code: def pvals(self, weighted=True, prune=False, hs_dims=None):
          
          stats = self.zscore(weighted=weighted, prune=prune, hs_dims=hs_dims)
          pvals = 2 * (1 - norm.cdf(np.abs(stats)))
  
          return self._apply_pruning_mask(pvals, hs_dims) if prune else pvals
docs: Return 2D ndarray with calculated P values
  
          This function calculates statistically significant cells for
          categorical contingency tables under the null hypothesis that the
          row and column variables are independent (uncorrelated).
          The values are calculated for 2D tables only.
  
          :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: 2 or 3 Dimensional ndarray, representing the p-values for each
                    cell of the table-like representation of the crunch cube.