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.arrays import BooleanArray
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from pandas.core.arrays.boolean import coerce_to_array
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def test_boolean_array_constructor():
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values = np.array([True, False, True, False], dtype="bool")
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mask = np.array([False, False, False, True], dtype="bool")
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result = BooleanArray(values, mask)
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expected = pd.array([True, False, True, None], dtype="boolean")
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tm.assert_extension_array_equal(result, expected)
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with pytest.raises(TypeError, match="values should be boolean numpy array"):
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BooleanArray(values.tolist(), mask)
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with pytest.raises(TypeError, match="mask should be boolean numpy array"):
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BooleanArray(values, mask.tolist())
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with pytest.raises(TypeError, match="values should be boolean numpy array"):
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BooleanArray(values.astype(int), mask)
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with pytest.raises(TypeError, match="mask should be boolean numpy array"):
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BooleanArray(values, None)
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with pytest.raises(ValueError, match="values.shape must match mask.shape"):
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BooleanArray(values.reshape(1, -1), mask)
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with pytest.raises(ValueError, match="values.shape must match mask.shape"):
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BooleanArray(values, mask.reshape(1, -1))
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def test_boolean_array_constructor_copy():
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values = np.array([True, False, True, False], dtype="bool")
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mask = np.array([False, False, False, True], dtype="bool")
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result = BooleanArray(values, mask)
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assert result._data is values
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assert result._mask is mask
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result = BooleanArray(values, mask, copy=True)
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assert result._data is not values
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assert result._mask is not mask
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def test_to_boolean_array():
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expected = BooleanArray(
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np.array([True, False, True]), np.array([False, False, False])
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)
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result = pd.array([True, False, True], dtype="boolean")
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tm.assert_extension_array_equal(result, expected)
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result = pd.array(np.array([True, False, True]), dtype="boolean")
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tm.assert_extension_array_equal(result, expected)
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result = pd.array(np.array([True, False, True], dtype=object), dtype="boolean")
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tm.assert_extension_array_equal(result, expected)
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# with missing values
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expected = BooleanArray(
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np.array([True, False, True]), np.array([False, False, True])
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)
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result = pd.array([True, False, None], dtype="boolean")
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tm.assert_extension_array_equal(result, expected)
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result = pd.array(np.array([True, False, None], dtype=object), dtype="boolean")
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tm.assert_extension_array_equal(result, expected)
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def test_to_boolean_array_all_none():
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expected = BooleanArray(np.array([True, True, True]), np.array([True, True, True]))
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result = pd.array([None, None, None], dtype="boolean")
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tm.assert_extension_array_equal(result, expected)
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result = pd.array(np.array([None, None, None], dtype=object), dtype="boolean")
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tm.assert_extension_array_equal(result, expected)
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@pytest.mark.parametrize(
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"a, b",
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[
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([True, False, None, np.nan, pd.NA], [True, False, None, None, None]),
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([True, np.nan], [True, None]),
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([True, pd.NA], [True, None]),
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([np.nan, np.nan], [None, None]),
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(np.array([np.nan, np.nan], dtype=float), [None, None]),
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],
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)
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def test_to_boolean_array_missing_indicators(a, b):
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result = pd.array(a, dtype="boolean")
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expected = pd.array(b, dtype="boolean")
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tm.assert_extension_array_equal(result, expected)
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@pytest.mark.parametrize(
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"values",
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[
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["foo", "bar"],
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["1", "2"],
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# "foo",
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[1, 2],
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[1.0, 2.0],
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pd.date_range("20130101", periods=2),
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np.array(["foo"]),
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np.array([1, 2]),
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np.array([1.0, 2.0]),
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[np.nan, {"a": 1}],
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],
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)
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def test_to_boolean_array_error(values):
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# error in converting existing arrays to BooleanArray
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msg = "Need to pass bool-like value"
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with pytest.raises(TypeError, match=msg):
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pd.array(values, dtype="boolean")
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def test_to_boolean_array_from_integer_array():
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result = pd.array(np.array([1, 0, 1, 0]), dtype="boolean")
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expected = pd.array([True, False, True, False], dtype="boolean")
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tm.assert_extension_array_equal(result, expected)
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# with missing values
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result = pd.array(np.array([1, 0, 1, None]), dtype="boolean")
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expected = pd.array([True, False, True, None], dtype="boolean")
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tm.assert_extension_array_equal(result, expected)
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def test_to_boolean_array_from_float_array():
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result = pd.array(np.array([1.0, 0.0, 1.0, 0.0]), dtype="boolean")
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expected = pd.array([True, False, True, False], dtype="boolean")
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tm.assert_extension_array_equal(result, expected)
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# with missing values
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result = pd.array(np.array([1.0, 0.0, 1.0, np.nan]), dtype="boolean")
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expected = pd.array([True, False, True, None], dtype="boolean")
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tm.assert_extension_array_equal(result, expected)
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def test_to_boolean_array_integer_like():
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# integers of 0's and 1's
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result = pd.array([1, 0, 1, 0], dtype="boolean")
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expected = pd.array([True, False, True, False], dtype="boolean")
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tm.assert_extension_array_equal(result, expected)
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# with missing values
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result = pd.array([1, 0, 1, None], dtype="boolean")
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expected = pd.array([True, False, True, None], dtype="boolean")
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tm.assert_extension_array_equal(result, expected)
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def test_coerce_to_array():
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# TODO this is currently not public API
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values = np.array([True, False, True, False], dtype="bool")
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mask = np.array([False, False, False, True], dtype="bool")
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result = BooleanArray(*coerce_to_array(values, mask=mask))
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expected = BooleanArray(values, mask)
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tm.assert_extension_array_equal(result, expected)
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assert result._data is values
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assert result._mask is mask
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result = BooleanArray(*coerce_to_array(values, mask=mask, copy=True))
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expected = BooleanArray(values, mask)
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tm.assert_extension_array_equal(result, expected)
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assert result._data is not values
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assert result._mask is not mask
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# mixed missing from values and mask
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values = [True, False, None, False]
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mask = np.array([False, False, False, True], dtype="bool")
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result = BooleanArray(*coerce_to_array(values, mask=mask))
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expected = BooleanArray(
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np.array([True, False, True, True]), np.array([False, False, True, True])
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)
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tm.assert_extension_array_equal(result, expected)
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result = BooleanArray(*coerce_to_array(np.array(values, dtype=object), mask=mask))
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tm.assert_extension_array_equal(result, expected)
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result = BooleanArray(*coerce_to_array(values, mask=mask.tolist()))
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tm.assert_extension_array_equal(result, expected)
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# raise errors for wrong dimension
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values = np.array([True, False, True, False], dtype="bool")
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mask = np.array([False, False, False, True], dtype="bool")
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# passing 2D values is OK as long as no mask
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coerce_to_array(values.reshape(1, -1))
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with pytest.raises(ValueError, match="values.shape and mask.shape must match"):
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coerce_to_array(values.reshape(1, -1), mask=mask)
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with pytest.raises(ValueError, match="values.shape and mask.shape must match"):
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coerce_to_array(values, mask=mask.reshape(1, -1))
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def test_coerce_to_array_from_boolean_array():
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# passing BooleanArray to coerce_to_array
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values = np.array([True, False, True, False], dtype="bool")
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mask = np.array([False, False, False, True], dtype="bool")
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arr = BooleanArray(values, mask)
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result = BooleanArray(*coerce_to_array(arr))
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tm.assert_extension_array_equal(result, arr)
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# no copy
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assert result._data is arr._data
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assert result._mask is arr._mask
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result = BooleanArray(*coerce_to_array(arr), copy=True)
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tm.assert_extension_array_equal(result, arr)
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assert result._data is not arr._data
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assert result._mask is not arr._mask
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with pytest.raises(ValueError, match="cannot pass mask for BooleanArray input"):
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coerce_to_array(arr, mask=mask)
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def test_coerce_to_numpy_array():
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# with missing values -> object dtype
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arr = pd.array([True, False, None], dtype="boolean")
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result = np.array(arr)
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expected = np.array([True, False, pd.NA], dtype="object")
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tm.assert_numpy_array_equal(result, expected)
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# also with no missing values -> object dtype
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arr = pd.array([True, False, True], dtype="boolean")
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result = np.array(arr)
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expected = np.array([True, False, True], dtype="object")
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tm.assert_numpy_array_equal(result, expected)
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# force bool dtype
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result = np.array(arr, dtype="bool")
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expected = np.array([True, False, True], dtype="bool")
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tm.assert_numpy_array_equal(result, expected)
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# with missing values will raise error
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arr = pd.array([True, False, None], dtype="boolean")
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msg = (
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"cannot convert to 'bool'-dtype NumPy array with missing values. "
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"Specify an appropriate 'na_value' for this dtype."
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)
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with pytest.raises(ValueError, match=msg):
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np.array(arr, dtype="bool")
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def test_to_boolean_array_from_strings():
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result = BooleanArray._from_sequence_of_strings(
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np.array(["True", "False", "1", "1.0", "0", "0.0", np.nan], dtype=object)
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)
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expected = BooleanArray(
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np.array([True, False, True, True, False, False, False]),
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np.array([False, False, False, False, False, False, True]),
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)
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tm.assert_extension_array_equal(result, expected)
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def test_to_boolean_array_from_strings_invalid_string():
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with pytest.raises(ValueError, match="cannot be cast"):
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BooleanArray._from_sequence_of_strings(["donkey"])
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@pytest.mark.parametrize("box", [True, False], ids=["series", "array"])
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def test_to_numpy(box):
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con = pd.Series if box else pd.array
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# default (with or without missing values) -> object dtype
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arr = con([True, False, True], dtype="boolean")
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result = arr.to_numpy()
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expected = np.array([True, False, True], dtype="object")
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tm.assert_numpy_array_equal(result, expected)
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arr = con([True, False, None], dtype="boolean")
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result = arr.to_numpy()
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expected = np.array([True, False, pd.NA], dtype="object")
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tm.assert_numpy_array_equal(result, expected)
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arr = con([True, False, None], dtype="boolean")
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result = arr.to_numpy(dtype="str")
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expected = np.array([True, False, pd.NA], dtype=f"{tm.ENDIAN}U5")
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tm.assert_numpy_array_equal(result, expected)
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# no missing values -> can convert to bool, otherwise raises
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arr = con([True, False, True], dtype="boolean")
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result = arr.to_numpy(dtype="bool")
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expected = np.array([True, False, True], dtype="bool")
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tm.assert_numpy_array_equal(result, expected)
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arr = con([True, False, None], dtype="boolean")
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with pytest.raises(ValueError, match="cannot convert to 'bool'-dtype"):
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result = arr.to_numpy(dtype="bool")
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# specify dtype and na_value
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arr = con([True, False, None], dtype="boolean")
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result = arr.to_numpy(dtype=object, na_value=None)
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expected = np.array([True, False, None], dtype="object")
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tm.assert_numpy_array_equal(result, expected)
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result = arr.to_numpy(dtype=bool, na_value=False)
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expected = np.array([True, False, False], dtype="bool")
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tm.assert_numpy_array_equal(result, expected)
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result = arr.to_numpy(dtype="int64", na_value=-99)
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expected = np.array([1, 0, -99], dtype="int64")
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tm.assert_numpy_array_equal(result, expected)
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result = arr.to_numpy(dtype="float64", na_value=np.nan)
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expected = np.array([1, 0, np.nan], dtype="float64")
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tm.assert_numpy_array_equal(result, expected)
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# converting to int or float without specifying na_value raises
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with pytest.raises(ValueError, match="cannot convert to 'int64'-dtype"):
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arr.to_numpy(dtype="int64")
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with pytest.raises(ValueError, match="cannot convert to 'float64'-dtype"):
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arr.to_numpy(dtype="float64")
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def test_to_numpy_copy():
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# to_numpy can be zero-copy if no missing values
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arr = pd.array([True, False, True], dtype="boolean")
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result = arr.to_numpy(dtype=bool)
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result[0] = False
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tm.assert_extension_array_equal(
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arr, pd.array([False, False, True], dtype="boolean")
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)
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arr = pd.array([True, False, True], dtype="boolean")
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result = arr.to_numpy(dtype=bool, copy=True)
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result[0] = False
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tm.assert_extension_array_equal(arr, pd.array([True, False, True], dtype="boolean"))
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