def test_n_distinct_handles_unnamed():
x = iris.Sepal_Length
y = iris.Sepal_Width
out = n_distinct(iris.Sepal_Length)
exp = n_distinct(x)
assert out == exp
out = n_distinct(iris.Sepal_Width)
exp = n_distinct(y)
assert out == exp
def test_one_group_for_NA():
x = c(NA, NA, NA, range(10, 0, -1), range(10, 0, -1))
w = numpy.array(c(20, 30, 40, range(1, 11), range(1, 11))) * 10
assert n_distinct(x, na_rm=False) == 11
res = tibble(x=x, w=w) >> group_by(f.x) >> summarise(n=n())
assert nrow(res) == 11
def test_n_distinct_handles_in_na_rm():
d = tibble(x=c([1, 2, 3, 4], NA))
yes = True
no = False
out = d >> summarise(n=n_distinct(f.x, na_rm=True)) >> pull(to="list")
assert out == [4]
out = d >> summarise(n=n_distinct(f.x, na_rm=False)) >> pull(to="list")
assert out == [5]
out = d >> summarise(n=n_distinct(f.x, na_rm=yes)) >> pull(to="list")
assert out == [4]
out = d >> summarise(n=n_distinct(f.x, na_rm=no)) >> pull(to="list")
assert out == [5]
out = (d >> summarise(n=n_distinct(f.x, na_rm=True or True)) >>
pull(to="list"))
assert out == [4]
def test_n_distinct_works_with_str_col():
wrapper = lambda data, col: summarise(
data, result=n_distinct(f[col], na_rm=True))
df = tibble(x=[1, 1, 3, NA])
out = wrapper(df, "x")
exp = tibble(result=2)
assert out.equals(exp)
def test_n_distinct_recyles_len1_vec():
# assert n_distinct(1, [1, 2, 3, 4]) == 4
# assert n_distinct([1, 2, 3, 4], 1) == 4
assert n_distinct(4) == 1
assert n_distinct(NA, na_rm=True) == 0
assert n_distinct([1, 2, 3, 4]) == 4
d = tibble(x=[1, 2, 3, 4])
res = d >> summarise(
y=sum(f.x),
# summrise fail to mix input and summarised data in one expression
# n1=n_distinct(f.y, f.x),
# n2=n_distinct(f.x, f.y),
n3=n_distinct(f.y),
n4=n_distinct(identity(f.y)),
n5=n_distinct(f.x),
)
# assert res.n1.tolist() == [4]
# assert res.n2.tolist() == [4]
assert res.n3.tolist() == [1]
assert res.n4.tolist() == [1]
assert res.n5.tolist() == [4]
res = (
tibble(g=c(1, 1, 1, 1, 2, 2), x=c(1, 2, 3, 1, 1, 2)) >> group_by(f.g)
>> summarise(
y=sum(f.x),
# n1=n_distinct(f.y, f.x),
# n2=n_distinct(f.x, f.y),
n3=n_distinct(f.y),
n4=n_distinct(identity(f.y)),
n5=n_distinct(f.x),
))
# assert res.n1.tolist() == [3,2]
# assert res.n2.tolist() == [3,2]
assert res.n3.tolist() == [1, 1]
assert res.n4.tolist() == [1, 1]
assert res.n5.tolist() == [3, 2]
def test_n_distinct_treats_na_correctly():
# test_that("n_distinct treats NA correctly in the REALSXP case (#384)", {
assert n_distinct(c(1.0, NA, NA), na_rm=False) == 2
def test_n_distinct_respects_data():
df = tibble(x=42)
out = df >> summarise(n=n_distinct(df.x))
exp = tibble(n=1)
assert out.equals(exp)