def _nanpercentile_1d(values, mask, q, na_value, interpolation):
    """
    Wraper for np.percentile that skips missing values, specialized to
    1-dimensional case.

    Parameters
    ----------
    values : array over which to find quantiles
    mask : ndarray[bool]
        locations in values that should be considered missing
    q : scalar or array of quantile indices to find
    na_value : scalar
        value to return for empty or all-null values
    interpolation : str

    Returns
    -------
    quantiles : scalar or array
    """
    # mask is Union[ExtensionArray, ndarray]
    values = values[~mask]

    if len(values) == 0:
        if lib.is_scalar(q):
            return na_value
        else:
            return np.array([na_value] * len(q),
                            dtype=values.dtype)

    return np.percentile(values, q, interpolation=interpolation)
def _nanpercentile_1d ( values , mask , q , na_value , interpolation ) : values = values [ ~ mask ] if len ( values ) == 0 : if lib . is_scalar ( q ) : return na_value else : return np . array ( [ na_value ] * len ( q ) , dtype = values . dtype ) return np . percentile ( values , q , interpolation = interpolation )
Wraper for np.percentile that skips missing values, specialized to
    1-dimensional case.

    Parameters
    ----------
    values : array over which to find quantiles
    mask : ndarray[bool]
        locations in values that should be considered missing
    q : scalar or array of quantile indices to find
    na_value : scalar
        value to return for empty or all-null values
    interpolation : str

    Returns
    -------
    quantiles : scalar or array