docs: def kendall(x, axis=0):
      """Kendall' tau (Rank) Correlation Matrix (for ordinal data)
  
      Parameters
      ----------
      x : ndarray
          data set
  
      axis : int, optional
          Variables as columns is the default (axis=0). If variables are
          in the rows use axis=1
  
      Returns
      -------
      r : ndarray
          Correlation Matrix (Kendall tau)
  
      p : ndarray
          p-values
      """
      # transpose if axis<>0
      if axis is not 0:
          x = x.T
  
      # read dimensions and
      n, c = x.shape
  
      # check if enough variables provided
      if c < 2:
          raise Exception(
              "Only " + str(c) + " variables provided. Min. 2 required.")
  
      # allocate variables
      r = np.ones((c, c))
      p = np.zeros((c, c))
  
      # compute each (i,j)-th correlation
      for i in range(0, c):
          for j in range(i + 1, c):
              r[i, j], p[i, j] = scipy.stats.kendalltau(x[:, i], x[:, j])
              r[j, i] = r[i, j]
              p[j, i] = p[i, j]
  
      # done
      return r, p