code: def _nanpercentile_1d(values, mask, q, na_value, interpolation):
      
      # 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)
docs: 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