Hello numpy list! I had tried to contribute to the AccumarrayLike recipe cookbook, but access seems to be restricted and no registration allowed. The current recipe mimics the matlab version in most aspects, but concerning performance it's horribly slow. The following snippet handles only the most simple usecase, but gives about 16x speed improvement compared to the current receipe, so it would probably worth to also mention it in the cookbook: def accum_custom(accmap, a, func=np.sum): indices = np.where(np.ediff1d(accmap, to_begin=[1], to_end=[np.nan]))[0] vals = np.zeros(len(indices) - 1) for i in xrange(len(indices) - 1): vals[i] = func(a[indices[i]:indices[i+1]]) return vals accmap = np.repeat(np.arange(100000), 20) a = np.random.randn(accmap.size) %timeit accum(accmap, a, func=np.sum)

...

...

1 loops, best of 3: 16.7 s per loop

%timeit accum_custom(accmap, a, func=np.sum)

...

...

1 loops, best of 3: 945 ms per loop

Best regards, Michael