def test_validity_ceil(nelem):
# Data
data = np.random.random(nelem) * 100
mask = utils.random_bitmask(nelem)
bitmask = utils.expand_bits_to_bytes(mask)[:nelem]
sr = Series.from_masked_array(data, mask)
# Result
with pytest.warns(
FutureWarning, match="Series.ceil and DataFrame.ceil are deprecated"
):
res = sr.ceil()
na_value = -100000
got = res.fillna(na_value).to_numpy()
res_mask = np.asarray(bitmask, dtype=np.bool_)[: data.size]
expect = np.ceil(data)
expect[~res_mask] = na_value
# Check
print("expect")
print(expect)
print("got")
print(got)
np.testing.assert_array_equal(expect, got)
def test_to_dense_array():
data = np.random.random(8)
mask = np.asarray([0b11010110], dtype=np.byte)
sr = Series.from_masked_array(data=data, mask=mask, null_count=3)
assert sr.has_nulls
assert sr.null_count != len(sr)
filled = sr.to_array(fillna="pandas")
dense = sr.to_array()
assert dense.size < filled.size
assert filled.size == len(sr)
def codes(self):
from cudf import Series
data = self._parent.data
if self._parent.has_null_mask:
mask = self._parent.mask
null_count = self._parent.null_count
return Series.from_masked_array(
data=data.mem, mask=mask.mem, null_count=null_count
)
else:
return Series(data, name=self._parent.name)
def test_sum_masked(nelem):
dtype = np.float64
data = gen_rand(dtype, nelem)
mask = utils.random_bitmask(nelem)
bitmask = utils.expand_bits_to_bytes(mask)[:nelem]
null_count = utils.count_zero(bitmask)
sr = Series.from_masked_array(data, mask, null_count)
got = sr.sum()
res_mask = np.asarray(bitmask, dtype=np.bool_)[: data.size]
expect = data[res_mask].sum()
significant = 4 if dtype == np.float32 else 6
np.testing.assert_approx_equal(expect, got, significant=significant)
def test_validity_ceil(nelem):
# Data
data = np.random.random(nelem) * 100
mask = utils.random_bitmask(nelem)
bitmask = utils.expand_bits_to_bytes(mask)[:nelem]
sr = Series.from_masked_array(data, mask)
# Result
res = sr.ceil()
na_value = -100000
got = res.fillna(na_value).to_array()
res_mask = np.asarray(bitmask, dtype=np.bool_)[:data.size]
expect = np.ceil(data)
expect[~res_mask] = na_value
# Check
print("expect")
print(expect)
print("got")
print(got)
np.testing.assert_array_equal(expect, got)
def gen_rand_series(dtype, size, **kwargs):
values = gen_rand(dtype, size, **kwargs)
if kwargs.get("has_nulls", False):
return Series.from_masked_array(values, random_bitmask(size))
return Series(values)
