def na_op(x, y):
try:
result = op(x, y)
except TypeError:
assert not isinstance(y, (list, ABCSeries, ABCIndexClass))
if isinstance(y, np.ndarray):
# bool-bool dtype operations should be OK, should not get here
assert not (is_bool_dtype(x) and is_bool_dtype(y))
x = ensure_object(x)
y = ensure_object(y)
result = libops.vec_binop(x, y, op)
else:
# let null fall thru
assert lib.is_scalar(y)
if not isna(y):
y = bool(y)
try:
result = libops.scalar_binop(x, y, op)
except (
TypeError,
ValueError,
AttributeError,
OverflowError,
NotImplementedError,
):
raise TypeError(
"cannot compare a dtyped [{dtype}] array "
"with a scalar of type [{typ}]".format(
dtype=x.dtype, typ=type(y).__name__
)
)
return result
def na_logical_op(x: np.ndarray, y, op):
try:
# For exposition, write:
# yarr = isinstance(y, np.ndarray)
# yint = is_integer(y) or (yarr and y.dtype.kind == "i")
# ybool = is_bool(y) or (yarr and y.dtype.kind == "b")
# xint = x.dtype.kind == "i"
# xbool = x.dtype.kind == "b"
# Then Cases where this goes through without raising include:
# (xint or xbool) and (yint or bool)
result = op(x, y)
except TypeError:
if isinstance(y, np.ndarray):
# bool-bool dtype operations should be OK, should not get here
assert not (is_bool_dtype(x.dtype) and is_bool_dtype(y.dtype))
x = ensure_object(x)
y = ensure_object(y)
result = libops.vec_binop(x, y, op)
else:
# let null fall thru
assert lib.is_scalar(y)
if not isna(y):
y = bool(y)
try:
result = libops.scalar_binop(x, y, op)
except (
TypeError,
ValueError,
AttributeError,
OverflowError,
NotImplementedError,
):
raise TypeError(
"Cannot perform '{op}' with a dtyped [{dtype}] array "
"and scalar of type [{typ}]".format(
op=op.__name__, dtype=x.dtype, typ=type(y).__name__
)
)
return result
