def test_ignores_null_empty():
df = tibble(a=1)
out = df >> bind_rows(NULL)
assert out.equals(df)
df0 = tibble()
out = df >> bind_rows(df0)
assert out.equals(df)
# no rows
df_no_rows = df.iloc[[], :]
out = df >> bind_rows(df_no_rows)
assert out.equals(df)
# no cols
df_no_cols = df.iloc[:, []]
out = df >> bind_rows(df_no_cols)
rows = out >> nrow()
assert rows == 2
val = out.fillna(1234) >> get(1, f.a)
assert val == 1234
out = df_no_cols >> bind_rows(df)
rows = out >> nrow()
assert rows == 2
val = out.fillna(888) >> get(0, f.a)
assert val == 888
def test_list_as_first_argument():
ll = tibble(a=1, b=2)
out = bind_rows([ll])
assert out.equals(ll)
out = bind_rows([ll, ll])
expect = tibble(a=[1, 1], b=[2, 2])
assert out.equals(expect)
def test_rowwise_vector():
tbl = tibble(a="foo", b="bar") >> bind_rows(dict(a="A", b="B"))
expect = tibble(a=["foo", "A"], b=["bar", "B"])
assert tbl.equals(expect)
id_tbl = bind_rows(None, a=dict(a=1, b=2), b=dict(a=3, b=4), _id="id")
expect = tibble(id=["a", "b"], a=[1, 3], b=[2, 4])
assert id_tbl.equals(expect)
def test_create_id_col():
df = tibble(x=range(1, 11))
df1 = df >> head(3)
df2 = df >> tail(2)
out = bind_rows([df1, df2], _id="col")
assert out.col.tolist() == [0, 0, 0, 1, 1]
out = bind_rows(None, one=df1, two=df2, _id="col")
assert out.col.tolist() == ["one"] * 3 + ["two"] * 2
def test_bind_empty_dfs():
out = bind_rows(None)
assert dim(out) == (0, 0)
out = bind_cols(None)
assert dim(out) == (0, 0)
df1 = tibble(x=factor([1, 2, 3]))
df2 = tibble()
out = df1 >> bind_rows(df2)
assert out.x.tolist() == [1, 2, 3]
def test_errors():
df1 = tibble(x=[1, 2, 3])
df2 = tibble(x=[4, 5, 6])
with pytest.raises(ValueError):
df1 >> bind_rows(df2, _id=5)
df1 = tibble(a=factor("a"))
df2 = tibble(a=1)
df1 >> bind_rows(df2) # no error, all converted to object
with pytest.raises(ValueError):
[1, 2] >> bind_rows()
def test_bind_na_cols():
df1 = tibble(x=factor(["foo", "bar"]))
df2 = tibble(x=NA)
out = df1 >> bind_rows(df2)
res = out >> get(2, f.x)
y = is_na(res)
assert_iterable_equal(y, [True])
out = df2 >> bind_rows(df1)
res = out >> get(0, f.x)
y = is_na(res)
assert_iterable_equal(y, [True])
y = is_categorical(out.x)
assert y
def test_complex():
df1 = tibble(r=[1 + 1j, 2 - 1j])
df2 = tibble(r=[1 - 1j, 2 + 1j])
df3 = df1 >> bind_rows(df2)
out = df3 >> nrow()
assert out == 4
assert df3.r.tolist() == df1.r.tolist() + df2.r.tolist()
def test_bind_factors():
df1 = tibble(a=factor("a"))
df2 = tibble(a=factor("b"))
out = df1 >> bind_rows(df2)
assert out.a.cat.categories.tolist() == ["a", "b"]
df1 = tibble(a=factor("a"))
df2 = tibble(a=factor(NA))
out = df1 >> bind_rows(df2)
assert out.a.cat.categories.tolist() == ["a"]
assert out.a.astype(object).fillna("NA").tolist() == ["a", "NA"]
out2 = None >> bind_rows([df1, df2])
assert_frame_equal(out2, out)
def test_factor_to_chars():
# we don't have warnings
df1 = tibble(a=factor("a"))
df2 = tibble(a="b")
out = df1 >> bind_rows(df1, df2)
a_type = is_factor(out.a)
assert not a_type
def test_int_to_float():
df1 = tibble(a=1.0, b=2)
df2 = tibble(a=1, b=2)
out = df1 >> bind_rows(df2)
a_type = is_float(out.a)
assert a_type
b_type = is_int(out.b)
assert b_type
def test_hierachical_data():
my_list = [dict(x=1, y="a"), dict(x=2, y="b")]
res = my_list >> bind_rows()
rows = nrow(res)
assert rows == 2
out = is_int(res.x)
assert out
out = is_character(res.y)
assert out
res = dict(x=1, y="a") >> bind_rows(dict(x=2, y="b"))
rows = nrow(res)
assert rows == 2
out = is_int(res.x)
assert out
out = is_character(res.y)
assert out
def test_group_split_bind_rows_round_trip():
iris["Species"] = iris["Species"].astype("category")
setosa = iris >> filter(f.Species == "setosa")
chunks = setosa >> group_split.list(f.Species)
assert len(chunks) == 1
assert bind_rows(chunks).equals(setosa)
chunks = setosa >> group_split.list(f.Species, _drop=False)
assert len(chunks) == 3
assert_frame_equal(chunks[0], setosa)
def avg_weights_and_filter(owfiles):
_log("- Averaging bin weights")
ofile = outfile.parent / "_avg_weights_filtered.bed"
df = None
for owfile in owfiles:
tmp = pandas.read_csv(owfile, sep="\t", header=0)
df = df >> bind_rows(tmp)
df = df >> group_by(f.chrom1, f.start1, f.end1) >> summarise(
chrom=f.chrom1,
start=f.start1,
end=f.end1,
name=paste(f.name, collapse=":"),
score=mean(f.weight),
strand="+",
) >> filter_(
f.score >= cutoff
) >> ungroup() >> select(
~f.chrom1, ~f.start1, ~f.end1,
)
df.to_csv(ofile, sep="\t", index=False, header=False)
return ofile, len(df.columns)
def test_empty_dict():
df = bind_rows({})
d = df >> dim()
assert d == (0, 0)
def test_reorder_cols():
df = tibble(a=1, b=2, c=3, d=4, e=5, f=6)
df_scramble = df[sample(df.columns)]
out = df >> bind_rows(df_scramble)
assert out.columns.tolist() == list("abcdef")
def test_bind_rows_grouped():
df = tibble(x=[1, 2, 3]) >> group_by(f.x)
out = bind_rows(df, {"x": 4})
assert_iterable_equal(out.x.obj, [1, 2, 3, 4])
def test_cat_ordered():
df = tibble(x=factor([1, 2, 3], ordered=True))
y = bind_rows(df, df)
assert y.x.cat.ordered
def test_non_existing_col():
# fill with NA, but not convert whole column to NAs
df1 = tibble(x=letters)
df2 = tibble(x=letters[:10], y=letters[:10])
out = df1 >> bind_rows(df2)
assert not out.y.isna().all()
def test_wrong_first_argument():
with pytest.raises(NotImplementedError):
1 >> bind_rows()
