input: def gini(x):
      """Calculate the Gini Coefficient of a given dataset.
      >>> gini([2,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,21])
      0.605
      """
      import numpy as np
      # calculate the frequency of each item in x
      x_value_counts = np.bincount(x)
  
      # calculate the cumulative sum of x_value_counts
      x_cum_sum = np.cumsum(x_value_counts)
  
      # calculate the Lorenz values
      n = len(x)
      lorenz = x_cum_sum / n
      lorenz = np.insert(lorenz, 0, 0) # add 0 to the beginning of array
  
      # calculate the Gini Coefficient
      width = lorenz[:-1] - lorenz[1:]
      gini_coef = (1 / n) * sum(width * (lorenz[1:] + lorenz[:-1]))
  
      return gini_coef
output:     import numpy as np
      # calculate the frequency of each item in x
      x_value_counts = np.bincount(x)
  
      # calculate the cumulative sum of x_value_counts
      x_cum_sum = np.cumsum(x_value_counts)
  
      # calculate the Lorenz values
      n = len(x)
      lorenz = x_cum_sum / n
      lorenz = np.insert(lorenz, 0, 0) # add 0 to the beginning of array
  
      # calculate the Gini Coefficient
      width = lorenz[:-1] - lorenz[1:]
      gini_coef = (1 / n) * sum(width * (lorenz[1:] + lorenz[:-1]))
  
      return gini_coef