490 lines
17 KiB
Python
490 lines
17 KiB
Python
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from datetime import datetime
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import operator
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import numpy as np
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import pytest
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from pandas import (
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DataFrame,
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Index,
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Series,
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bdate_range,
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)
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import pandas._testing as tm
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from pandas.core import ops
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class TestSeriesLogicalOps:
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@pytest.mark.parametrize("bool_op", [operator.and_, operator.or_, operator.xor])
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def test_bool_operators_with_nas(self, bool_op):
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# boolean &, |, ^ should work with object arrays and propagate NAs
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ser = Series(bdate_range("1/1/2000", periods=10), dtype=object)
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ser[::2] = np.nan
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mask = ser.isna()
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filled = ser.fillna(ser[0])
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result = bool_op(ser < ser[9], ser > ser[3])
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expected = bool_op(filled < filled[9], filled > filled[3])
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expected[mask] = False
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tm.assert_series_equal(result, expected)
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def test_logical_operators_bool_dtype_with_empty(self):
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# GH#9016: support bitwise op for integer types
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index = list("bca")
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s_tft = Series([True, False, True], index=index)
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s_fff = Series([False, False, False], index=index)
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s_empty = Series([], dtype=object)
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res = s_tft & s_empty
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expected = s_fff
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tm.assert_series_equal(res, expected)
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res = s_tft | s_empty
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expected = s_tft
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tm.assert_series_equal(res, expected)
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def test_logical_operators_int_dtype_with_int_dtype(self):
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# GH#9016: support bitwise op for integer types
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s_0123 = Series(range(4), dtype="int64")
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s_3333 = Series([3] * 4)
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s_4444 = Series([4] * 4)
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res = s_0123 & s_3333
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expected = Series(range(4), dtype="int64")
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tm.assert_series_equal(res, expected)
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res = s_0123 | s_4444
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expected = Series(range(4, 8), dtype="int64")
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tm.assert_series_equal(res, expected)
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s_1111 = Series([1] * 4, dtype="int8")
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res = s_0123 & s_1111
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expected = Series([0, 1, 0, 1], dtype="int64")
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tm.assert_series_equal(res, expected)
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res = s_0123.astype(np.int16) | s_1111.astype(np.int32)
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expected = Series([1, 1, 3, 3], dtype="int32")
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tm.assert_series_equal(res, expected)
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def test_logical_operators_int_dtype_with_int_scalar(self):
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# GH#9016: support bitwise op for integer types
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s_0123 = Series(range(4), dtype="int64")
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res = s_0123 & 0
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expected = Series([0] * 4)
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tm.assert_series_equal(res, expected)
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res = s_0123 & 1
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expected = Series([0, 1, 0, 1])
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tm.assert_series_equal(res, expected)
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def test_logical_operators_int_dtype_with_float(self):
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# GH#9016: support bitwise op for integer types
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s_0123 = Series(range(4), dtype="int64")
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msg = "Cannot perform.+with a dtyped.+array and scalar of type"
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with pytest.raises(TypeError, match=msg):
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s_0123 & np.NaN
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with pytest.raises(TypeError, match=msg):
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s_0123 & 3.14
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msg = "unsupported operand type.+for &:"
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with pytest.raises(TypeError, match=msg):
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s_0123 & [0.1, 4, 3.14, 2]
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with pytest.raises(TypeError, match=msg):
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s_0123 & np.array([0.1, 4, 3.14, 2])
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with pytest.raises(TypeError, match=msg):
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s_0123 & Series([0.1, 4, -3.14, 2])
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def test_logical_operators_int_dtype_with_str(self):
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s_1111 = Series([1] * 4, dtype="int8")
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msg = "Cannot perform 'and_' with a dtyped.+array and scalar of type"
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with pytest.raises(TypeError, match=msg):
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s_1111 & "a"
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with pytest.raises(TypeError, match="unsupported operand.+for &"):
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s_1111 & ["a", "b", "c", "d"]
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def test_logical_operators_int_dtype_with_bool(self):
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# GH#9016: support bitwise op for integer types
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s_0123 = Series(range(4), dtype="int64")
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expected = Series([False] * 4)
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result = s_0123 & False
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tm.assert_series_equal(result, expected)
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result = s_0123 & [False]
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tm.assert_series_equal(result, expected)
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result = s_0123 & (False,)
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tm.assert_series_equal(result, expected)
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result = s_0123 ^ False
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expected = Series([False, True, True, True])
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tm.assert_series_equal(result, expected)
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def test_logical_operators_int_dtype_with_object(self):
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# GH#9016: support bitwise op for integer types
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s_0123 = Series(range(4), dtype="int64")
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result = s_0123 & Series([False, np.NaN, False, False])
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expected = Series([False] * 4)
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tm.assert_series_equal(result, expected)
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s_abNd = Series(["a", "b", np.NaN, "d"])
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with pytest.raises(TypeError, match="unsupported.* 'int' and 'str'"):
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s_0123 & s_abNd
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def test_logical_operators_bool_dtype_with_int(self):
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index = list("bca")
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s_tft = Series([True, False, True], index=index)
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s_fff = Series([False, False, False], index=index)
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res = s_tft & 0
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expected = s_fff
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tm.assert_series_equal(res, expected)
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res = s_tft & 1
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expected = s_tft
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tm.assert_series_equal(res, expected)
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def test_logical_ops_bool_dtype_with_ndarray(self):
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# make sure we operate on ndarray the same as Series
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left = Series([True, True, True, False, True])
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right = [True, False, None, True, np.nan]
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expected = Series([True, False, False, False, False])
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result = left & right
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tm.assert_series_equal(result, expected)
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result = left & np.array(right)
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tm.assert_series_equal(result, expected)
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result = left & Index(right)
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tm.assert_series_equal(result, expected)
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result = left & Series(right)
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tm.assert_series_equal(result, expected)
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expected = Series([True, True, True, True, True])
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result = left | right
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tm.assert_series_equal(result, expected)
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result = left | np.array(right)
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tm.assert_series_equal(result, expected)
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result = left | Index(right)
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tm.assert_series_equal(result, expected)
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result = left | Series(right)
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tm.assert_series_equal(result, expected)
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expected = Series([False, True, True, True, True])
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result = left ^ right
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tm.assert_series_equal(result, expected)
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result = left ^ np.array(right)
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tm.assert_series_equal(result, expected)
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result = left ^ Index(right)
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tm.assert_series_equal(result, expected)
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result = left ^ Series(right)
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tm.assert_series_equal(result, expected)
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def test_logical_operators_int_dtype_with_bool_dtype_and_reindex(self):
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# GH#9016: support bitwise op for integer types
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# with non-matching indexes, logical operators will cast to object
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# before operating
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index = list("bca")
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s_tft = Series([True, False, True], index=index)
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s_tft = Series([True, False, True], index=index)
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s_tff = Series([True, False, False], index=index)
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s_0123 = Series(range(4), dtype="int64")
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# s_0123 will be all false now because of reindexing like s_tft
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expected = Series([False] * 7, index=[0, 1, 2, 3, "a", "b", "c"])
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result = s_tft & s_0123
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tm.assert_series_equal(result, expected)
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expected = Series([False] * 7, index=[0, 1, 2, 3, "a", "b", "c"])
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result = s_0123 & s_tft
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tm.assert_series_equal(result, expected)
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s_a0b1c0 = Series([1], list("b"))
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res = s_tft & s_a0b1c0
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expected = s_tff.reindex(list("abc"))
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tm.assert_series_equal(res, expected)
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res = s_tft | s_a0b1c0
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expected = s_tft.reindex(list("abc"))
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tm.assert_series_equal(res, expected)
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def test_scalar_na_logical_ops_corners(self):
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s = Series([2, 3, 4, 5, 6, 7, 8, 9, 10])
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msg = "Cannot perform.+with a dtyped.+array and scalar of type"
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with pytest.raises(TypeError, match=msg):
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s & datetime(2005, 1, 1)
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s = Series([2, 3, 4, 5, 6, 7, 8, 9, datetime(2005, 1, 1)])
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s[::2] = np.nan
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expected = Series(True, index=s.index)
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expected[::2] = False
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result = s & list(s)
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tm.assert_series_equal(result, expected)
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def test_scalar_na_logical_ops_corners_aligns(self):
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s = Series([2, 3, 4, 5, 6, 7, 8, 9, datetime(2005, 1, 1)])
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s[::2] = np.nan
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d = DataFrame({"A": s})
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expected = DataFrame(False, index=range(9), columns=["A"] + list(range(9)))
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result = s & d
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tm.assert_frame_equal(result, expected)
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result = d & s
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tm.assert_frame_equal(result, expected)
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@pytest.mark.parametrize("op", [operator.and_, operator.or_, operator.xor])
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def test_logical_ops_with_index(self, op):
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# GH#22092, GH#19792
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ser = Series([True, True, False, False])
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idx1 = Index([True, False, True, False])
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idx2 = Index([1, 0, 1, 0])
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expected = Series([op(ser[n], idx1[n]) for n in range(len(ser))])
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result = op(ser, idx1)
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tm.assert_series_equal(result, expected)
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expected = Series([op(ser[n], idx2[n]) for n in range(len(ser))], dtype=bool)
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result = op(ser, idx2)
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tm.assert_series_equal(result, expected)
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@pytest.mark.filterwarnings("ignore:passing object-dtype arraylike:FutureWarning")
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def test_reversed_xor_with_index_returns_index(self):
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# GH#22092, GH#19792
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ser = Series([True, True, False, False])
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idx1 = Index(
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[True, False, True, False], dtype=object
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) # TODO: raises if bool-dtype
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idx2 = Index([1, 0, 1, 0])
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msg = "operating as a set operation"
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expected = Index.symmetric_difference(idx1, ser)
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with tm.assert_produces_warning(FutureWarning, match=msg):
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result = idx1 ^ ser
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tm.assert_index_equal(result, expected)
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expected = Index.symmetric_difference(idx2, ser)
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with tm.assert_produces_warning(FutureWarning, match=msg):
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result = idx2 ^ ser
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tm.assert_index_equal(result, expected)
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@pytest.mark.parametrize(
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"op",
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[
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pytest.param(
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ops.rand_,
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marks=pytest.mark.xfail(
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reason="GH#22092 Index __and__ returns Index intersection",
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raises=AssertionError,
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),
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),
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pytest.param(
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ops.ror_,
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marks=pytest.mark.xfail(
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reason="GH#22092 Index __or__ returns Index union",
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raises=AssertionError,
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),
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),
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],
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)
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def test_reversed_logical_op_with_index_returns_series(self, op):
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# GH#22092, GH#19792
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ser = Series([True, True, False, False])
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idx1 = Index([True, False, True, False])
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idx2 = Index([1, 0, 1, 0])
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msg = "operating as a set operation"
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expected = Series(op(idx1.values, ser.values))
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with tm.assert_produces_warning(FutureWarning, match=msg):
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result = op(ser, idx1)
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tm.assert_series_equal(result, expected)
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expected = Series(op(idx2.values, ser.values))
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with tm.assert_produces_warning(FutureWarning, match=msg):
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result = op(ser, idx2)
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tm.assert_series_equal(result, expected)
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@pytest.mark.parametrize(
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"op, expected",
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[
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(ops.rand_, Index([False, True])),
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(ops.ror_, Index([False, True])),
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(ops.rxor, Index([], dtype=bool)),
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],
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)
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def test_reverse_ops_with_index(self, op, expected):
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# https://github.com/pandas-dev/pandas/pull/23628
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# multi-set Index ops are buggy, so let's avoid duplicates...
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ser = Series([True, False])
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idx = Index([False, True])
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msg = "operating as a set operation"
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with tm.assert_produces_warning(FutureWarning, match=msg):
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# behaving as set ops is deprecated, will become logical ops
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result = op(ser, idx)
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tm.assert_index_equal(result, expected)
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def test_logical_ops_label_based(self):
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# GH#4947
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# logical ops should be label based
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a = Series([True, False, True], list("bca"))
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b = Series([False, True, False], list("abc"))
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expected = Series([False, True, False], list("abc"))
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result = a & b
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
expected = Series([True, True, False], list("abc"))
|
||
|
|
result = a | b
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
expected = Series([True, False, False], list("abc"))
|
||
|
|
result = a ^ b
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
# rhs is bigger
|
||
|
|
a = Series([True, False, True], list("bca"))
|
||
|
|
b = Series([False, True, False, True], list("abcd"))
|
||
|
|
|
||
|
|
expected = Series([False, True, False, False], list("abcd"))
|
||
|
|
result = a & b
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
expected = Series([True, True, False, False], list("abcd"))
|
||
|
|
result = a | b
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
# filling
|
||
|
|
|
||
|
|
# vs empty
|
||
|
|
empty = Series([], dtype=object)
|
||
|
|
|
||
|
|
result = a & empty.copy()
|
||
|
|
expected = Series([False, False, False], list("bca"))
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
result = a | empty.copy()
|
||
|
|
expected = Series([True, False, True], list("bca"))
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
# vs non-matching
|
||
|
|
result = a & Series([1], ["z"])
|
||
|
|
expected = Series([False, False, False, False], list("abcz"))
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
result = a | Series([1], ["z"])
|
||
|
|
expected = Series([True, True, False, False], list("abcz"))
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
# identity
|
||
|
|
# we would like s[s|e] == s to hold for any e, whether empty or not
|
||
|
|
for e in [
|
||
|
|
empty.copy(),
|
||
|
|
Series([1], ["z"]),
|
||
|
|
Series(np.nan, b.index),
|
||
|
|
Series(np.nan, a.index),
|
||
|
|
]:
|
||
|
|
result = a[a | e]
|
||
|
|
tm.assert_series_equal(result, a[a])
|
||
|
|
|
||
|
|
for e in [Series(["z"])]:
|
||
|
|
result = a[a | e]
|
||
|
|
tm.assert_series_equal(result, a[a])
|
||
|
|
|
||
|
|
# vs scalars
|
||
|
|
index = list("bca")
|
||
|
|
t = Series([True, False, True])
|
||
|
|
|
||
|
|
for v in [True, 1, 2]:
|
||
|
|
result = Series([True, False, True], index=index) | v
|
||
|
|
expected = Series([True, True, True], index=index)
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
msg = "Cannot perform.+with a dtyped.+array and scalar of type"
|
||
|
|
for v in [np.nan, "foo"]:
|
||
|
|
with pytest.raises(TypeError, match=msg):
|
||
|
|
t | v
|
||
|
|
|
||
|
|
for v in [False, 0]:
|
||
|
|
result = Series([True, False, True], index=index) | v
|
||
|
|
expected = Series([True, False, True], index=index)
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
for v in [True, 1]:
|
||
|
|
result = Series([True, False, True], index=index) & v
|
||
|
|
expected = Series([True, False, True], index=index)
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
for v in [False, 0]:
|
||
|
|
result = Series([True, False, True], index=index) & v
|
||
|
|
expected = Series([False, False, False], index=index)
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
msg = "Cannot perform.+with a dtyped.+array and scalar of type"
|
||
|
|
for v in [np.nan]:
|
||
|
|
with pytest.raises(TypeError, match=msg):
|
||
|
|
t & v
|
||
|
|
|
||
|
|
def test_logical_ops_df_compat(self):
|
||
|
|
# GH#1134
|
||
|
|
s1 = Series([True, False, True], index=list("ABC"), name="x")
|
||
|
|
s2 = Series([True, True, False], index=list("ABD"), name="x")
|
||
|
|
|
||
|
|
exp = Series([True, False, False, False], index=list("ABCD"), name="x")
|
||
|
|
tm.assert_series_equal(s1 & s2, exp)
|
||
|
|
tm.assert_series_equal(s2 & s1, exp)
|
||
|
|
|
||
|
|
# True | np.nan => True
|
||
|
|
exp_or1 = Series([True, True, True, False], index=list("ABCD"), name="x")
|
||
|
|
tm.assert_series_equal(s1 | s2, exp_or1)
|
||
|
|
# np.nan | True => np.nan, filled with False
|
||
|
|
exp_or = Series([True, True, False, False], index=list("ABCD"), name="x")
|
||
|
|
tm.assert_series_equal(s2 | s1, exp_or)
|
||
|
|
|
||
|
|
# DataFrame doesn't fill nan with False
|
||
|
|
tm.assert_frame_equal(s1.to_frame() & s2.to_frame(), exp.to_frame())
|
||
|
|
tm.assert_frame_equal(s2.to_frame() & s1.to_frame(), exp.to_frame())
|
||
|
|
|
||
|
|
exp = DataFrame({"x": [True, True, np.nan, np.nan]}, index=list("ABCD"))
|
||
|
|
tm.assert_frame_equal(s1.to_frame() | s2.to_frame(), exp_or1.to_frame())
|
||
|
|
tm.assert_frame_equal(s2.to_frame() | s1.to_frame(), exp_or.to_frame())
|
||
|
|
|
||
|
|
# different length
|
||
|
|
s3 = Series([True, False, True], index=list("ABC"), name="x")
|
||
|
|
s4 = Series([True, True, True, True], index=list("ABCD"), name="x")
|
||
|
|
|
||
|
|
exp = Series([True, False, True, False], index=list("ABCD"), name="x")
|
||
|
|
tm.assert_series_equal(s3 & s4, exp)
|
||
|
|
tm.assert_series_equal(s4 & s3, exp)
|
||
|
|
|
||
|
|
# np.nan | True => np.nan, filled with False
|
||
|
|
exp_or1 = Series([True, True, True, False], index=list("ABCD"), name="x")
|
||
|
|
tm.assert_series_equal(s3 | s4, exp_or1)
|
||
|
|
# True | np.nan => True
|
||
|
|
exp_or = Series([True, True, True, True], index=list("ABCD"), name="x")
|
||
|
|
tm.assert_series_equal(s4 | s3, exp_or)
|
||
|
|
|
||
|
|
tm.assert_frame_equal(s3.to_frame() & s4.to_frame(), exp.to_frame())
|
||
|
|
tm.assert_frame_equal(s4.to_frame() & s3.to_frame(), exp.to_frame())
|
||
|
|
|
||
|
|
tm.assert_frame_equal(s3.to_frame() | s4.to_frame(), exp_or1.to_frame())
|
||
|
|
tm.assert_frame_equal(s4.to_frame() | s3.to_frame(), exp_or.to_frame())
|