def _create_arithmetic_method(cls, op):
pd_op = ops.get_array_op(op)
@ops.unpack_zerodim_and_defer(op.__name__)
def arithmetic_method(self, other):
if isinstance(other, cls):
other = other._ndarray
result = pd_op(self._ndarray, other)
if op is divmod or op is ops.rdivmod:
a, b = result
if isinstance(a, np.ndarray):
# for e.g. op vs TimedeltaArray, we may already
# have an ExtensionArray, in which case we do not wrap
return self._wrap_ndarray_result(
a), self._wrap_ndarray_result(b)
return a, b
if isinstance(result, np.ndarray):
# for e.g. multiplication vs TimedeltaArray, we may already
# have an ExtensionArray, in which case we do not wrap
return self._wrap_ndarray_result(result)
return result
return compat.set_function_name(arithmetic_method,
f"__{op.__name__}__", cls)
def _cmp_method(self, other, op):
if isinstance(other, PandasArray):
other = other._ndarray
pd_op = ops.get_array_op(op)
result = pd_op(self._ndarray, other)
if op is divmod or op is ops.rdivmod:
a, b = result
if isinstance(a, np.ndarray):
# for e.g. op vs TimedeltaArray, we may already
# have an ExtensionArray, in which case we do not wrap
return self._wrap_ndarray_result(a), self._wrap_ndarray_result(b)
return a, b
if isinstance(result, np.ndarray):
# for e.g. multiplication vs TimedeltaArray, we may already
# have an ExtensionArray, in which case we do not wrap
return self._wrap_ndarray_result(result)
return result
def _cmp_method(self, other, op):
if isinstance(other, PandasArray):
other = other._ndarray
other = ops.maybe_prepare_scalar_for_op(other, (len(self),))
pd_op = ops.get_array_op(op)
other = ensure_wrapped_if_datetimelike(other)
with np.errstate(all="ignore"):
result = pd_op(self._ndarray, other)
if op is divmod or op is ops.rdivmod:
a, b = result
if isinstance(a, np.ndarray):
# for e.g. op vs TimedeltaArray, we may already
# have an ExtensionArray, in which case we do not wrap
return self._wrap_ndarray_result(a), self._wrap_ndarray_result(b)
return a, b
if isinstance(result, np.ndarray):
# for e.g. multiplication vs TimedeltaArray, we may already
# have an ExtensionArray, in which case we do not wrap
return self._wrap_ndarray_result(result)
return result
def _arith_method(self, other, op):
op_name = op.__name__
omask = None
if isinstance(other, BaseMaskedArray):
other, omask = other._data, other._mask
elif is_list_like(other):
if not isinstance(other, ExtensionArray):
other = np.asarray(other)
if other.ndim > 1:
raise NotImplementedError(
"can only perform ops with 1-d structures")
# We wrap the non-masked arithmetic logic used for numpy dtypes
# in Series/Index arithmetic ops.
other = ops.maybe_prepare_scalar_for_op(other, (len(self), ))
pd_op = ops.get_array_op(op)
other = ensure_wrapped_if_datetimelike(other)
if op_name in {"pow", "rpow"} and isinstance(other, np.bool_):
# Avoid DeprecationWarning: In future, it will be an error
# for 'np.bool_' scalars to be interpreted as an index
# e.g. test_array_scalar_like_equivalence
other = bool(other)
mask = self._propagate_mask(omask, other)
if other is libmissing.NA:
result = np.ones_like(self._data)
if self.dtype.kind == "b":
if op_name in {
"floordiv", "rfloordiv", "mod", "rmod", "pow", "rpow"
}:
dtype = "int8"
elif op_name in {"truediv", "rtruediv"}:
dtype = "float64"
else:
dtype = "bool"
result = result.astype(dtype)
elif "truediv" in op_name and self.dtype.kind != "f":
# The actual data here doesn't matter since the mask
# will be all-True, but since this is division, we want
# to end up with floating dtype.
result = result.astype(np.float64)
else:
# Make sure we do this before the "pow" mask checks
# to get an expected exception message on shape mismatch.
if self.dtype.kind in ["i", "u"
] and op_name in ["floordiv", "mod"]:
# TODO(GH#30188) ATM we don't match the behavior of non-masked
# types with respect to floordiv-by-zero
pd_op = op
elif self.dtype.kind == "b" and ("div" in op_name or "pow"
in op_name or "mod" in op_name):
# TODO(GH#41165): should these be disallowed?
pd_op = op
with np.errstate(all="ignore"):
result = pd_op(self._data, other)
if op_name == "pow":
# 1 ** x is 1.
mask = np.where((self._data == 1) & ~self._mask, False, mask)
# x ** 0 is 1.
if omask is not None:
mask = np.where((other == 0) & ~omask, False, mask)
elif other is not libmissing.NA:
mask = np.where(other == 0, False, mask)
elif op_name == "rpow":
# 1 ** x is 1.
if omask is not None:
mask = np.where((other == 1) & ~omask, False, mask)
elif other is not libmissing.NA:
mask = np.where(other == 1, False, mask)
# x ** 0 is 1.
mask = np.where((self._data == 0) & ~self._mask, False, mask)
return self._maybe_mask_result(result, mask)
