I concur with the consensus.

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On 10 Aug 2017, 11:10 PM +0200, Eric Wieser <wieser.eric+nu...@gmail.com>,
wrote:
> Let’s try and keep this on topic - most replies to this message has been
> about #9211, which is an orthogonal issue.
> There are two main questions here:
>
> 1. Would the community prefer to use np.quantile(x, 0.25) instead of
> np.percentile(x, 25), if they had the choice
> 2. Is this desirable enough to justify increasing the API surface?
>
> The general consensus on the github issue answers yes to 1, but is neutral on
> 2. It would be good to get more opinions.
> Eric
> On Fri, 21 Jul 2017 at 16:12 Chun-Wei Yuan chunwei.y...@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%):
> >
> > https://github.com/numpy/numpy/pull/9213
> >
> > 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().
> >
> > Best,
> >
> > C
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> > NumPy-Discussion@python.org
> > https://mail.python.org/mailman/listinfo/numpy-discussion
>
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