> Is this proposal still relevant?

Yes.

As Raymond says, deciding on a good API is the hard part. Its relatively 
simple to change a poor implementation for a better one, but backwards 
compatibility means that changing the API is very difficult.

I would find it very helpful if somebody has time to do a survey of 
other statistics libraries or languages (e.g. numpy, R, Octave, Matlab, 
SAS etc) and see how they handle data with weights.

- what APIs do they provide?
- do they require weights to be positive integers, or do they 
  support arbitrary float weights?
- including negative weights? 
  (what physical meaning does a negative weight have?)

At the moment, a simple helper function seems to do the trick for 
non-negative integer weights:

def flatten(items):
    for item in items:
        yield from item

py> data = [1, 2, 3, 4]
py> weights = [1, 4, 1, 2]
py> statistics.mean(flatten([x]*w for x, w in zip(data, weights)))
2.5

In principle, the implementation could be as simple as a single 
recursive call:

def mean(data, weights=None):
    if weights is not None:
        return mean(flatten([x]*w for x, w in zip(data, weights)))
    # base case without weights is unchanged

or perhaps it could be just a recipe in the docs.