numpy.lib.tests.test_nanfunctions

module documentation
(source)

Undocumented

Class SharedNanFunctionsTestsMixin Undocumented
Class TestNanFunctions_ArgminArgmax Undocumented
Class TestNanFunctions_CumSumProd Undocumented
Class TestNanFunctions_MeanVarStd Undocumented
Class TestNanFunctions_Median Undocumented
Class TestNanFunctions_MinMax Undocumented
Class TestNanFunctions_NumberTypes Undocumented
Class TestNanFunctions_Percentile Undocumented
Class TestNanFunctions_Quantile Undocumented
Class TestNanFunctions_SumProd Undocumented
Class TestSignatureMatch No class docstring; 0/2 constant, 1/2 method, 1/1 static method documented
Function test__nan_mask Undocumented
Function test__replace_nan Test that _replace_nan returns the original array if there are no NaNs, not a copy.
Constant _TEST_ARRAYS Undocumented
Constant _TIME_UNITS Undocumented
Constant _TYPE_CODES Undocumented
Variable _ndat Undocumented
Variable _ndat_ones Undocumented
Variable _ndat_zeros Undocumented
Variable _rdat Undocumented
@pytest.mark.parametrize('arr, expected', [(np.array([np.nan, 5.0, np.nan, np.inf]), np.array([False, True, False, True])), (np.array([1, 5, 7, 9], dtype=np.int64), True), (np.array([False, True, False, True]), True), (np.array([[np.nan, 5.0], [np.nan, np.inf]], dtype=np.complex64), np.array([[False, True], [False, True]]))])
def test__nan_mask(arr, expected): (source)

Undocumented

def test__replace_nan(): (source)

Test that _replace_nan returns the original array if there are no NaNs, not a copy.

_TEST_ARRAYS = (source)

Undocumented

Value
{'0d': np.array(5), '1d': np.array([127, 39, 93, 87, 46])}
_TIME_UNITS: tuple[str, ...] = (source)

Undocumented

Value
('Y', 'M', 'W', 'D', 'h', 'm', 's', 'ms', 'us', 'ns', 'ps', 'fs', 'as')
_TYPE_CODES = (source)

Undocumented

Value
list(np.typecodes['AllFloat'])
_ndat = (source)

Undocumented

_ndat_ones = (source)

Undocumented

_ndat_zeros = (source)

Undocumented

_rdat = (source)

Undocumented