numpy.lib.tests.test_function_base.TestQuantile

def test_basic(self):

@pytest.mark.xfail(reason='See gh-19154')
def test_correct_quantile_value(self):

def test_fraction(self):

def test_max_ulp(self):

def test_no_p_overwrite(self):

@pytest.mark.parametrize('method', ['inverted_cdf', 'averaged_inverted_cdf', 'closest_observation', 'interpolated_inverted_cdf', 'hazen', 'weibull', 'linear', 'median_unbiased', 'normal_unbiased', 'nearest', 'lower', 'higher', 'midpoint'])
def test_quantile_monotonic(self, method):

@hypothesis.given(arr=arrays(dtype=np.float64, shape=st.integers(min_value=3, max_value=1000), elements=st.floats(allow_infinity=False, allow_nan=False, min_value=-1e+300, max_value=1e+300)))
def test_quantile_monotonic_hypo(self, arr):

@pytest.mark.parametrize('dtype', np.typecodes['AllInteger'])
def test_quantile_preserve_int_type(self, dtype):

def test_quantile_scalar_nan(self):