def test_all_finite(self):
alpha, beta = 0.3, 0.1
left_tailed = self.prng.beta(alpha, beta, size=100)
assert nanops.nankurt(left_tailed) < 0
alpha, beta = 0.1, 0.3
right_tailed = self.prng.beta(alpha, beta, size=100)
assert nanops.nankurt(right_tailed) > 0
def test_all_finite(self):
alpha, beta = 0.3, 0.1
left_tailed = self.prng.beta(alpha, beta, size=100)
assert nanops.nankurt(left_tailed) < 0
alpha, beta = 0.1, 0.3
right_tailed = self.prng.beta(alpha, beta, size=100)
assert nanops.nankurt(right_tailed) > 0
def test_all_finite(self):
alpha, beta = 0.3, 0.1
left_tailed = self.prng.beta(alpha, beta, size=100)
self.assertLess(nanops.nankurt(left_tailed), 0)
alpha, beta = 0.1, 0.3
right_tailed = self.prng.beta(alpha, beta, size=100)
self.assertGreater(nanops.nankurt(right_tailed), 0)
def test_all_finite(self):
alpha, beta = 0.3, 0.1
left_tailed = self.prng.beta(alpha, beta, size=100)
self.assertLess(nanops.nankurt(left_tailed), 0)
alpha, beta = 0.1, 0.3
right_tailed = self.prng.beta(alpha, beta, size=100)
self.assertGreater(nanops.nankurt(right_tailed), 0)
def kurt(self,
axis=None,
dtype=None,
out=None,
keepdims=False,
skipna=True):
nv.validate_stat_ddof_func((),
dict(dtype=dtype,
out=out,
keepdims=keepdims),
fname="kurt")
return nanops.nankurt(self._ndarray, axis=axis, skipna=skipna)
def kurt(
self,
*,
axis=None,
dtype: Optional[NpDtype] = None,
out=None,
keepdims=False,
skipna=True,
):
nv.validate_stat_ddof_func(
(), {"dtype": dtype, "out": out, "keepdims": keepdims}, fname="kurt"
)
result = nanops.nankurt(self._ndarray, axis=axis, skipna=skipna)
return self._wrap_reduction_result(axis, result)
def test_axis(self):
samples = np.vstack(
[self.samples, np.nan * np.ones(len(self.samples))])
kurt = nanops.nankurt(samples, axis=1)
tm.assert_almost_equal(kurt, np.array([self.actual_kurt, np.nan]))
def test_ground_truth(self):
kurt = nanops.nankurt(self.samples)
tm.assert_almost_equal(kurt, self.actual_kurt)
def test_constant_series(self):
# xref GH 11974
for val in [3075.2, 3075.3, 3075.5]:
data = val * np.ones(300)
kurt = nanops.nankurt(data)
assert kurt == 0.0
def test_constant_series(self, val):
# xref GH 11974
data = val * np.ones(300)
kurt = nanops.nankurt(data)
assert kurt == 0.0
def test_nans_skipna(self):
samples = np.hstack([self.samples, np.nan])
kurt = nanops.nankurt(samples, skipna=True)
tm.assert_almost_equal(kurt, self.actual_kurt)
def test_nans(self):
samples = np.hstack([self.samples, np.nan])
kurt = nanops.nankurt(samples, skipna=False)
self.assertTrue(np.isnan(kurt))
def test_axis(self):
samples = np.vstack([self.samples,
np.nan * np.ones(len(self.samples))])
kurt = nanops.nankurt(samples, axis=1)
tm.assert_almost_equal(kurt, np.array([self.actual_kurt, np.nan]))
def test_ground_truth(self):
kurt = nanops.nankurt(self.samples)
self.assertAlmostEqual(kurt, self.actual_kurt)
def test_constant_series(self):
# xref GH 11974
for val in [3075.2, 3075.3, 3075.5]:
data = val * np.ones(300)
kurt = nanops.nankurt(data)
self.assertEqual(kurt, 0.0)
def test_nans(self):
samples = np.hstack([self.samples, np.nan])
kurt = nanops.nankurt(samples, skipna=False)
assert np.isnan(kurt)
def test_nans_skipna(self):
samples = np.hstack([self.samples, np.nan])
kurt = nanops.nankurt(samples, skipna=True)
tm.assert_almost_equal(kurt, self.actual_kurt)
def kurt(self, axis=None, dtype=None, out=None, keepdims=False,
skipna=True):
nv.validate_stat_ddof_func((), dict(dtype=dtype, out=out,
keepdims=keepdims),
fname='kurt')
return nanops.nankurt(self._ndarray, axis=axis, skipna=skipna)
