def test_operator_func_between_series(dtype, func, has_nulls, fill_value):
nelem = 1000
arr1 = utils.gen_rand(dtype, nelem) * 10000
# Keeping a low value because CUDA 'pow' has 2 full range error
arr2 = utils.gen_rand(dtype, nelem) * 100
if has_nulls == 'some':
nulls1 = utils.random_bitmask(nelem)
nulls2 = utils.random_bitmask(nelem)
sr1 = Series.from_masked_array(arr1, nulls1)
sr2 = Series.from_masked_array(arr2, nulls2)
else:
sr1 = Series(arr1)
sr2 = Series(arr2)
psr1 = sr1.to_pandas()
psr2 = sr2.to_pandas()
expect = getattr(psr1, func)(psr2, fill_value=fill_value)
got = getattr(sr1, func)(sr2, fill_value=fill_value)
# This is being done because of the various gymnastics required to support
# equality for null values. cudf.Series().to_pandas() replaces nulls with
# None and so a bool Series becomes object Series. Which does not match the
# output of equality op in pandas which remains a bool. Furthermore, NaN
# values are treated as not comparable and always return False in a bool op
# except in not-equal op where bool(Nan != Nan) gives True.
if got.dtype == np.bool:
got = got.fillna(True) if func == 'ne' else got.fillna(False)
utils.assert_eq(expect, got)
def test_series_std(ddof):
np.random.seed(0)
arr = np.random.random(100) - 0.5
sr = Series(arr)
pd = sr.to_pandas()
got = sr.std(ddof=ddof)
expect = pd.std(ddof=ddof)
np.testing.assert_approx_equal(expect, got)
