Percentile Linear returns incorrect value.

The percentile function does not return the expected values. It seems to be getting the linear distance between two points the wrong way around and returns a value closer to the lower number.

For example:

array([   110531353,    167471747,    167471747,    183000406,
          200000000,    759174457,    921094606,    931142911,
         1300000000,   1341797102,   1380317195,   1380317195,
         1500000000,   1500000000,   1500000000,   1830004057,
         1932444073,   2000000000,   2000000000, 2345976525, 2500000000,
       2745006085, 2847019692, 3000000000, 3000000000, 3000000000,
       3000000000, 3312761268, 3500000000, 3588824707, 4000000000,
       4140951585, 5000000000, 6600000000, 7100000000, 7717299940,
       8445515490, 8972061767, 9662220364, 11000000000, 11042537559,
       12422854754, 13000000000, 13000000000, 13803171949, 15000000000,
       17000000000, 20000000000, 22085075118, 24845709508, 32025070994,
       34000000000, 35000000000, 36000000000, 36000000000, 39453076027,
       40000000000, 46930784627, 82819031694, 110425375592], dtype=int64)

> > > np.percentile(a,75)
> > > 14102378961.75
> > > np.percentile(a,50)
> > > 3794412353.5
> > > np.percentile(a,25)
> > > 1747503042.75

expected values:

75 = 14700792987.25
50 = 3794412353.5
25 = 1582501014.25

When reading the documentation the linear methodology seems correct but there possibly could be an issue with the fraction it is using??

[seberg]

There is nothing wrong with it, but there are different ways to calculate percentile, see gh-10736. In particular, like many many default values, NumPy’s is a sample percentile and not a population estimtae (there are many population estimate choices!). Comments on that PR are very welcome.

[ricardoV94]

@apbard I completely agree with you that the Method1 should be available, and that the names of the other interpolation methods are confusing. I posted this issue separately in here before finding your message in this thread.