scipy.stats.scoreatpercentile¶
- scipy.stats.scoreatpercentile(a, per, limit=(), interpolation_method='fraction', axis=None)[source]¶
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 : {‘fraction’, ‘lower’, ‘higher’}, optional
This optional parameter specifies the interpolation method to use, when the desired quantile lies between two data points i and j
- 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. The default (None) is to compute the median along a flattened version of the array.
Returns: score : float or ndarray
Score at percentile(s).
See also
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