Ivan
2020-12-22 21:00:49
I'm not sure if that's the mathematical result you are looking for. You could use stats.kendalltau to compute the distance for all possible pairs, then take the variance from that resulting vector.
To get the vector of distances, I loop through the zipped list (a, a-shifted) using np.roll:
dist = []
for x1, x2 in zip(a, np.roll(a, shift=1, axis=0)):
dist.append(kendalltau(x1, x2)[0])
To take the variance of all distances:
np.std(dist)
Or if you are looking for the variance as 
(discussed here)
then take the norm of the distance vector:
np.linalg.norm(dist)
Note I'm using a as defined with np.array, not np.matrix:
a = np.array([[1,2,3,4,5,6], [2,3,4,6,1,5], [6,3,1,2,5,4]])