In this case you just doing fine. Just use different variables for looping:
ne = np.randn(768,)
naa = np.randn(768,)

de=np.zeros((1, ))
for ne_, naa_ in zip(ne, naa):          
    de+=np.sqrt((ne_ - naa_)**2)
de/ne.shape[0]

But using loops are not efficient. The more optimized way to this using numpy:
np.sum(np.sqrt((ne - naa)**2)) / ne.shape[0]

Sklearn:
from sklearn.metrics.pairwise import euclidean_distances
np.sum(np.diag(euclidean_distances(ne.reshape(-1, 1), naa.reshape(-1, 1))))/ne.shape[0]

Sorry that I did not understand your question very well.
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