Method get_indexer_non_unique

pandas/core/indexes/interval.py:853–933

Compute indexer and mask for new index given the current index. The indexer should be then used as an input to ndarray.take to align the current data to the new index. Parameters ---------- target : IntervalIndex or list of Intervals An

(
        self, target: Index
    )

        return ensure_platform_int(indexer)

    def get_indexer_non_unique(
        self, target: Index
    ) -> tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]]:
        """
        Compute indexer and mask for new index given the current index.

        The indexer should be then used as an input to ndarray.take to align the
        current data to the new index.

        Parameters
        ----------
        target : IntervalIndex or list of Intervals
            An iterable containing the values to be used for computing indexer.

        Returns
        -------
        indexer : np.ndarray[np.intp]
            Integers from 0 to n - 1 indicating that the index at these
            positions matches the corresponding target values. Missing values
            in the target are marked by -1.
        missing : np.ndarray[np.intp]
            An indexer into the target of the values not found.
            These correspond to the -1 in the indexer array.

        See Also
        --------
        Index.get_indexer : Computes indexer and mask for new index given
            the current index.
        Index.get_indexer_for : Returns an indexer even when non-unique.

        Examples
        --------
        >>> index = pd.Index(["c", "b", "a", "b", "b"])
        >>> index.get_indexer_non_unique(["b", "b"])
        (array([1, 3, 4, 1, 3, 4]), array([], dtype=int64))

        In the example below there are no matched values.

        >>> index = pd.Index(["c", "b", "a", "b", "b"])
        >>> index.get_indexer_non_unique(["q", "r", "t"])
        (array([-1, -1, -1]), array([0, 1, 2]))

        For this reason, the returned ``indexer`` contains only integers equal to -1.
        It demonstrates that there's no match between the index and the ``target``
        values at these positions. The mask [0, 1, 2] in the return value shows that
        the first, second, and third elements are missing.

        Notice that the return value is a tuple contains two items. In the example
        below the first item is an array of locations in ``index``. The second
        item is a mask shows that the first and third elements are missing.

        >>> index = pd.Index(["c", "b", "a", "b", "b"])
        >>> index.get_indexer_non_unique(["f", "b", "s"])
        (array([-1,  1,  3,  4, -1]), array([0, 2]))
        """
        target = ensure_index(target)

        if not self._should_compare(target) and not self._should_partial_index(target):