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Author steven.daprano
Recipients gregory.p.smith, ncoghlan, oscarbenjamin, remi.lapeyre, rhettinger, steven.daprano, wolma
Date 2019-01-19.06:30:43
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> Is this proposal still relevant?


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)))

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.
Date User Action Args
2019-01-19 06:30:45steven.dapranosetrecipients: + steven.daprano, rhettinger, gregory.p.smith, ncoghlan, oscarbenjamin, wolma, remi.lapeyre
2019-01-19 06:30:43steven.dapranolinkissue20479 messages
2019-01-19 06:30:43steven.dapranocreate