from sklearn.metrics import pairwise_distances
feature_distances = pairwise_distances(features)
mapping_distances = pairwise_distances(mapping)

def rank_matrix(distances):
    rank = np.zeros(distances.shape)
    for (i,j), value in np.ndenumerate(distances):
        rank[i,j] = len([k for k, d in enumerate(distances[i]) if d < distances[i,j] or (d == distances[i,j] and k < j)])
    return rank

f_rank = rank_matrix(feature_distances)
m_rank = rank_matrix(mapping_distances)

q = np.zeros(f_rank.shape)
for (k,l), _ in np.ndenumerate(q):
    q[k,l] = np.where((m_rank == k) & (f_rank == l))[0].size