An efficient way to calculate weighted Gini coefficient in Python

gini.py

import numba
import numpy as np

@numba.jit
def gini(x, w=None):
# from https://stackoverflow.com/questions/48999542/more-efficient-weighted-gini-coefficient-in-python
    # The rest of the code requires numpy arrays.
#     x = np.asarray(x)
    if w is not None:
#         w = np.asarray(w)
        sorted_indices = np.argsort(x)
        sorted_x = x[sorted_indices]
        sorted_w = w[sorted_indices]
        # Force float dtype to avoid overflows
        cumw = np.cumsum(sorted_w)
        cumxw = np.cumsum(sorted_x * sorted_w)
        return (np.sum(cumxw[1:] * cumw[:-1] - cumxw[:-1] * cumw[1:]) / 
                (cumxw[-1] * cumw[-1]))
    else:
#         x = np.asarray(x)
        sorted_x = np.sort(x)
        n = x.size
        cumx = np.cumsum(sorted_x)
        # The above formula, with all weights equal to 1 simplifies to:
        return (n + 1 - 2 * np.sum(cumx) / cumx[-1]) / n
    
gini(np.asarray([1,2,3,4,5]))
# 0.2666666666666666