You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
144 lines
4.0 KiB
144 lines
4.0 KiB
import numpy as np
|
|
import pytest
|
|
|
|
import pandas as pd
|
|
import pandas._testing as tm
|
|
|
|
|
|
def test_basic():
|
|
s = pd.Series([[0, 1, 2], np.nan, [], (3, 4)], index=list("abcd"), name="foo")
|
|
result = s.explode()
|
|
expected = pd.Series(
|
|
[0, 1, 2, np.nan, np.nan, 3, 4], index=list("aaabcdd"), dtype=object, name="foo"
|
|
)
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
|
|
def test_mixed_type():
|
|
s = pd.Series(
|
|
[[0, 1, 2], np.nan, None, np.array([]), pd.Series(["a", "b"])], name="foo"
|
|
)
|
|
result = s.explode()
|
|
expected = pd.Series(
|
|
[0, 1, 2, np.nan, None, np.nan, "a", "b"],
|
|
index=[0, 0, 0, 1, 2, 3, 4, 4],
|
|
dtype=object,
|
|
name="foo",
|
|
)
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
|
|
def test_empty():
|
|
s = pd.Series(dtype=object)
|
|
result = s.explode()
|
|
expected = s.copy()
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
|
|
def test_nested_lists():
|
|
s = pd.Series([[[1, 2, 3]], [1, 2], 1])
|
|
result = s.explode()
|
|
expected = pd.Series([[1, 2, 3], 1, 2, 1], index=[0, 1, 1, 2])
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
|
|
def test_multi_index():
|
|
s = pd.Series(
|
|
[[0, 1, 2], np.nan, [], (3, 4)],
|
|
name="foo",
|
|
index=pd.MultiIndex.from_product([list("ab"), range(2)], names=["foo", "bar"]),
|
|
)
|
|
result = s.explode()
|
|
index = pd.MultiIndex.from_tuples(
|
|
[("a", 0), ("a", 0), ("a", 0), ("a", 1), ("b", 0), ("b", 1), ("b", 1)],
|
|
names=["foo", "bar"],
|
|
)
|
|
expected = pd.Series(
|
|
[0, 1, 2, np.nan, np.nan, 3, 4], index=index, dtype=object, name="foo"
|
|
)
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
|
|
def test_large():
|
|
s = pd.Series([range(256)]).explode()
|
|
result = s.explode()
|
|
tm.assert_series_equal(result, s)
|
|
|
|
|
|
def test_invert_array():
|
|
df = pd.DataFrame({"a": pd.date_range("20190101", periods=3, tz="UTC")})
|
|
|
|
listify = df.apply(lambda x: x.array, axis=1)
|
|
result = listify.explode()
|
|
tm.assert_series_equal(result, df["a"].rename())
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"s", [pd.Series([1, 2, 3]), pd.Series(pd.date_range("2019", periods=3, tz="UTC"))]
|
|
)
|
|
def non_object_dtype(s):
|
|
result = s.explode()
|
|
tm.assert_series_equal(result, s)
|
|
|
|
|
|
def test_typical_usecase():
|
|
|
|
df = pd.DataFrame(
|
|
[{"var1": "a,b,c", "var2": 1}, {"var1": "d,e,f", "var2": 2}],
|
|
columns=["var1", "var2"],
|
|
)
|
|
exploded = df.var1.str.split(",").explode()
|
|
result = df[["var2"]].join(exploded)
|
|
expected = pd.DataFrame(
|
|
{"var2": [1, 1, 1, 2, 2, 2], "var1": list("abcdef")},
|
|
columns=["var2", "var1"],
|
|
index=[0, 0, 0, 1, 1, 1],
|
|
)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
|
|
def test_nested_EA():
|
|
# a nested EA array
|
|
s = pd.Series(
|
|
[
|
|
pd.date_range("20170101", periods=3, tz="UTC"),
|
|
pd.date_range("20170104", periods=3, tz="UTC"),
|
|
]
|
|
)
|
|
result = s.explode()
|
|
expected = pd.Series(
|
|
pd.date_range("20170101", periods=6, tz="UTC"), index=[0, 0, 0, 1, 1, 1]
|
|
)
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
|
|
def test_duplicate_index():
|
|
# GH 28005
|
|
s = pd.Series([[1, 2], [3, 4]], index=[0, 0])
|
|
result = s.explode()
|
|
expected = pd.Series([1, 2, 3, 4], index=[0, 0, 0, 0], dtype=object)
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
|
|
def test_ignore_index():
|
|
# GH 34932
|
|
s = pd.Series([[1, 2], [3, 4]])
|
|
result = s.explode(ignore_index=True)
|
|
expected = pd.Series([1, 2, 3, 4], index=[0, 1, 2, 3], dtype=object)
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
|
|
def test_explode_sets():
|
|
# https://github.com/pandas-dev/pandas/issues/35614
|
|
s = pd.Series([{"a", "b", "c"}], index=[1])
|
|
result = s.explode().sort_values()
|
|
expected = pd.Series(["a", "b", "c"], index=[1, 1, 1])
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
|
|
def test_explode_scalars_can_ignore_index():
|
|
# https://github.com/pandas-dev/pandas/issues/40487
|
|
s = pd.Series([1, 2, 3], index=["a", "b", "c"])
|
|
result = s.explode(ignore_index=True)
|
|
expected = pd.Series([1, 2, 3])
|
|
tm.assert_series_equal(result, expected)
|
|
|