Python pandas.core.nanops.nankurt() Examples
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code examples of pandas.core.nanops.nankurt().
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Example #1
Source File: test_nanops.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 5 votes |
def test_ground_truth(self): kurt = nanops.nankurt(self.samples) tm.assert_almost_equal(kurt, self.actual_kurt)
Example #2
Source File: test_nanops.py From twitter-stock-recommendation with MIT License | 5 votes |
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)
Example #3
Source File: test_nanops.py From twitter-stock-recommendation with MIT License | 5 votes |
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]))
Example #4
Source File: test_nanops.py From twitter-stock-recommendation with MIT License | 5 votes |
def test_ground_truth(self): kurt = nanops.nankurt(self.samples) tm.assert_almost_equal(kurt, self.actual_kurt)
Example #5
Source File: test_nanops.py From twitter-stock-recommendation with MIT License | 5 votes |
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
Example #6
Source File: test_nanops.py From twitter-stock-recommendation with MIT License | 5 votes |
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
Example #7
Source File: test_nanops.py From twitter-stock-recommendation with MIT License | 5 votes |
def test_nankurt(self): from scipy.stats import kurtosis func1 = partial(kurtosis, fisher=True) func = partial(self._skew_kurt_wrap, func=func1) with np.errstate(invalid='ignore'): self.check_funs(nanops.nankurt, func, allow_complex=False, allow_str=False, allow_date=False, allow_tdelta=False)
Example #8
Source File: test_nanops.py From elasticintel with GNU General Public License v3.0 | 5 votes |
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)
Example #9
Source File: test_nanops.py From elasticintel with GNU General Public License v3.0 | 5 votes |
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]))
Example #10
Source File: test_nanops.py From elasticintel with GNU General Public License v3.0 | 5 votes |
def test_ground_truth(self): kurt = nanops.nankurt(self.samples) tm.assert_almost_equal(kurt, self.actual_kurt)
Example #11
Source File: test_nanops.py From elasticintel with GNU General Public License v3.0 | 5 votes |
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
Example #12
Source File: test_nanops.py From elasticintel with GNU General Public License v3.0 | 5 votes |
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
Example #13
Source File: test_nanops.py From elasticintel with GNU General Public License v3.0 | 5 votes |
def test_nankurt(self): tm.skip_if_no_package('scipy', min_version='0.17.0') from scipy.stats import kurtosis func1 = partial(kurtosis, fisher=True) func = partial(self._skew_kurt_wrap, func=func1) with np.errstate(invalid='ignore'): self.check_funs(nanops.nankurt, func, allow_complex=False, allow_str=False, allow_date=False, allow_tdelta=False)
Example #14
Source File: numpy_.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 5 votes |
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)
Example #15
Source File: test_nanops.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 5 votes |
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)
Example #16
Source File: test_nanops.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 5 votes |
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]))
Example #17
Source File: test_nanops.py From recruit with Apache License 2.0 | 5 votes |
def test_nankurt(self): from scipy.stats import kurtosis func1 = partial(kurtosis, fisher=True) func = partial(self._skew_kurt_wrap, func=func1) with np.errstate(invalid='ignore'): self.check_funs(nanops.nankurt, func, allow_complex=False, allow_str=False, allow_date=False, allow_tdelta=False)
Example #18
Source File: test_nanops.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 5 votes |
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
Example #19
Source File: test_nanops.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 5 votes |
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
Example #20
Source File: test_nanops.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 5 votes |
def test_nankurt(self): from scipy.stats import kurtosis func1 = partial(kurtosis, fisher=True) func = partial(self._skew_kurt_wrap, func=func1) with np.errstate(invalid='ignore'): self.check_funs(nanops.nankurt, func, allow_complex=False, allow_str=False, allow_date=False, allow_tdelta=False)
Example #21
Source File: test_nanops.py From vnpy_crypto with MIT License | 5 votes |
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)
Example #22
Source File: test_nanops.py From vnpy_crypto with MIT License | 5 votes |
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]))
Example #23
Source File: test_nanops.py From vnpy_crypto with MIT License | 5 votes |
def test_ground_truth(self): kurt = nanops.nankurt(self.samples) tm.assert_almost_equal(kurt, self.actual_kurt)
Example #24
Source File: test_nanops.py From vnpy_crypto with MIT License | 5 votes |
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
Example #25
Source File: test_nanops.py From vnpy_crypto with MIT License | 5 votes |
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
Example #26
Source File: test_nanops.py From vnpy_crypto with MIT License | 5 votes |
def test_nankurt(self): from scipy.stats import kurtosis func1 = partial(kurtosis, fisher=True) func = partial(self._skew_kurt_wrap, func=func1) with np.errstate(invalid='ignore'): self.check_funs(nanops.nankurt, func, allow_complex=False, allow_str=False, allow_date=False, allow_tdelta=False)
Example #27
Source File: numpy_.py From recruit with Apache License 2.0 | 5 votes |
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)
Example #28
Source File: test_nanops.py From recruit with Apache License 2.0 | 5 votes |
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)
Example #29
Source File: test_nanops.py From recruit with Apache License 2.0 | 5 votes |
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]))
Example #30
Source File: test_nanops.py From recruit with Apache License 2.0 | 5 votes |
def test_ground_truth(self): kurt = nanops.nankurt(self.samples) tm.assert_almost_equal(kurt, self.actual_kurt)