parameters: (a, axis=0, bias=True)
return_statement: return vals
docstring: Computes the skewness of a data set.
  
      Parameters
      ----------
      a : ndarray
          data
      axis : int or None, optional
          Axis along which skewness is calculated. Default is 0.
          If None, compute over the whole array `a`.
      bias : bool, optional
          If False, then the calculations are corrected for statistical bias.
  
      Returns
      -------
      skewness : ndarray
          The skewness of values along an axis, returning 0 where all values are
          equal.
  
      Notes
      -----
      For more details about `skew`, see `stats.skew`.
docstring_summary: Computes the skewness of a data set.
docstring_tokens: Computes the skewness of a data set .
function: def skew(a, axis=0, bias=True):
      """
      Computes the skewness of a data set.
  
      Parameters
      ----------
      a : ndarray
          data
      axis : int or None, optional
          Axis along which skewness is calculated. Default is 0.
          If None, compute over the whole array `a`.
      bias : bool, optional
          If False, then the calculations are corrected for statistical bias.
  
      Returns
      -------
      skewness : ndarray
          The skewness of values along an axis, returning 0 where all values are
          equal.
  
      Notes
      -----
      For more details about `skew`, see `stats.skew`.
  
      """
      a, axis = _chk_asarray(a,axis)
      n = a.count(axis)
      m2 = moment(a, 2, axis)
      m3 = moment(a, 3, axis)
      olderr = np.seterr(all='ignore')
      try:
          vals = ma.where(m2 == 0, 0, m3 / m2**1.5)
      finally:
          np.seterr(**olderr)
  
      if not bias:
          can_correct = (n > 2) & (m2 > 0)
          if can_correct.any():
              m2 = np.extract(can_correct, m2)
              m3 = np.extract(can_correct, m3)
              nval = ma.sqrt((n-1.0)*n)/(n-2.0)*m3/m2**1.5
              np.place(vals, can_correct, nval)
      return vals
function_tokens: def skew ( a , axis = 0 , bias = True ) : a , axis = _chk_asarray ( a , axis ) n = a . count ( axis ) m2 = moment ( a , 2 , axis ) m3 = moment ( a , 3 , axis ) olderr = np . seterr ( all = 'ignore' ) try : vals = ma . where ( m2 == 0 , 0 , m3 / m2 ** 1.5 ) finally : np . seterr ( * * olderr ) if not bias : can_correct = ( n > 2 ) & ( m2 > 0 ) if can_correct . any ( ) : m2 = np . extract ( can_correct , m2 ) m3 = np . extract ( can_correct , m3 ) nval = ma . sqrt ( ( n - 1.0 ) * n ) / ( n - 2.0 ) * m3 / m2 ** 1.5 np . place ( vals , can_correct , nval ) return vals