Per https://stackoverflow.com/a/48981834/1840471, this is an implementation of the weighted Gini coefficient in Python: import numpy as np def gini(x, weights=None): if weights is None: weights = np.ones_like(x) # Calculate mean absolute deviation in two steps, for weights. count = np.multiply.outer(weights, weights) mad = np.abs(np.subtract.outer(x, x) * count).sum() / count.sum() rmad = mad / np.average(x, weights=weights) # Gini equals half the relative mean absolute deviation. return 0.5 * rmad This is clean and works well for medium-sized arrays, but as warned in its initial suggestion (https://stackoverflow.com/a/39513799/1840471) it's O(n2). On my computer that means it breaks after ~20k rows: n = 20000 # Works, 30000 fails. gini(np.random.rand(n), np.random.rand(n)) Can this be adjusted to work for larger datasets? Mine is ~150k rows. Continue reading...
