I'm looking for a way of computing standard deviations from samples that allows the inclusion of sample weights.
numpy.average does that:
weights : array_like, optional An array of weights associated with the values in a. Each value in a contributes to the average according to its associated weight. The weights array can either be 1-D (in which case its length must be the size of a along the given axis) or of the same shape as a. If weights=None, then all data in a are assumed to have a weight equal to one.
, however, np.std() does not. Is there an alternative?
