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219 lines
7.2 KiB
219 lines
7.2 KiB
6 months ago
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import operator
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import re
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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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CategoricalIndex,
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DataFrame,
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Interval,
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Series,
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isnull,
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)
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import pandas._testing as tm
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class TestDataFrameLogicalOperators:
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# &, |, ^
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@pytest.mark.parametrize(
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"left, right, op, expected",
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[
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(
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[True, False, np.nan],
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[True, False, True],
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operator.and_,
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[True, False, False],
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),
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(
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[True, False, True],
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[True, False, np.nan],
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operator.and_,
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[True, False, False],
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),
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(
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[True, False, np.nan],
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[True, False, True],
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operator.or_,
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[True, False, False],
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),
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(
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[True, False, True],
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[True, False, np.nan],
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operator.or_,
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[True, False, True],
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),
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],
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)
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def test_logical_operators_nans(self, left, right, op, expected, frame_or_series):
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# GH#13896
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result = op(frame_or_series(left), frame_or_series(right))
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expected = frame_or_series(expected)
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tm.assert_equal(result, expected)
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def test_logical_ops_empty_frame(self):
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# GH#5808
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# empty frames, non-mixed dtype
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df = DataFrame(index=[1])
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result = df & df
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tm.assert_frame_equal(result, df)
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result = df | df
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tm.assert_frame_equal(result, df)
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df2 = DataFrame(index=[1, 2])
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result = df & df2
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tm.assert_frame_equal(result, df2)
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dfa = DataFrame(index=[1], columns=["A"])
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result = dfa & dfa
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expected = DataFrame(False, index=[1], columns=["A"])
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tm.assert_frame_equal(result, expected)
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def test_logical_ops_bool_frame(self):
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# GH#5808
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df1a_bool = DataFrame(True, index=[1], columns=["A"])
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result = df1a_bool & df1a_bool
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tm.assert_frame_equal(result, df1a_bool)
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result = df1a_bool | df1a_bool
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tm.assert_frame_equal(result, df1a_bool)
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def test_logical_ops_int_frame(self):
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# GH#5808
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df1a_int = DataFrame(1, index=[1], columns=["A"])
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df1a_bool = DataFrame(True, index=[1], columns=["A"])
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result = df1a_int | df1a_bool
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tm.assert_frame_equal(result, df1a_bool)
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# Check that this matches Series behavior
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res_ser = df1a_int["A"] | df1a_bool["A"]
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tm.assert_series_equal(res_ser, df1a_bool["A"])
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def test_logical_ops_invalid(self, using_infer_string):
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# GH#5808
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df1 = DataFrame(1.0, index=[1], columns=["A"])
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df2 = DataFrame(True, index=[1], columns=["A"])
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msg = re.escape("unsupported operand type(s) for |: 'float' and 'bool'")
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with pytest.raises(TypeError, match=msg):
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df1 | df2
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df1 = DataFrame("foo", index=[1], columns=["A"])
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df2 = DataFrame(True, index=[1], columns=["A"])
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msg = re.escape("unsupported operand type(s) for |: 'str' and 'bool'")
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if using_infer_string:
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import pyarrow as pa
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with pytest.raises(pa.lib.ArrowNotImplementedError, match="|has no kernel"):
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df1 | df2
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else:
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with pytest.raises(TypeError, match=msg):
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df1 | df2
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def test_logical_operators(self):
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def _check_bin_op(op):
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result = op(df1, df2)
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expected = DataFrame(
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op(df1.values, df2.values), index=df1.index, columns=df1.columns
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)
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assert result.values.dtype == np.bool_
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tm.assert_frame_equal(result, expected)
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def _check_unary_op(op):
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result = op(df1)
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expected = DataFrame(op(df1.values), index=df1.index, columns=df1.columns)
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assert result.values.dtype == np.bool_
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tm.assert_frame_equal(result, expected)
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df1 = {
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"a": {"a": True, "b": False, "c": False, "d": True, "e": True},
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"b": {"a": False, "b": True, "c": False, "d": False, "e": False},
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"c": {"a": False, "b": False, "c": True, "d": False, "e": False},
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"d": {"a": True, "b": False, "c": False, "d": True, "e": True},
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"e": {"a": True, "b": False, "c": False, "d": True, "e": True},
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}
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df2 = {
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"a": {"a": True, "b": False, "c": True, "d": False, "e": False},
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"b": {"a": False, "b": True, "c": False, "d": False, "e": False},
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"c": {"a": True, "b": False, "c": True, "d": False, "e": False},
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"d": {"a": False, "b": False, "c": False, "d": True, "e": False},
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"e": {"a": False, "b": False, "c": False, "d": False, "e": True},
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}
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df1 = DataFrame(df1)
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df2 = DataFrame(df2)
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_check_bin_op(operator.and_)
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_check_bin_op(operator.or_)
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_check_bin_op(operator.xor)
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_check_unary_op(operator.inv) # TODO: belongs elsewhere
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@pytest.mark.filterwarnings("ignore:Downcasting object dtype arrays:FutureWarning")
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def test_logical_with_nas(self):
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d = DataFrame({"a": [np.nan, False], "b": [True, True]})
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# GH4947
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# bool comparisons should return bool
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result = d["a"] | d["b"]
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expected = Series([False, True])
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tm.assert_series_equal(result, expected)
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# GH4604, automatic casting here
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result = d["a"].fillna(False) | d["b"]
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expected = Series([True, True])
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tm.assert_series_equal(result, expected)
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msg = "The 'downcast' keyword in fillna is deprecated"
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with tm.assert_produces_warning(FutureWarning, match=msg):
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result = d["a"].fillna(False, downcast=False) | d["b"]
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expected = Series([True, True])
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tm.assert_series_equal(result, expected)
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def test_logical_ops_categorical_columns(self):
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# GH#38367
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intervals = [Interval(1, 2), Interval(3, 4)]
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data = DataFrame(
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[[1, np.nan], [2, np.nan]],
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columns=CategoricalIndex(
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intervals, categories=intervals + [Interval(5, 6)]
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),
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)
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mask = DataFrame(
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[[False, False], [False, False]], columns=data.columns, dtype=bool
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)
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result = mask | isnull(data)
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expected = DataFrame(
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[[False, True], [False, True]],
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columns=CategoricalIndex(
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intervals, categories=intervals + [Interval(5, 6)]
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),
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)
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tm.assert_frame_equal(result, expected)
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def test_int_dtype_different_index_not_bool(self):
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# GH 52500
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df1 = DataFrame([1, 2, 3], index=[10, 11, 23], columns=["a"])
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df2 = DataFrame([10, 20, 30], index=[11, 10, 23], columns=["a"])
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result = np.bitwise_xor(df1, df2)
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expected = DataFrame([21, 8, 29], index=[10, 11, 23], columns=["a"])
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tm.assert_frame_equal(result, expected)
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result = df1 ^ df2
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tm.assert_frame_equal(result, expected)
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def test_different_dtypes_different_index_raises(self):
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# GH 52538
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df1 = DataFrame([1, 2], index=["a", "b"])
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df2 = DataFrame([3, 4], index=["b", "c"])
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with pytest.raises(TypeError, match="unsupported operand type"):
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df1 & df2
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