Dear all, I have a long-standing PR (#9211 <https://github.com/numpy/numpy/pull/9211>) about adding a "weights" option to the quantile/percentile methods' API. The code is tested for all 13 different interpolation methods, and I welcome any additional testing suggestions.
A couple of remarks: 1.) weights can be any positive real number, and there's no distinction between frequency weights and probability weights. Underneath, along the data axis you're applying weights to, if any weight is less than 1, then the code will renormalize the weights such that the minimum is 1. For example, if weights=[0.1, 0.9], then the quantiles will be computed as if weights=[1, 9]. If weights=[0.25, 0.75], then they'll be treated as if weights=[1, 3]. In other words, probability weights are converted to frequency weights for the calculation. If another function using quantile() wants to make that distinction between frequency/probability weights in its API, it is free to do so. But right now I do not see any reason for it in quantile()/percentile(). 2.) The use of np.vectorize(). In computing quantiles, it's necessary to sort the data values along the data axis, and rearrange the corresponding weights according to the sort order. Hence each column down the data axis has a different rearrangement. vectorize() allows for such synchronized operations between equal-shaped arrays. I don't see that from, say, apply_along_axis(). Lastly, I'll be sure to add more docs/comments to better explain the steps in the code. Best, Chun
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