docstr: Direct quote from Gries (2019)
  A maybe more useful variant of this measure is its normalized version, the variation
  coefficient (vc, see (9)); the normalization consists of dividing sdpopulation by the mean frequency
  of the element in the corpus parts f/n:
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code: def vc(self):
      """
      Direct quote from Gries (2019)
      A maybe more useful variant of this measure is its normalized version, the variation
      coefficient (vc, see (9)); the normalization consists of dividing sdpopulation by the mean frequency
      of the element in the corpus parts f/n:
      """
      ss = StandardScaler(with_mean=False).fit(self.v)
      return np.sqrt(ss.var_) / ss.mean_
  
imports: from typing import Optional, Callable, List
  import numpy
  import pandas
  from scattertext.termranking import AbsoluteFrequencyRanker
  from scipy.sparse import csc_matrix, csr_matrix
  from sklearn.preprocessing import StandardScaler
  from scattertext.smoothing.lowess import Lowess
  from scattertext.termranking.TermRanker import TermRanker
  from scattertext.TermDocMatrix import TermDocMatrix
label_desc: Use image node_id 5 for calling the Dispersion obj's underlying member method code with example usage: obj.vc() and returns: unknown