from datetime import ( date, time, timedelta, ) import pickle import numpy as np import pytest from pandas._libs.missing import NA from pandas.core.dtypes.common import is_scalar import pandas as pd import pandas._testing as tm def test_singleton(): assert NA is NA new_NA = type(NA)() assert new_NA is NA def test_repr(): assert repr(NA) == "" assert str(NA) == "" def test_format(): # GH-34740 assert format(NA) == "" assert format(NA, ">10") == " " assert format(NA, "xxx") == "" # NA is flexible, accept any format spec assert f"{NA}" == "" assert f"{NA:>10}" == " " assert f"{NA:xxx}" == "" def test_truthiness(): msg = "boolean value of NA is ambiguous" with pytest.raises(TypeError, match=msg): bool(NA) with pytest.raises(TypeError, match=msg): not NA def test_hashable(): assert hash(NA) == hash(NA) d = {NA: "test"} assert d[NA] == "test" @pytest.mark.parametrize( "other", [NA, 1, 1.0, "a", b"a", np.int64(1), np.nan], ids=repr ) def test_arithmetic_ops(all_arithmetic_functions, other): op = all_arithmetic_functions if op.__name__ in ("pow", "rpow", "rmod") and isinstance(other, (str, bytes)): pytest.skip(reason=f"{op.__name__} with NA and {other} not defined.") if op.__name__ in ("divmod", "rdivmod"): assert op(NA, other) is (NA, NA) else: if op.__name__ == "rpow": # avoid special case other += 1 assert op(NA, other) is NA @pytest.mark.parametrize( "other", [ NA, 1, 1.0, "a", b"a", np.int64(1), np.nan, np.bool_(True), time(0), date(1, 2, 3), timedelta(1), pd.NaT, ], ) def test_comparison_ops(comparison_op, other): assert comparison_op(NA, other) is NA assert comparison_op(other, NA) is NA @pytest.mark.parametrize( "value", [ 0, 0.0, -0, -0.0, False, np.bool_(False), np.int_(0), np.float_(0), np.int_(-0), np.float_(-0), ], ) @pytest.mark.parametrize("asarray", [True, False]) def test_pow_special(value, asarray): if asarray: value = np.array([value]) result = NA**value if asarray: result = result[0] else: # this assertion isn't possible for ndarray. assert isinstance(result, type(value)) assert result == 1 @pytest.mark.parametrize( "value", [1, 1.0, True, np.bool_(True), np.int_(1), np.float_(1)] ) @pytest.mark.parametrize("asarray", [True, False]) def test_rpow_special(value, asarray): if asarray: value = np.array([value]) result = value**NA if asarray: result = result[0] elif not isinstance(value, (np.float_, np.bool_, np.int_)): # this assertion isn't possible with asarray=True assert isinstance(result, type(value)) assert result == value @pytest.mark.parametrize("value", [-1, -1.0, np.int_(-1), np.float_(-1)]) @pytest.mark.parametrize("asarray", [True, False]) def test_rpow_minus_one(value, asarray): if asarray: value = np.array([value]) result = value**NA if asarray: result = result[0] assert pd.isna(result) def test_unary_ops(): assert +NA is NA assert -NA is NA assert abs(NA) is NA assert ~NA is NA def test_logical_and(): assert NA & True is NA assert True & NA is NA assert NA & False is False assert False & NA is False assert NA & NA is NA msg = "unsupported operand type" with pytest.raises(TypeError, match=msg): NA & 5 def test_logical_or(): assert NA | True is True assert True | NA is True assert NA | False is NA assert False | NA is NA assert NA | NA is NA msg = "unsupported operand type" with pytest.raises(TypeError, match=msg): NA | 5 def test_logical_xor(): assert NA ^ True is NA assert True ^ NA is NA assert NA ^ False is NA assert False ^ NA is NA assert NA ^ NA is NA msg = "unsupported operand type" with pytest.raises(TypeError, match=msg): NA ^ 5 def test_logical_not(): assert ~NA is NA @pytest.mark.parametrize("shape", [(3,), (3, 3), (1, 2, 3)]) def test_arithmetic_ndarray(shape, all_arithmetic_functions): op = all_arithmetic_functions a = np.zeros(shape) if op.__name__ == "pow": a += 5 result = op(NA, a) expected = np.full(a.shape, NA, dtype=object) tm.assert_numpy_array_equal(result, expected) def test_is_scalar(): assert is_scalar(NA) is True def test_isna(): assert pd.isna(NA) is True assert pd.notna(NA) is False def test_series_isna(): s = pd.Series([1, NA], dtype=object) expected = pd.Series([False, True]) tm.assert_series_equal(s.isna(), expected) def test_ufunc(): assert np.log(NA) is NA assert np.add(NA, 1) is NA result = np.divmod(NA, 1) assert result[0] is NA and result[1] is NA result = np.frexp(NA) assert result[0] is NA and result[1] is NA def test_ufunc_raises(): msg = "ufunc method 'at'" with pytest.raises(ValueError, match=msg): np.log.at(NA, 0) def test_binary_input_not_dunder(): a = np.array([1, 2, 3]) expected = np.array([NA, NA, NA], dtype=object) result = np.logaddexp(a, NA) tm.assert_numpy_array_equal(result, expected) result = np.logaddexp(NA, a) tm.assert_numpy_array_equal(result, expected) # all NA, multiple inputs assert np.logaddexp(NA, NA) is NA result = np.modf(NA, NA) assert len(result) == 2 assert all(x is NA for x in result) def test_divmod_ufunc(): # binary in, binary out. a = np.array([1, 2, 3]) expected = np.array([NA, NA, NA], dtype=object) result = np.divmod(a, NA) assert isinstance(result, tuple) for arr in result: tm.assert_numpy_array_equal(arr, expected) tm.assert_numpy_array_equal(arr, expected) result = np.divmod(NA, a) for arr in result: tm.assert_numpy_array_equal(arr, expected) tm.assert_numpy_array_equal(arr, expected) def test_integer_hash_collision_dict(): # GH 30013 result = {NA: "foo", hash(NA): "bar"} assert result[NA] == "foo" assert result[hash(NA)] == "bar" def test_integer_hash_collision_set(): # GH 30013 result = {NA, hash(NA)} assert len(result) == 2 assert NA in result assert hash(NA) in result def test_pickle_roundtrip(): # https://github.com/pandas-dev/pandas/issues/31847 result = pickle.loads(pickle.dumps(NA)) assert result is NA def test_pickle_roundtrip_pandas(): result = tm.round_trip_pickle(NA) assert result is NA @pytest.mark.parametrize( "values, dtype", [([1, 2, NA], "Int64"), (["A", "B", NA], "string")] ) @pytest.mark.parametrize("as_frame", [True, False]) def test_pickle_roundtrip_containers(as_frame, values, dtype): s = pd.Series(pd.array(values, dtype=dtype)) if as_frame: s = s.to_frame(name="A") result = tm.round_trip_pickle(s) tm.assert_equal(result, s)