# scipy.stats.scoreatpercentile

scipy.stats.scoreatpercentile(a, per, limit=(), interpolation_method='fraction', axis=None)

  Calculate the score at a given percentile of the input sequence.

  For example, the score at per=50 is the median. If the desired quantile lies between two data points, we interpolate between them, according to the value of interpolation. If the parameter limit is provided, it should be a tuple (lower, upper) of two values.

  Parameters
    a array_like

      A 1-D array of values from which to extract score.

    per array_like

      Percentile(s) at which to extract score. Values should be in range [0,100].

    limit tuple, optional

      Tuple of two scalars, the lower and upper limits within which to compute the percentile. Values of a outside this (closed) interval will be ignored.

    interpolation_method{‘fraction’, ‘lower’, ‘higher’}, optional

      Specifies the interpolation method to use, when the desired quantile lies between two data points i and j The following options are available (default is ‘fraction’):

      > - ‘fraction’: `i + (j - i) * fraction` where `fraction` is the fractional part of the index surrounded by `i` and `j`

      > - ‘lower’: `i`

      > - ‘higher’: `j`

    axis int, optional

      Axis along which the percentiles are computed. Default is None. If None, compute over the whole array a.

  Returns
    score float or ndarray

      Score at percentile(s).

  See also

  `percentileofscore`, `numpy.percentile`

  Notes

  This function will become obsolete in the future. For NumPy 1.9 and higher, `numpy.percentile` provides all the functionality that `scoreatpercentile` provides. And it’s significantly faster. Therefore it’s recommended to use `numpy.percentile` for users that have numpy >= 1.9.

  Examples

      >>> from scipy import stats
      >>> a = np.arange(100)
      >>> stats.scoreatpercentile(a, 50)
      49.5

scipy.stats.percentileofscore scipy.stats.relfreq