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One way to calculate the Gini coefficient of a sample is using the relative mean difference (RMD) which is 2 times the Gini coefficient. RMD depends on the mean difference which is given by: So I need to calculate each difference between pair of elements in a sample (yi - yj) . It took me quite a bit to figure out a way to do it but I want to know if there is a function that does this for you. At first I tried this but I bet it's very slow in big data sets (by the way, s is the sample): In [124]: %%timeit from itertools import permutations k = 0 for i, j in list(permutations(s,2)): k += abs(i-j) MD = k/float(len(s)**2) G = MD / float(mean(s)) G = G/2 G 10000 loops, best of 3: 78 us per loop Then I tried the following which is less understandable but quicker: In [126]: %%timeit m = abs(s - s.reshape(len(s), 1)) MD = np.sum(m)/float((len(s)**2)) G = MD / float(mean(s)) G = G/2 G 10000 loops, best of 3: 46.8 us per loop Is there something efficient but easy to generalize? For example, what if I want to sum over three indices? This is the sample I was using: sample = array([5487574374, 686306, 5092789, 17264231, 41733014, 60870152, 82204091, 227787612, 264942911, 716909668, 679759369, 1336605253, 788028471, 331434695, 146295398, 88673463, 224589748, 128576176, 346121028]) gini(sample) Out[155]: 0.2692307692307692 Thanks!

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