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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@pytest.mark.parametrize(
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"ufunc", [np.add, np.logical_or, np.logical_and, np.logical_xor]
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)
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def test_ufuncs_binary(ufunc):
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# two BooleanArrays
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a = pd.array([True, False, None], dtype="boolean")
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result = ufunc(a, a)
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expected = pd.array(ufunc(a._data, a._data), dtype="boolean")
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expected[a._mask] = np.nan
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tm.assert_extension_array_equal(result, expected)
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s = pd.Series(a)
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result = ufunc(s, a)
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expected = pd.Series(ufunc(a._data, a._data), dtype="boolean")
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expected[a._mask] = np.nan
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tm.assert_series_equal(result, expected)
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# Boolean with numpy array
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arr = np.array([True, True, False])
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result = ufunc(a, arr)
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expected = pd.array(ufunc(a._data, arr), dtype="boolean")
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expected[a._mask] = np.nan
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tm.assert_extension_array_equal(result, expected)
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result = ufunc(arr, a)
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expected = pd.array(ufunc(arr, a._data), dtype="boolean")
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expected[a._mask] = np.nan
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tm.assert_extension_array_equal(result, expected)
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# BooleanArray with scalar
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result = ufunc(a, True)
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expected = pd.array(ufunc(a._data, True), dtype="boolean")
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expected[a._mask] = np.nan
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tm.assert_extension_array_equal(result, expected)
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result = ufunc(True, a)
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expected = pd.array(ufunc(True, a._data), dtype="boolean")
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expected[a._mask] = np.nan
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tm.assert_extension_array_equal(result, expected)
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# not handled types
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msg = r"operand type\(s\) all returned NotImplemented from __array_ufunc__"
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with pytest.raises(TypeError, match=msg):
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ufunc(a, "test")
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@pytest.mark.parametrize("ufunc", [np.logical_not])
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def test_ufuncs_unary(ufunc):
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a = pd.array([True, False, None], dtype="boolean")
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result = ufunc(a)
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expected = pd.array(ufunc(a._data), dtype="boolean")
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expected[a._mask] = np.nan
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tm.assert_extension_array_equal(result, expected)
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ser = pd.Series(a)
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result = ufunc(ser)
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expected = pd.Series(ufunc(a._data), dtype="boolean")
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expected[a._mask] = np.nan
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tm.assert_series_equal(result, expected)
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def test_ufunc_numeric():
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# np.sqrt on np.bool_ returns float16, which we upcast to Float32
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# bc we do not have Float16
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arr = pd.array([True, False, None], dtype="boolean")
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res = np.sqrt(arr)
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expected = pd.array([1, 0, None], dtype="Float32")
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tm.assert_extension_array_equal(res, expected)
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@pytest.mark.parametrize("values", [[True, False], [True, None]])
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def test_ufunc_reduce_raises(values):
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arr = pd.array(values, dtype="boolean")
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res = np.add.reduce(arr)
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if arr[-1] is pd.NA:
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expected = pd.NA
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else:
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expected = arr._data.sum()
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tm.assert_almost_equal(res, expected)
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def test_value_counts_na():
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arr = pd.array([True, False, pd.NA], dtype="boolean")
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result = arr.value_counts(dropna=False)
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expected = pd.Series([1, 1, 1], index=arr, dtype="Int64", name="count")
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assert expected.index.dtype == arr.dtype
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tm.assert_series_equal(result, expected)
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result = arr.value_counts(dropna=True)
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expected = pd.Series([1, 1], index=arr[:-1], dtype="Int64", name="count")
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assert expected.index.dtype == arr.dtype
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tm.assert_series_equal(result, expected)
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def test_value_counts_with_normalize():
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ser = pd.Series([True, False, pd.NA], dtype="boolean")
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result = ser.value_counts(normalize=True)
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expected = pd.Series([1, 1], index=ser[:-1], dtype="Float64", name="proportion") / 2
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assert expected.index.dtype == "boolean"
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tm.assert_series_equal(result, expected)
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def test_diff():
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a = pd.array(
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[True, True, False, False, True, None, True, None, False], dtype="boolean"
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)
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result = pd.core.algorithms.diff(a, 1)
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expected = pd.array(
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[None, False, True, False, True, None, None, None, None], dtype="boolean"
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)
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tm.assert_extension_array_equal(result, expected)
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ser = pd.Series(a)
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result = ser.diff()
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expected = pd.Series(expected)
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tm.assert_series_equal(result, expected)
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