import numpy as np
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import pytest
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import pandas as pd
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import pandas._testing as tm
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from pandas.core.arrays import FloatingArray
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from pandas.tests.arrays.masked_shared import (
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ComparisonOps,
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NumericOps,
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)
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class TestComparisonOps(NumericOps, ComparisonOps):
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@pytest.mark.parametrize("other", [True, False, pd.NA, -1.0, 0.0, 1])
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def test_scalar(self, other, comparison_op, dtype):
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ComparisonOps.test_scalar(self, other, comparison_op, dtype)
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def test_compare_with_integerarray(self, comparison_op):
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op = comparison_op
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a = pd.array([0, 1, None] * 3, dtype="Int64")
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b = pd.array([0] * 3 + [1] * 3 + [None] * 3, dtype="Float64")
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other = b.astype("Int64")
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expected = op(a, other)
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result = op(a, b)
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tm.assert_extension_array_equal(result, expected)
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expected = op(other, a)
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result = op(b, a)
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tm.assert_extension_array_equal(result, expected)
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def test_equals():
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# GH-30652
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# equals is generally tested in /tests/extension/base/methods, but this
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# specifically tests that two arrays of the same class but different dtype
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# do not evaluate equal
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a1 = pd.array([1, 2, None], dtype="Float64")
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a2 = pd.array([1, 2, None], dtype="Float32")
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assert a1.equals(a2) is False
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def test_equals_nan_vs_na():
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# GH#44382
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mask = np.zeros(3, dtype=bool)
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data = np.array([1.0, np.nan, 3.0], dtype=np.float64)
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left = FloatingArray(data, mask)
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assert left.equals(left)
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tm.assert_extension_array_equal(left, left)
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assert left.equals(left.copy())
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assert left.equals(FloatingArray(data.copy(), mask.copy()))
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mask2 = np.array([False, True, False], dtype=bool)
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data2 = np.array([1.0, 2.0, 3.0], dtype=np.float64)
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right = FloatingArray(data2, mask2)
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assert right.equals(right)
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tm.assert_extension_array_equal(right, right)
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assert not left.equals(right)
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# with mask[1] = True, the only difference is data[1], which should
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# not matter for equals
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mask[1] = True
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assert left.equals(right)
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