numpy:nanpercentile

Parameters

a : array_like

Input array or object that can be converted to an array, containing nan values to be ignored.

q : array_like of float

Percentile or sequence of percentiles to compute, which must be between 0 and 100 inclusive.

axis : {int, tuple of int, None}, optional

Axis or axes along which the percentiles are computed. The default is to compute the percentile(s) along a flattened version of the array.

out : ndarray, optional

Alternative output array in which to place the result. It must have the same shape and buffer length as the expected output, but the type (of the output) will be cast if necessary.

overwrite_input : bool, optional

If True, then allow the input array a to be modified by intermediate calculations, to save memory. In this case, the contents of the input a after this function completes is undefined.

method : str, optional

This parameter specifies the method to use for estimating the percentile. There are many different methods, some unique to NumPy. See the notes for explanation. The options sorted by their R type as summarized in the H&F paper ^[1] are:

• 'inverted_cdf'

• 'averaged_inverted_cdf'

• 'closest_observation'

• 'interpolated_inverted_cdf'

• 'hazen'

• 'weibull'

• 'linear' (default)

• 'median_unbiased'

• 'normal_unbiased'

The first three methods are discontinuous. NumPy further defines the following discontinuous variations of the default 'linear' (7.) option:

• 'lower'

• 'higher',

• 'midpoint'

• 'nearest'

keepdims : bool, optional

If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, the result will broadcast correctly against the original array a.

If this is anything but the default value it will be passed through (in the special case of an empty array) to the mean function of the underlying array. If the array is a sub-class and mean does not have the kwarg keepdims this will raise a RuntimeError.

weights : array_like, optional

An array of weights associated with the values in a. Each value in a contributes to the percentile according to its associated weight. The weights array can either be 1-D (in which case its length must be the size of a along the given axis) or of the same shape as a. If weights=None, then all data in a are assumed to have a weight equal to one. Only method="inverted_cdf" supports weights.