Python pandas.core.nanops.nanskew() Examples

The following are 30 code examples of pandas.core.nanops.nanskew(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module pandas.core.nanops , or try the search function .
Example #1
Source File: test_nanops.py    From predictive-maintenance-using-machine-learning with Apache License 2.0 5 votes vote down vote up 
def test_ground_truth(self):
        skew = nanops.nanskew(self.samples)
        tm.assert_almost_equal(skew, self.actual_skew)
Example #2
Source File: test_nanops.py    From twitter-stock-recommendation with MIT License 5 votes vote down vote up 
def test_nans_skipna(self):
        samples = np.hstack([self.samples, np.nan])
        skew = nanops.nanskew(samples, skipna=True)
        tm.assert_almost_equal(skew, self.actual_skew)
Example #3
Source File: test_nanops.py    From twitter-stock-recommendation with MIT License 5 votes vote down vote up 
def test_axis(self):
        samples = np.vstack([self.samples,
                             np.nan * np.ones(len(self.samples))])
        skew = nanops.nanskew(samples, axis=1)
        tm.assert_almost_equal(skew, np.array([self.actual_skew, np.nan]))
Example #4
Source File: test_nanops.py    From twitter-stock-recommendation with MIT License 5 votes vote down vote up 
def test_ground_truth(self):
        skew = nanops.nanskew(self.samples)
        tm.assert_almost_equal(skew, self.actual_skew)
Example #5
Source File: test_nanops.py    From twitter-stock-recommendation with MIT License 5 votes vote down vote up 
def test_all_finite(self):
        alpha, beta = 0.3, 0.1
        left_tailed = self.prng.beta(alpha, beta, size=100)
        assert nanops.nanskew(left_tailed) < 0

        alpha, beta = 0.1, 0.3
        right_tailed = self.prng.beta(alpha, beta, size=100)
        assert nanops.nanskew(right_tailed) > 0
Example #6
Source File: test_nanops.py    From twitter-stock-recommendation with MIT License 5 votes vote down vote up 
def test_constant_series(self):
        # xref GH 11974
        for val in [3075.2, 3075.3, 3075.5]:
            data = val * np.ones(300)
            skew = nanops.nanskew(data)
            assert skew == 0.0
Example #7
Source File: test_nanops.py    From twitter-stock-recommendation with MIT License 5 votes vote down vote up 
def test_nanskew(self):
        from scipy.stats import skew
        func = partial(self._skew_kurt_wrap, func=skew)
        with np.errstate(invalid='ignore'):
            self.check_funs(nanops.nanskew, 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 vote down vote up 
def test_nans_skipna(self):
        samples = np.hstack([self.samples, np.nan])
        skew = nanops.nanskew(samples, skipna=True)
        tm.assert_almost_equal(skew, self.actual_skew)
Example #9
Source File: test_nanops.py    From elasticintel with GNU General Public License v3.0 5 votes vote down vote up 
def test_axis(self):
        samples = np.vstack([self.samples,
                             np.nan * np.ones(len(self.samples))])
        skew = nanops.nanskew(samples, axis=1)
        tm.assert_almost_equal(skew, np.array([self.actual_skew, np.nan]))
Example #10
Source File: test_nanops.py    From elasticintel with GNU General Public License v3.0 5 votes vote down vote up 
def test_ground_truth(self):
        skew = nanops.nanskew(self.samples)
        tm.assert_almost_equal(skew, self.actual_skew)
Example #11
Source File: test_nanops.py    From elasticintel with GNU General Public License v3.0 5 votes vote down vote up 
def test_all_finite(self):
        alpha, beta = 0.3, 0.1
        left_tailed = self.prng.beta(alpha, beta, size=100)
        assert nanops.nanskew(left_tailed) < 0

        alpha, beta = 0.1, 0.3
        right_tailed = self.prng.beta(alpha, beta, size=100)
        assert nanops.nanskew(right_tailed) > 0
Example #12
Source File: test_nanops.py    From elasticintel with GNU General Public License v3.0 5 votes vote down vote up 
def test_constant_series(self):
        # xref GH 11974
        for val in [3075.2, 3075.3, 3075.5]:
            data = val * np.ones(300)
            skew = nanops.nanskew(data)
            assert skew == 0.0
Example #13
Source File: test_nanops.py    From elasticintel with GNU General Public License v3.0 5 votes vote down vote up 
def test_nanskew(self):
        tm.skip_if_no_package('scipy', min_version='0.17.0')
        from scipy.stats import skew
        func = partial(self._skew_kurt_wrap, func=skew)
        with np.errstate(invalid='ignore'):
            self.check_funs(nanops.nanskew, 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 vote down vote up 
def skew(self, axis=None, dtype=None, out=None, keepdims=False,
             skipna=True):
        nv.validate_stat_ddof_func((), dict(dtype=dtype, out=out,
                                            keepdims=keepdims),
                                   fname='skew')
        return nanops.nanskew(self._ndarray, axis=axis, skipna=skipna)

    # ------------------------------------------------------------------------
    # Additional Methods
Example #15
Source File: test_nanops.py    From predictive-maintenance-using-machine-learning with Apache License 2.0 5 votes vote down vote up 
def test_nans_skipna(self):
        samples = np.hstack([self.samples, np.nan])
        skew = nanops.nanskew(samples, skipna=True)
        tm.assert_almost_equal(skew, self.actual_skew)
Example #16
Source File: test_nanops.py    From predictive-maintenance-using-machine-learning with Apache License 2.0 5 votes vote down vote up 
def test_axis(self):
        samples = np.vstack([self.samples,
                             np.nan * np.ones(len(self.samples))])
        skew = nanops.nanskew(samples, axis=1)
        tm.assert_almost_equal(skew, np.array([self.actual_skew, np.nan]))
Example #17
Source File: test_nanops.py    From recruit with Apache License 2.0 5 votes vote down vote up 
def test_nanskew(self):
        from scipy.stats import skew
        func = partial(self._skew_kurt_wrap, func=skew)
        with np.errstate(invalid='ignore'):
            self.check_funs(nanops.nanskew, 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 vote down vote up 
def test_all_finite(self):
        alpha, beta = 0.3, 0.1
        left_tailed = self.prng.beta(alpha, beta, size=100)
        assert nanops.nanskew(left_tailed) < 0

        alpha, beta = 0.1, 0.3
        right_tailed = self.prng.beta(alpha, beta, size=100)
        assert nanops.nanskew(right_tailed) > 0
Example #19
Source File: test_nanops.py    From predictive-maintenance-using-machine-learning with Apache License 2.0 5 votes vote down vote up 
def test_constant_series(self):
        # xref GH 11974
        for val in [3075.2, 3075.3, 3075.5]:
            data = val * np.ones(300)
            skew = nanops.nanskew(data)
            assert skew == 0.0
Example #20
Source File: test_nanops.py    From predictive-maintenance-using-machine-learning with Apache License 2.0 5 votes vote down vote up 
def test_nanskew(self):
        from scipy.stats import skew
        func = partial(self._skew_kurt_wrap, func=skew)
        with np.errstate(invalid='ignore'):
            self.check_funs(nanops.nanskew, 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 vote down vote up 
def test_nans_skipna(self):
        samples = np.hstack([self.samples, np.nan])
        skew = nanops.nanskew(samples, skipna=True)
        tm.assert_almost_equal(skew, self.actual_skew)
Example #22
Source File: test_nanops.py    From vnpy_crypto with MIT License 5 votes vote down vote up 
def test_axis(self):
        samples = np.vstack([self.samples,
                             np.nan * np.ones(len(self.samples))])
        skew = nanops.nanskew(samples, axis=1)
        tm.assert_almost_equal(skew, np.array([self.actual_skew, np.nan]))
Example #23
Source File: test_nanops.py    From vnpy_crypto with MIT License 5 votes vote down vote up 
def test_ground_truth(self):
        skew = nanops.nanskew(self.samples)
        tm.assert_almost_equal(skew, self.actual_skew)
Example #24
Source File: test_nanops.py    From vnpy_crypto with MIT License 5 votes vote down vote up 
def test_all_finite(self):
        alpha, beta = 0.3, 0.1
        left_tailed = self.prng.beta(alpha, beta, size=100)
        assert nanops.nanskew(left_tailed) < 0

        alpha, beta = 0.1, 0.3
        right_tailed = self.prng.beta(alpha, beta, size=100)
        assert nanops.nanskew(right_tailed) > 0
Example #25
Source File: test_nanops.py    From vnpy_crypto with MIT License 5 votes vote down vote up 
def test_constant_series(self):
        # xref GH 11974
        for val in [3075.2, 3075.3, 3075.5]:
            data = val * np.ones(300)
            skew = nanops.nanskew(data)
            assert skew == 0.0
Example #26
Source File: test_nanops.py    From vnpy_crypto with MIT License 5 votes vote down vote up 
def test_nanskew(self):
        from scipy.stats import skew
        func = partial(self._skew_kurt_wrap, func=skew)
        with np.errstate(invalid='ignore'):
            self.check_funs(nanops.nanskew, 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 vote down vote up 
def skew(self, axis=None, dtype=None, out=None, keepdims=False,
             skipna=True):
        nv.validate_stat_ddof_func((), dict(dtype=dtype, out=out,
                                            keepdims=keepdims),
                                   fname='skew')
        return nanops.nanskew(self._ndarray, axis=axis, skipna=skipna)

    # ------------------------------------------------------------------------
    # Additional Methods
Example #28
Source File: test_nanops.py    From recruit with Apache License 2.0 5 votes vote down vote up 
def test_nans_skipna(self):
        samples = np.hstack([self.samples, np.nan])
        skew = nanops.nanskew(samples, skipna=True)
        tm.assert_almost_equal(skew, self.actual_skew)
Example #29
Source File: test_nanops.py    From recruit with Apache License 2.0 5 votes vote down vote up 
def test_axis(self):
        samples = np.vstack([self.samples,
                             np.nan * np.ones(len(self.samples))])
        skew = nanops.nanskew(samples, axis=1)
        tm.assert_almost_equal(skew, np.array([self.actual_skew, np.nan]))
Example #30
Source File: test_nanops.py    From recruit with Apache License 2.0 5 votes vote down vote up 
def test_ground_truth(self):
        skew = nanops.nanskew(self.samples)
        tm.assert_almost_equal(skew, self.actual_skew)