code: def confidential_interval(x, alpha=0.98):
      
      from scipy.stats import t
      if x.ndim == 1:
          df = len(x) - 1
          # calculate positive critical value of student's T distribution
          cv = t.interval(alpha, df)
          # calculate sample standard distribution
          std = np.std(x)
      else:
          # calculate degree of freedom
          df = len(x[0]) - 1
          # calculate positive critical value of student's T distribution
          cv = t.interval(alpha, df)[1]
          # calculate sample standard distribution
          std = np.std(x, axis=1)
      # calculate positive difference from
      # sample average to confidential interval
      return std * cv / np.sqrt(df)
docs: Return a numpy array of column confidential interval
  
      Parameters
      ----------
      x : ndarray
          A numpy array instance
      alpha : float
          Alpha value of confidential interval
  
      Returns
      -------
      ndarray
          A 1 x n numpy array which indicate the each difference from sample
          average point to confidential interval point