(self)
return vals
The sample skewness is computed as the Fisher-Pearson coefficient
        of skewness, i.e.
        .. math::
            g_1=\frac{m_3}{m_2^{3/2}}

        where
        .. math::
            m_i=\frac{1}{N}\sum_{n=1}^N(x[n]-\bar{x})^i

        If the bias is False, return the adjusted Fisher-Pearson standardized moment coefficient
        i.e.

        .. math::

        G_1=\frac{k_3}{k_2^{3/2}}=
            \frac{\sqrt{N(N-1)}}{N-2}\frac{m_3}{m_2^{3/2}}.
The sample skewness is computed as the Fisher-Pearson coefficient
        of skewness, i.e.
        .. math::
            g_1=\frac{m_3}{m_2^{3/2}}
The sample skewness is computed as the Fisher - Pearson coefficient of skewness i . e . .. math :: g_1 = \ frac { m_3 } { m_2^ { 3 / 2 }}
def skewness(self):
        """
            The sample skewness is computed as the Fisher-Pearson coefficient
        of skewness, i.e.
        .. math::
            g_1=\frac{m_3}{m_2^{3/2}}

        where
        .. math::
            m_i=\frac{1}{N}\sum_{n=1}^N(x[n]-\bar{x})^i

        If the bias is False, return the adjusted Fisher-Pearson standardized moment coefficient
        i.e.

        .. math::

        G_1=\frac{k_3}{k_2^{3/2}}=
            \frac{\sqrt{N(N-1)}}{N-2}\frac{m_3}{m_2^{3/2}}.

        """
        m2 = self.variance
        m3 = self.moment_3
        n = self.count

        zero = (m2 == 0)
        np.seterr(divide='ignore', invalid='ignore')
        vals = np.where(zero, 0, m3 / m2 ** 1.5)

        if not self.bias:
            can_correct = (n > 2) & (m2 > 0)
            if can_correct.any():
                m2 = np.extract(can_correct, m2)
                m3 = np.extract(can_correct, m3)
                nval = np.sqrt((n - 1.0) * n) / (n - 2.0) * m3 / m2 ** 1.5
                np.place(vals, can_correct, nval)
        return vals
def skewness ( self ) : m2 = self . variance m3 = self . moment_3 n = self . count zero = ( m2 == 0 ) np . seterr ( divide = 'ignore' , invalid = 'ignore' ) vals = np . where ( zero , 0 , m3 / m2 ** 1.5 ) if not self . bias : can_correct = ( n > 2 ) & ( m2 > 0 ) if can_correct . any ( ) : m2 = np . extract ( can_correct , m2 ) m3 = np . extract ( can_correct , m3 ) nval = np . sqrt ( ( n - 1.0 ) * n ) / ( n - 2.0 ) * m3 / m2 ** 1.5 np . place ( vals , can_correct , nval ) return vals