Function nanquantile

numpy/lib/_nanfunctions_impl.py:1406–1574

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

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

@array_function_dispatch(_nanquantile_dispatcher)
def nanquantile(
        a,
        q,
        axis=None,
        out=None,
        overwrite_input=False,
        method="linear",
        keepdims=np._NoValue,
        *,
        weights=None,
):
    """
    Compute the qth quantile of the data along the specified axis,
    while ignoring nan values.
    Returns the qth quantile(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
        Probability or sequence of probabilities for the quantiles to compute.
        Values must be between 0 and 1 inclusive.
    axis : {int, tuple of int, None}, optional
        Axis or axes along which the quantiles are computed. The
        default is to compute the quantile(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
        quantile.  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'
        * 'higher',
        * 'midpoint'