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80 lines
2.8 KiB
80 lines
2.8 KiB
import numpy as np
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import pytest
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from pandas import (
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Index,
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date_range,
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)
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import pandas._testing as tm
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from pandas.core.reshape.util import cartesian_product
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class TestCartesianProduct:
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def test_simple(self):
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x, y = list("ABC"), [1, 22]
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result1, result2 = cartesian_product([x, y])
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expected1 = np.array(["A", "A", "B", "B", "C", "C"])
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expected2 = np.array([1, 22, 1, 22, 1, 22])
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tm.assert_numpy_array_equal(result1, expected1)
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tm.assert_numpy_array_equal(result2, expected2)
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def test_datetimeindex(self):
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# regression test for GitHub issue #6439
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# make sure that the ordering on datetimeindex is consistent
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x = date_range("2000-01-01", periods=2)
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result1, result2 = (Index(y).day for y in cartesian_product([x, x]))
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expected1 = Index([1, 1, 2, 2])
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expected2 = Index([1, 2, 1, 2])
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tm.assert_index_equal(result1, expected1)
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tm.assert_index_equal(result2, expected2)
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def test_tzaware_retained(self):
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x = date_range("2000-01-01", periods=2, tz="US/Pacific")
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y = np.array([3, 4])
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result1, result2 = cartesian_product([x, y])
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expected = x.repeat(2)
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tm.assert_index_equal(result1, expected)
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def test_tzaware_retained_categorical(self):
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x = date_range("2000-01-01", periods=2, tz="US/Pacific").astype("category")
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y = np.array([3, 4])
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result1, result2 = cartesian_product([x, y])
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expected = x.repeat(2)
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tm.assert_index_equal(result1, expected)
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def test_empty(self):
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# product of empty factors
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X = [[], [0, 1], []]
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Y = [[], [], ["a", "b", "c"]]
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for x, y in zip(X, Y):
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expected1 = np.array([], dtype=np.asarray(x).dtype)
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expected2 = np.array([], dtype=np.asarray(y).dtype)
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result1, result2 = cartesian_product([x, y])
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tm.assert_numpy_array_equal(result1, expected1)
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tm.assert_numpy_array_equal(result2, expected2)
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# empty product (empty input):
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result = cartesian_product([])
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expected = []
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assert result == expected
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@pytest.mark.parametrize(
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"X", [1, [1], [1, 2], [[1], 2], "a", ["a"], ["a", "b"], [["a"], "b"]]
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)
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def test_invalid_input(self, X):
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msg = "Input must be a list-like of list-likes"
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with pytest.raises(TypeError, match=msg):
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cartesian_product(X=X)
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def test_exceed_product_space(self):
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# GH31355: raise useful error when produce space is too large
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msg = "Product space too large to allocate arrays!"
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with pytest.raises(ValueError, match=msg):
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dims = [np.arange(0, 22, dtype=np.int16) for i in range(12)] + [
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(np.arange(15128, dtype=np.int16)),
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]
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cartesian_product(X=dims)
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