Efficiently normalize word embeddings

I'm using glove word embedding and would like to [-1,1] normalize it using python. The data is in the format of a dict with the word as key and a np array as value. Thus I would have to loop through all 2m entries, get the min and max and then loop again to normalize it.

Is there a more efficient way to do that?

import numpy as np

from sklearn.preprocessing import minmax_scale