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 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 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)