numpy.lib.tests.test_nanfunctions

module documentation

Undocumented

Class ​Shared​Nan​Functions​Tests​Mixin Undocumented
Class ​Test​Nan​Functions_​Argmin​Argmax Undocumented
Class ​Test​Nan​Functions_​Cum​Sum​Prod Undocumented
Class ​Test​Nan​Functions_​Mean​Var​Std Undocumented
Class ​Test​Nan​Functions_​Median Undocumented
Class ​Test​Nan​Functions_​Min​Max Undocumented
Class ​Test​Nan​Functions_​Number​Types Undocumented
Class ​Test​Nan​Functions_​Percentile Undocumented
Class ​Test​Nan​Functions_​Quantile Undocumented
Class ​Test​Nan​Functions_​Sum​Prod Undocumented
Class ​Test​Signature​Match 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):

Undocumented

def test__replace_nan():
Test that _replace_nan returns the original array if there are no NaNs, not a copy.
_TEST_ARRAYS =

Undocumented

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

Undocumented

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

Undocumented

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

Undocumented

_ndat_ones =

Undocumented

_ndat_zeros =

Undocumented

_rdat =

Undocumented