Efficient and precise calculation of the euclidean distance

Question

Following some online research (1, 2, numpy, scipy, scikit, math), I have found several ways for calculating the Euclidean Distance in Python:

Answer 1

This is not exactly answering the question, but it is probably worth mentioning that if you aren't interested in the actual euclidean distance, but just want to compare euclidean distances against each other, square roots are monotone functions, i.e. x**(1/2) < y**(1/2) if and only if x < y.

So if you don't want the explicit distance, but for instance just want to know if the euclidean distance of vector1 is closer to a list of vectors, called vectorlist, you can avoid the expensive (in terms of both precision and time) square root, but can make do with something like

min(vectorlist, key = lambda compare: sum([(a - b)**2 for a, b in zip(vector1, compare)])