LSR/env/lib/python3.6/site-packages/pandas/tests/reshape/test_util.py
2020-06-04 17:24:47 +02:00

52 lines
1.8 KiB
Python

import numpy as np
import pytest
from pandas import Index, date_range
import pandas._testing as tm
from pandas.core.reshape.util import cartesian_product
class TestCartesianProduct:
def test_simple(self):
x, y = list("ABC"), [1, 22]
result1, result2 = cartesian_product([x, y])
expected1 = np.array(["A", "A", "B", "B", "C", "C"])
expected2 = np.array([1, 22, 1, 22, 1, 22])
tm.assert_numpy_array_equal(result1, expected1)
tm.assert_numpy_array_equal(result2, expected2)
def test_datetimeindex(self):
# regression test for GitHub issue #6439
# make sure that the ordering on datetimeindex is consistent
x = date_range("2000-01-01", periods=2)
result1, result2 = [Index(y).day for y in cartesian_product([x, x])]
expected1 = Index([1, 1, 2, 2])
expected2 = Index([1, 2, 1, 2])
tm.assert_index_equal(result1, expected1)
tm.assert_index_equal(result2, expected2)
def test_empty(self):
# product of empty factors
X = [[], [0, 1], []]
Y = [[], [], ["a", "b", "c"]]
for x, y in zip(X, Y):
expected1 = np.array([], dtype=np.asarray(x).dtype)
expected2 = np.array([], dtype=np.asarray(y).dtype)
result1, result2 = cartesian_product([x, y])
tm.assert_numpy_array_equal(result1, expected1)
tm.assert_numpy_array_equal(result2, expected2)
# empty product (empty input):
result = cartesian_product([])
expected = []
assert result == expected
@pytest.mark.parametrize(
"X", [1, [1], [1, 2], [[1], 2], "a", ["a"], ["a", "b"], [["a"], "b"]]
)
def test_invalid_input(self, X):
msg = "Input must be a list-like of list-likes"
with pytest.raises(TypeError, match=msg):
cartesian_product(X=X)