code: def _get_signif_5(arr, nboots):
      
  
      statsarr = np.zeros((3, 7), dtype=np.float64)
      bootsarr = np.zeros((3, nboots))
  
      idx = 0
      for acol in [2, 3, 4]:
          rows = np.array([0, 1, acol, 5])
          tarr = arr[:, rows, :]
  
          abxa, baxa, dst = _prop_dstat(tarr)
          boots = _get_boots(tarr, nboots)
          estimate, stddev = (boots.mean(), boots.std())
          if stddev:
              zscore = np.abs(dst) / stddev
          else:
              zscore = np.NaN
          stats = [dst, estimate, stddev, zscore, abxa, baxa, arr.shape[0]]
  
          statsarr[idx] = stats
          bootsarr[idx] = boots
          idx += 1
  
      return statsarr, bootsarr
docs: returns a list of stats and an array of dstat boots. Stats includes
      z-score and two-sided P-value.