I think that there would be a very good reason to have a separate function if we were to introduce weights to the inputs, similarly to the way that we have mean and average. This would have some (positive) repercussions like making weighted histograms with the Freedman-Diaconis binwidth estimator a possibility. I have had this change on the back-burner for a long time, mainly because I was too lazy to figure out how to include it in the C code. However, I will take a closer look.



On Fri, Jul 21, 2017 at 5:11 PM, Chun-Wei Yuan <chunwei.yuan@gmail.com> wrote:
There's an ongoing effort to introduce quantile() into numpy.  You'd use it just like percentile(), but would input your q value in probability space (0.5 for 50%):


Since there's a great deal of overlap between these two functions, we'd like to solicit opinions on how to move forward on this.

The current thinking is to tolerate the redundancy and keep both, using one as the engine for the other.  I'm partial to having quantile because 1.) I prefer probability space, and 2.) I have a PR waiting on quantile().



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