Function quantile

numpy/lib/function_base.py:4293–4544

Compute the q-th quantile of the data along the specified axis. .. versionadded:: 1.15.0 Parameters ---------- a : array_like of real numbers Input array or object that can be converted to an array. q : array_like of float Probability or sequence of probabi

(a,
             q,
             axis=None,
             out=None,
             overwrite_input=False,
             method="linear",
             keepdims=False,
             *,
             interpolation=None)

@array_function_dispatch(_quantile_dispatcher)
def quantile(a,
             q,
             axis=None,
             out=None,
             overwrite_input=False,
             method="linear",
             keepdims=False,
             *,
             interpolation=None):
    """
    Compute the q-th quantile of the data along the specified axis.

    .. versionadded:: 1.15.0

    Parameters
    ----------
    a : array_like of real numbers
        Input array or object that can be converted to an array.
    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'
        * 'nearest'

        .. versionchanged:: 1.22.0