def test_logical():
a = pa.array([True, False, False, None])
b = pa.array([True, True, False, True])
assert pc.and_(a, b) == pa.array([True, False, False, None])
assert pc.and_kleene(a, b) == pa.array([True, False, False, None])
assert pc.or_(a, b) == pa.array([True, True, False, None])
assert pc.or_kleene(a, b) == pa.array([True, True, False, True])
assert pc.xor(a, b) == pa.array([False, True, False, None])
assert pc.invert(a) == pa.array([False, True, True, None])
ARROW_CMP_FUNCS = {
"eq": pc.equal,
"ne": pc.not_equal,
"lt": pc.less,
"gt": pc.greater,
"le": pc.less_equal,
"ge": pc.greater_equal,
}
ARROW_LOGICAL_FUNCS = {
"and":
NotImplemented if pa_version_under2p0 else pc.and_kleene,
"rand":
NotImplemented
if pa_version_under2p0 else lambda x, y: pc.and_kleene(y, x),
"or":
NotImplemented if pa_version_under2p0 else pc.or_kleene,
"ror":
NotImplemented
if pa_version_under2p0 else lambda x, y: pc.or_kleene(y, x),
"xor":
NotImplemented if pa_version_under2p0 else pc.xor,
"rxor":
NotImplemented if pa_version_under2p0 else lambda x, y: pc.xor(y, x),
}
def cast_for_truediv(arrow_array: pa.ChunkedArray,
pa_object: pa.Array | pa.Scalar) -> pa.ChunkedArray:
# Ensure int / int -> float mirroring Python/Numpy behavior
# as pc.divide_checked(int, int) -> int