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