def test_mutate_internally():
df = tibble(g=c(1, 2), x=c(1, 2))
df1 = df >> distinct(aa=f.g * 2)
df2 = df >> mutate(aa=f.g * 2) >> distinct(f.aa)
assert df1.equals(df2)
def test_single_column():
df = tibble(x=c(1, 1, 1, 1), y=c(1, 1, 2, 2), z=c(1, 2, 1, 2))
x = df >> distinct(f.x, _keep_all=False)
assert all(x.x == unique(df.x))
y = df >> distinct(f.y, _keep_all=False)
assert all(y.y == unique(df.y))
def test_switch_groupby_distinct_equal():
df = tibble(g=c(1, 2), x=c(1, 2))
df1 = df >> distinct() >> group_by(f.g)
df2 = df >> group_by(f.g) >> distinct()
assert df1.equals(df2)
def test_not_duplicating_cols():
df = tibble(a=[1, 2, 3], b=[4, 5, 6])
out = df >> distinct(f.a, f.a)
assert out.columns.tolist() == ["a"]
out = df >> group_by(f.a) >> distinct(f.a)
assert out.columns.tolist() == ["a"]
def test_rowwise_df():
df = tibble(x=[1, 1, 1, 2], y=[1, 2, 2, 2])
rf = df >> rowwise()
out = distinct(rf)
# ungrouped df keeps index
exp = distinct(df).reset_index(drop=True)
assert out.equals(exp)
assert isinstance(out, TibbleRowwise)
def test_unless_keep_all_true():
df = tibble(x=[1, 1, 1], y=[3, 2, 1])
expect1 = tibble(x=1)
out1 = df >> distinct(f.x)
assert out1.equals(expect1)
expect2 = tibble(x=1, y=3)
out2 = df >> distinct(f.x, _keep_all=True)
assert out2.equals(expect2)
def test_preserves_grouping():
gf = tibble(x=c(1, 1, 2, 2), y=f.x) >> group_by(f.x)
out = gf >> distinct()
gvars = group_vars(out)
assert gvars == ["x"]
out = gf >> distinct(x=f.x + 2)
gvars = group_vars(out)
assert gvars == ["x"]
def test_errors():
df = tibble(g=c(1, 2), x=c(1, 2))
with pytest.raises(KeyError):
df >> distinct(f.aa, f.x)
with pytest.raises(KeyError):
df >> distinct(f.aa, f.bb)
with pytest.raises(KeyError):
df >> distinct(y=f.a + 1)
def test_on_iter_type():
df = tibble(a=c("1", "1", "2", "2", "3", "3"), b=("A", ))
df2 = tibble(
x=range(1, 6),
y=[(1, 2, 3), (2, 3, 4), (3, 4, 5), (4, 5, 6), (5, 6, 7)],
)
out = df >> distinct()
expect = df >> slice_([0, 2, 4])
assert out.equals(expect)
out2 = df2 >> distinct()
assert out2.equals(df2)
def test_auto_splicing():
species = tibble(Species=iris.Species)
df1 = iris >> distinct(f.Species)
df2 = iris >> distinct(species)
assert df1.equals(df2)
df3 = iris >> distinct(across(f.Species))
assert df1.equals(df3)
df4 = (iris >> mutate(across(starts_with("Sepal"), round)) >> distinct(
f.Sepal_Length, f.Sepal_Width))
df5 = iris >> distinct(across(starts_with("Sepal"), round))
assert df4.equals(df5)
def test_grouping_cols_always_included():
df = tibble(g=c(1, 2), x=c(1, 2))
out = df >> group_by(f.g) >> distinct(f.x)
assert out.columns.tolist() == ["g", "x"]
def test_keeps_only_specified_cols():
df = tibble(x=c(1, 1, 1), y=c(1, 1, 1))
expect = tibble(x=1)
out = df >> distinct(f.x)
assert out.equals(expect)
def test_novar_use_all():
df = tibble(x=[1, 1], y=[2, 2])
expect = tibble(x=1, y=2)
out = df >> distinct()
assert out.equals(expect)
def test_0_col_df():
df = tibble(x=range(10)) >> select(~f.x)
cols = df >> distinct() >> ncol()
assert cols == 0
def test_on_na():
df = tibble(col_a=[1, NA, NA]) # >> mutate(col_a=f.col_a+0.0)
rows = df >> distinct() >> nrow()
assert rows == 2
def test_preserves_order():
d = tibble(x=[1, 2], y=[3, 4])
out = d >> distinct(f.y, f.x)
assert out.columns.tolist() == ["x", "y"]