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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
|
