):
if should_reindex_frame_op(self, other, op, axis, default_axis,
fill_value, level):
return frame_arith_method_with_reindex(self, other, op)
if isinstance(other, ABCSeries) and fill_value is not None:
# TODO: We could allow this in cases where we end up going
# through the DataFrame path
raise NotImplementedError(
f"fill_value {fill_value} not supported.")
axis = self._get_axis_number(axis) if axis is not None else 1
other = maybe_prepare_scalar_for_op(other, self.shape)
self, other = align_method_FRAME(self,
other,
axis,
flex=True,
level=level)
if isinstance(other, ABCDataFrame):
# Another DataFrame
new_data = self._combine_frame(other, na_op, fill_value)
elif isinstance(other, ABCSeries):
new_data = self._dispatch_frame_op(other, op, axis=axis)
else:
# in this case we always have `np.ndim(other) == 0`
if fill_value is not None:
self = self.fillna(fill_value)
new_data = self._dispatch_frame_op(other, op)
return self._construct_result(new_data)
f.__name__ = op_name
return f
