There are a bunch of points X, given in n-dimensional space (suppose we have two-dimensional, that is, the dimension of the array X is a *bunch of* x 2),

there is a single point C in the same space (dimension 1x2)

and there is a dist (u, v) metric, defined over a space, which determines the distance between two points (let us have a Euclidean metric, dist () returns a float).

Is there in pure python or in numpy (or somewhere else, to peep) a way to calculate the distances from C to all points X, faster than

`[dst(x,C) for x in X]`

?

And it is corny for a very long time in seconds.

In principle, it is also sufficient to obtain only the sum of these distances, if it suddenly turns out to be easier.