Alex, At the moment, there does not appear to be anything in numpy. However, I am working (slowly) on upgrading the C code for partitioning with arbitrary arrays of real weights. That will get `partition`, `median`, `percentile` to work with weights, as well as enabling weights for the automated bin estimators of `histogram`. `mean` already has an implementation of weights via `average`.
You may be interested in my original post to the mailing list here: https://mail.scipy.org/pipermail/numpy-discussion/2016-February/075000.html. Josef P. mentioned in one of his responses that statsmodels has a weighted quantile computation available as of PR 2707: https://github.com/statsmodels/statsmodels/pull/2707. That should effectively serve your purpose. -Joe On Tue, Mar 1, 2016 at 6:03 PM, Alex Rogozhnikov <alex.rogozhni...@yandex.ru> wrote: > Hi, > I know the topic was already raised a long ago: > https://mail.scipy.org/pipermail/numpy-discussion/2010-July/051851.html > > There are also several questions on SO: > http://stackoverflow.com/questions/20601872/numpy-or-scipy-to-calculate-weighted-median > http://stackoverflow.com/questions/13546146/percentile-calculation-with-weighted-data > http://stackoverflow.com/questions/26102867/python-weighted-median-algorithm-with-pandas > > The only working solution with numpy: > http://stackoverflow.com/questions/21844024/weighted-percentile-using-numpy > uses sorting. > > Are there better options at the moment (numpy/scipy/pandas)? > > Cheers, > Alex. > > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > https://mail.scipy.org/mailman/listinfo/numpy-discussion > _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org https://mail.scipy.org/mailman/listinfo/numpy-discussion
