1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
| import operator
| import re
|
| import numpy as np
| import pytest
|
| from pandas import (
| CategoricalIndex,
| DataFrame,
| Interval,
| Series,
| isnull,
| )
| import pandas._testing as tm
|
|
| class TestDataFrameLogicalOperators:
| # &, |, ^
|
| @pytest.mark.parametrize(
| "left, right, op, expected",
| [
| (
| [True, False, np.nan],
| [True, False, True],
| operator.and_,
| [True, False, False],
| ),
| (
| [True, False, True],
| [True, False, np.nan],
| operator.and_,
| [True, False, False],
| ),
| (
| [True, False, np.nan],
| [True, False, True],
| operator.or_,
| [True, False, False],
| ),
| (
| [True, False, True],
| [True, False, np.nan],
| operator.or_,
| [True, False, True],
| ),
| ],
| )
| def test_logical_operators_nans(self, left, right, op, expected, frame_or_series):
| # GH#13896
| result = op(frame_or_series(left), frame_or_series(right))
| expected = frame_or_series(expected)
|
| tm.assert_equal(result, expected)
|
| def test_logical_ops_empty_frame(self):
| # GH#5808
| # empty frames, non-mixed dtype
| df = DataFrame(index=[1])
|
| result = df & df
| tm.assert_frame_equal(result, df)
|
| result = df | df
| tm.assert_frame_equal(result, df)
|
| df2 = DataFrame(index=[1, 2])
| result = df & df2
| tm.assert_frame_equal(result, df2)
|
| dfa = DataFrame(index=[1], columns=["A"])
|
| result = dfa & dfa
| expected = DataFrame(False, index=[1], columns=["A"])
| tm.assert_frame_equal(result, expected)
|
| def test_logical_ops_bool_frame(self):
| # GH#5808
| df1a_bool = DataFrame(True, index=[1], columns=["A"])
|
| result = df1a_bool & df1a_bool
| tm.assert_frame_equal(result, df1a_bool)
|
| result = df1a_bool | df1a_bool
| tm.assert_frame_equal(result, df1a_bool)
|
| def test_logical_ops_int_frame(self):
| # GH#5808
| df1a_int = DataFrame(1, index=[1], columns=["A"])
| df1a_bool = DataFrame(True, index=[1], columns=["A"])
|
| result = df1a_int | df1a_bool
| tm.assert_frame_equal(result, df1a_bool)
|
| # Check that this matches Series behavior
| res_ser = df1a_int["A"] | df1a_bool["A"]
| tm.assert_series_equal(res_ser, df1a_bool["A"])
|
| def test_logical_ops_invalid(self, using_infer_string):
| # GH#5808
|
| df1 = DataFrame(1.0, index=[1], columns=["A"])
| df2 = DataFrame(True, index=[1], columns=["A"])
| msg = re.escape("unsupported operand type(s) for |: 'float' and 'bool'")
| with pytest.raises(TypeError, match=msg):
| df1 | df2
|
| df1 = DataFrame("foo", index=[1], columns=["A"])
| df2 = DataFrame(True, index=[1], columns=["A"])
| if using_infer_string and df1["A"].dtype.storage == "pyarrow":
| msg = "operation 'or_' not supported for dtype 'str'"
| else:
| msg = re.escape("unsupported operand type(s) for |: 'str' and 'bool'")
| with pytest.raises(TypeError, match=msg):
| df1 | df2
|
| def test_logical_operators(self):
| def _check_bin_op(op):
| result = op(df1, df2)
| expected = DataFrame(
| op(df1.values, df2.values), index=df1.index, columns=df1.columns
| )
| assert result.values.dtype == np.bool_
| tm.assert_frame_equal(result, expected)
|
| def _check_unary_op(op):
| result = op(df1)
| expected = DataFrame(op(df1.values), index=df1.index, columns=df1.columns)
| assert result.values.dtype == np.bool_
| tm.assert_frame_equal(result, expected)
|
| df1 = {
| "a": {"a": True, "b": False, "c": False, "d": True, "e": True},
| "b": {"a": False, "b": True, "c": False, "d": False, "e": False},
| "c": {"a": False, "b": False, "c": True, "d": False, "e": False},
| "d": {"a": True, "b": False, "c": False, "d": True, "e": True},
| "e": {"a": True, "b": False, "c": False, "d": True, "e": True},
| }
|
| df2 = {
| "a": {"a": True, "b": False, "c": True, "d": False, "e": False},
| "b": {"a": False, "b": True, "c": False, "d": False, "e": False},
| "c": {"a": True, "b": False, "c": True, "d": False, "e": False},
| "d": {"a": False, "b": False, "c": False, "d": True, "e": False},
| "e": {"a": False, "b": False, "c": False, "d": False, "e": True},
| }
|
| df1 = DataFrame(df1)
| df2 = DataFrame(df2)
|
| _check_bin_op(operator.and_)
| _check_bin_op(operator.or_)
| _check_bin_op(operator.xor)
|
| _check_unary_op(operator.inv) # TODO: belongs elsewhere
|
| @pytest.mark.filterwarnings("ignore:Downcasting object dtype arrays:FutureWarning")
| def test_logical_with_nas(self):
| d = DataFrame({"a": [np.nan, False], "b": [True, True]})
|
| # GH4947
| # bool comparisons should return bool
| result = d["a"] | d["b"]
| expected = Series([False, True])
| tm.assert_series_equal(result, expected)
|
| # GH4604, automatic casting here
| result = d["a"].fillna(False) | d["b"]
| expected = Series([True, True])
| tm.assert_series_equal(result, expected)
|
| msg = "The 'downcast' keyword in fillna is deprecated"
| with tm.assert_produces_warning(FutureWarning, match=msg):
| result = d["a"].fillna(False, downcast=False) | d["b"]
| expected = Series([True, True])
| tm.assert_series_equal(result, expected)
|
| def test_logical_ops_categorical_columns(self):
| # GH#38367
| intervals = [Interval(1, 2), Interval(3, 4)]
| data = DataFrame(
| [[1, np.nan], [2, np.nan]],
| columns=CategoricalIndex(
| intervals, categories=intervals + [Interval(5, 6)]
| ),
| )
| mask = DataFrame(
| [[False, False], [False, False]], columns=data.columns, dtype=bool
| )
| result = mask | isnull(data)
| expected = DataFrame(
| [[False, True], [False, True]],
| columns=CategoricalIndex(
| intervals, categories=intervals + [Interval(5, 6)]
| ),
| )
| tm.assert_frame_equal(result, expected)
|
| def test_int_dtype_different_index_not_bool(self):
| # GH 52500
| df1 = DataFrame([1, 2, 3], index=[10, 11, 23], columns=["a"])
| df2 = DataFrame([10, 20, 30], index=[11, 10, 23], columns=["a"])
| result = np.bitwise_xor(df1, df2)
| expected = DataFrame([21, 8, 29], index=[10, 11, 23], columns=["a"])
| tm.assert_frame_equal(result, expected)
|
| result = df1 ^ df2
| tm.assert_frame_equal(result, expected)
|
| def test_different_dtypes_different_index_raises(self):
| # GH 52538
| df1 = DataFrame([1, 2], index=["a", "b"])
| df2 = DataFrame([3, 4], index=["b", "c"])
| with pytest.raises(TypeError, match="unsupported operand type"):
| df1 & df2
|
|
