More efficient weighted Gini coefficient in Python

Question

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, weight         


        

Answer 1

Adapting the StatsGini R function from here:

import numpy as np
import pandas as pd

def gini(x, w=None):
    # Array indexing requires reset indexes.
    x = pd.Series(x).reset_index(drop=True)
    if w is None:
        w = np.ones_like(x)
    w = pd.Series(w).reset_index(drop=True)
    n = x.size
    wxsum = sum(w * x)
    wsum = sum(w)
    sxw = np.argsort(x)
    sx = x[sxw] * w[sxw]
    sw = w[sxw]
    pxi = np.cumsum(sx) / wxsum
    pci = np.cumsum(sw) / wsum
    g = 0.0
    for i in np.arange(1, n):
        g = g + pxi.iloc[i] * pci.iloc[i - 1] - pci.iloc[i] * pxi.iloc[i - 1]
    return g

This works for large vectors, at least up to 10M rows:

n = 1e7
gini(np.random.rand(n), np.random.rand(n))  # Takes ~15s.

It also produces the same result as the function provided in the question, for example giving 0.2553 for this example:

gini(np.array([3, 1, 6, 2, 1]), np.array([4, 2, 2, 10, 1]))