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The ETCCDI is rather picky about percentiles. According to them (as per implementation in reference code), the method to calculate the percentile should be [Hyndman & Fan method #8](https://www.researchgate.net/profile/Rob_Hyndman/publication/222105754_Sample_Quantiles_in_Statistical_Packages/links/02e7e530c316d129d7000000.pdf) [1]. This is also the preferred method by [NIST](https://www.itl.nist.gov/div898//software/dataplot/refman2/auxillar/percenti.htm). This method is available in the [R package](https://stat.ethz.ch/R-manual/R-devel/library/stats/html/quantile.html), although not as default, and in [scipy.stats.mstats.mquantiles](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.mstats.mquantiles.html).
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The method is however not available in [numpy.percentile](https://docs.scipy.org/doc/numpy/reference/generated/numpy.percentile.html), and there seems to be some [confusion regarding methods and their implementation](https://github.com/numpy/numpy/issues/10736). In particular there is an outline for a numpy implementation of all H&F methods in [th`alphap=0.4` and `betap=0.4`is comment](https://github.com/numpy/numpy/issues/10736#issuecomment-390425384), but it seems that progress on this has stalled.
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The method is however not available in [numpy.percentile](https://docs.scipy.org/doc/numpy/reference/generated/numpy.percentile.html), and there seems to be some [confusion regarding methods and their implementation](https://github.com/numpy/numpy/issues/10736). In particular there is an outline for a numpy implementation of all H&F methods in [this comment](https://github.com/numpy/numpy/issues/10736#issuecomment-390425384), but it seems that progress on this has stalled.
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Moreover, the python percentile calculation 'ecosystem' becomes more diverse with a [Python3.8 percentile](https://docs.python.org/dev/library/statistics.html#statistics.quantiles) function.
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