optimization - Optimizing mean in python -

I have a function that updates centroid (mean) in the Kashmir algorithm. I ran a profiler and saw that this function uses a lot of computing time.

It looks:

  def updateCentroid (self, label): x = []; Y = [] for the point in the self. Cluster [label]. Point: X.append (point.x) Y.append (point.y) self.clusters [label] .centroid.x = numpy.mean (X) self.cluster [label] .centroid.y = numpy.mean ( Y)  

So I think, is there a more efficient way to calculate the mean of these points? If not, is there another great way to prepare it? ;)

Edit:

Thanks for all the great responses! I was thinking that maybe I can count less cumulative ways:

Where x_bar (t) means new and x_bar (t-1) is old means.

The result of which is similar:

  def UpdateCentroid (self, label): cluster = self.clusters [label] n = len (cluster.points) cluster. Centroid.x * = (n-1) / n cluster.centroid.x + = cluster.points [n-1] .x / n cluster.centrode.y * = (n-1) / n cluster.scentroy.ai + = Cluster point [N-1]. / N>  

This is not working really, but do you think it can work with some twiking?