code: 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
  
imports: from __future__ import division, print_function, absolute_import
  import numpy
  from numpy import ndarray
  import numpy.ma
  from numpy.ma import masked, nomask
  from scipy._lib.six import iteritems
  import itertools
  import warnings
  from collections import namedtuple
  from .None import distributions
  import scipy.special
  from ._stats_mstats_common import _find_repeats, linregress, theilslopes, siegelslopes
label_desc: Use image node_id 44 for calling a global function with example usage: skew(a, axis, bias) and returns: vals