bpo-36546: More tests: type preservation and equal inputs (#13000)
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@ -2153,12 +2153,11 @@ class TestQuantiles(unittest.TestCase):
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]:
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self.assertEqual(expected, quantiles(data, n=n))
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self.assertEqual(len(quantiles(data, n=n)), n - 1)
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self.assertEqual(list(map(float, expected)),
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quantiles(map(Decimal, data), n=n))
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self.assertEqual(list(map(Decimal, expected)),
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quantiles(map(Decimal, data), n=n))
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self.assertEqual(list(map(Fraction, expected)),
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quantiles(map(Fraction, data), n=n))
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# Preserve datatype when possible
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for datatype in (float, Decimal, Fraction):
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result = quantiles(map(datatype, data), n=n)
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self.assertTrue(all(type(x) == datatype) for x in result)
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self.assertEqual(result, list(map(datatype, expected)))
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# Invariant under tranlation and scaling
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def f(x):
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return 3.5 * x - 1234.675
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@ -2199,12 +2198,11 @@ class TestQuantiles(unittest.TestCase):
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]:
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self.assertEqual(expected, quantiles(data, n=n, method="inclusive"))
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self.assertEqual(len(quantiles(data, n=n, method="inclusive")), n - 1)
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self.assertEqual(list(map(float, expected)),
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quantiles(map(Decimal, data), n=n, method="inclusive"))
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self.assertEqual(list(map(Decimal, expected)),
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quantiles(map(Decimal, data), n=n, method="inclusive"))
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self.assertEqual(list(map(Fraction, expected)),
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quantiles(map(Fraction, data), n=n, method="inclusive"))
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# Preserve datatype when possible
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for datatype in (float, Decimal, Fraction):
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result = quantiles(map(datatype, data), n=n, method="inclusive")
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self.assertTrue(all(type(x) == datatype) for x in result)
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self.assertEqual(result, list(map(datatype, expected)))
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# Invariant under tranlation and scaling
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def f(x):
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return 3.5 * x - 1234.675
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@ -2222,6 +2220,14 @@ class TestQuantiles(unittest.TestCase):
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self.assertTrue(all(math.isclose(e, a, abs_tol=0.0001)
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for e, a in zip(expected, actual)))
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def test_equal_inputs(self):
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quantiles = statistics.quantiles
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for n in range(2, 10):
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data = [10.0] * n
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self.assertEqual(quantiles(data), [10.0, 10.0, 10.0])
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self.assertEqual(quantiles(data, method='inclusive'),
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[10.0, 10.0, 10.0])
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def test_equal_sized_groups(self):
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quantiles = statistics.quantiles
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total = 10_000
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