Function nanpercentile

numpy/lib/_nanfunctions_impl.py:1227–1397

Compute the qth percentile of the data along the specified axis, while ignoring nan values. Returns the qth percentile(s) of the array elements. Parameters ---------- a : array_like Input array or object that can be converted to an array, containing nan val

(
        a,
        q,
        axis=None,
        out=None,
        overwrite_input=False,
        method="linear",
        keepdims=np._NoValue,
        *,
        weights=None,
)

@array_function_dispatch(_nanpercentile_dispatcher)
def nanpercentile(
        a,
        q,
        axis=None,
        out=None,
        overwrite_input=False,
        method="linear",
        keepdims=np._NoValue,
        *,
        weights=None,
):
    """
    Compute the qth percentile of the data along the specified axis,
    while ignoring nan values.

    Returns the qth percentile(s) of the array elements.

    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:

        1. 'inverted_cdf'
        2. 'averaged_inverted_cdf'
        3. 'closest_observation'
        4. 'interpolated_inverted_cdf'
        5. 'hazen'
        6. 'weibull'
        7. 'linear'  (default)
        8. 'median_unbiased'
        9. 'normal_unbiased'

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

        * 'lower'