Hi, Joe,
> I am working (slowly) on upgrading the C code for partitioning with
> arbitrary arrays of real weights
really good to know there is some work in this direction.
02 марта 2016 г., в 6:27, Joseph Fox-Rabinovitz
написал(а):
> 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.
It’s the same sort+cumsum approach, and even worse because relies on
aggregating.
Thanks for letting know, but I’ll definitely prefer implementation from SO
(till numpy will support weights).
Cheers,
Alex
>
>-Joe
>
>
> On Tue, Mar 1, 2016 at 6:03 PM, Alex Rogozhnikov
> 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.
>>
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