code: def zscore(self, weighted=True, prune=False, hs_dims=None):
          
          res = [s.zscore(weighted, prune, hs_dims) for s in self.slices]
          return np.array(res) if self.ndim == 3 else res[0]
docs: Return ndarray with cube's zscore measurements.
  
          Zscore is a measure of statistical significance of observed vs.
          expected counts. It's only applicable to a 2D contingency tables.
          For 3D cubes, the measures of separate slices are stacked together
          and returned as the result.
  
          :param weighted: Use weighted counts for zscores
          :param prune: Prune based on unweighted counts
          :param hs_dims: Include headers and subtotals (as NaN values)
          :returns zscore: ndarray representing zscore measurements