from decimal import Decimal import numpy as np import pytest from pandas.compat.numpy import np_version_gte1p25 import pandas as pd import pandas._testing as tm class TestDataFrameUnaryOperators: # __pos__, __neg__, __invert__ @pytest.mark.parametrize( "df,expected", [ (pd.DataFrame({"a": [-1, 1]}), pd.DataFrame({"a": [1, -1]})), (pd.DataFrame({"a": [False, True]}), pd.DataFrame({"a": [True, False]})), ( pd.DataFrame({"a": pd.Series(pd.to_timedelta([-1, 1]))}), pd.DataFrame({"a": pd.Series(pd.to_timedelta([1, -1]))}), ), ], ) def test_neg_numeric(self, df, expected): tm.assert_frame_equal(-df, expected) tm.assert_series_equal(-df["a"], expected["a"]) @pytest.mark.parametrize( "df, expected", [ (np.array([1, 2], dtype=object), np.array([-1, -2], dtype=object)), ([Decimal("1.0"), Decimal("2.0")], [Decimal("-1.0"), Decimal("-2.0")]), ], ) def test_neg_object(self, df, expected): # GH#21380 df = pd.DataFrame({"a": df}) expected = pd.DataFrame({"a": expected}) tm.assert_frame_equal(-df, expected) tm.assert_series_equal(-df["a"], expected["a"]) @pytest.mark.parametrize( "df", [ pd.DataFrame({"a": ["a", "b"]}), pd.DataFrame({"a": pd.to_datetime(["2017-01-22", "1970-01-01"])}), ], ) def test_neg_raises(self, df): msg = ( "bad operand type for unary -: 'str'|" r"bad operand type for unary -: 'DatetimeArray'" ) with pytest.raises(TypeError, match=msg): (-df) with pytest.raises(TypeError, match=msg): (-df["a"]) def test_invert(self, float_frame): df = float_frame tm.assert_frame_equal(-(df < 0), ~(df < 0)) def test_invert_mixed(self): shape = (10, 5) df = pd.concat( [ pd.DataFrame(np.zeros(shape, dtype="bool")), pd.DataFrame(np.zeros(shape, dtype=int)), ], axis=1, ignore_index=True, ) result = ~df expected = pd.concat( [ pd.DataFrame(np.ones(shape, dtype="bool")), pd.DataFrame(-np.ones(shape, dtype=int)), ], axis=1, ignore_index=True, ) tm.assert_frame_equal(result, expected) def test_invert_empty_not_input(self): # GH#51032 df = pd.DataFrame() result = ~df tm.assert_frame_equal(df, result) assert df is not result @pytest.mark.parametrize( "df", [ pd.DataFrame({"a": [-1, 1]}), pd.DataFrame({"a": [False, True]}), pd.DataFrame({"a": pd.Series(pd.to_timedelta([-1, 1]))}), ], ) def test_pos_numeric(self, df): # GH#16073 tm.assert_frame_equal(+df, df) tm.assert_series_equal(+df["a"], df["a"]) @pytest.mark.parametrize( "df", [ pd.DataFrame({"a": np.array([-1, 2], dtype=object)}), pd.DataFrame({"a": [Decimal("-1.0"), Decimal("2.0")]}), ], ) def test_pos_object(self, df): # GH#21380 tm.assert_frame_equal(+df, df) tm.assert_series_equal(+df["a"], df["a"]) @pytest.mark.parametrize( "df", [ pytest.param( pd.DataFrame({"a": ["a", "b"]}), # filterwarnings removable once min numpy version is 1.25 marks=[ pytest.mark.filterwarnings("ignore:Applying:DeprecationWarning") ], ), ], ) def test_pos_object_raises(self, df): # GH#21380 if np_version_gte1p25: with pytest.raises( TypeError, match=r"^bad operand type for unary \+: \'str\'$" ): tm.assert_frame_equal(+df, df) else: tm.assert_series_equal(+df["a"], df["a"]) @pytest.mark.parametrize( "df", [pd.DataFrame({"a": pd.to_datetime(["2017-01-22", "1970-01-01"])})] ) def test_pos_raises(self, df): msg = r"bad operand type for unary \+: 'DatetimeArray'" with pytest.raises(TypeError, match=msg): (+df) with pytest.raises(TypeError, match=msg): (+df["a"]) def test_unary_nullable(self): df = pd.DataFrame( { "a": pd.array([1, -2, 3, pd.NA], dtype="Int64"), "b": pd.array([4.0, -5.0, 6.0, pd.NA], dtype="Float32"), "c": pd.array([True, False, False, pd.NA], dtype="boolean"), # include numpy bool to make sure bool-vs-boolean behavior # is consistent in non-NA locations "d": np.array([True, False, False, True]), } ) result = +df res_ufunc = np.positive(df) expected = df # TODO: assert that we have copies? tm.assert_frame_equal(result, expected) tm.assert_frame_equal(res_ufunc, expected) result = -df res_ufunc = np.negative(df) expected = pd.DataFrame( { "a": pd.array([-1, 2, -3, pd.NA], dtype="Int64"), "b": pd.array([-4.0, 5.0, -6.0, pd.NA], dtype="Float32"), "c": pd.array([False, True, True, pd.NA], dtype="boolean"), "d": np.array([False, True, True, False]), } ) tm.assert_frame_equal(result, expected) tm.assert_frame_equal(res_ufunc, expected) result = abs(df) res_ufunc = np.abs(df) expected = pd.DataFrame( { "a": pd.array([1, 2, 3, pd.NA], dtype="Int64"), "b": pd.array([4.0, 5.0, 6.0, pd.NA], dtype="Float32"), "c": pd.array([True, False, False, pd.NA], dtype="boolean"), "d": np.array([True, False, False, True]), } ) tm.assert_frame_equal(result, expected) tm.assert_frame_equal(res_ufunc, expected)