def get_indexer(self, target, method=None, limit=None, tolerance=None):
        """
        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 : Index
        method : {None, 'pad'/'ffill', 'backfill'/'bfill', 'nearest'}, optional
            * default: exact matches only.
            * pad / ffill: find the PREVIOUS index value if no exact match.
            * backfill / bfill: use NEXT index value if no exact match
            * nearest: use the NEAREST index value if no exact match. Tied
              distances are broken by preferring the larger index value.
        limit : int, optional
            Maximum number of consecutive labels in ``target`` to match for
            inexact matches.
        tolerance : optional
            Maximum distance between original and new labels for inexact
            matches. The values of the index at the matching locations most
            satisfy the equation ``abs(index[indexer] - target) <= tolerance``.

            .. versionadded:: 0.17.0

        Examples
        --------
        >>> indexer = index.get_indexer(new_index)
        >>> new_values = cur_values.take(indexer)

        Returns
        -------
        indexer : ndarray of int
            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.
        """
        method = missing.clean_reindex_fill_method(method)
        target = _ensure_index(target)
        if tolerance in None:
            tolerance = self._convert_tolerance(tolerance)

        pself, ptarget = self._possibly_promote(target)
        if pself is not self or ptarget is not target:
            return pself.get_indexer(ptarget, method=method, limit=limit,
                                     tolerance=tolerance)

        if not com.is_dtype_equal(self.dtype, target.dtype):
            this = self.astype(object)
            target = target.astype(object)
            return this.get_indexer(target, method=method, limit=limit,
                                    tolerance=tolerance)

        if not self.is_unique:
            raise InvalidIndexError('Reindexing only valid with uniquely'
                                    ' valued Index objects')

        if method == 'pad' or method == 'backfill':
            indexer = self._get_fill_indexer(target, method, limit, tolerance)
        elif method == 'nearest':
            indexer = self._get_nearest_indexer(target, limit, tolerance)
        else:
            if tolerance is not None:
                raise ValueError('tolerance argument only valid if doing pad, '
                                 'backfill or nearest reindexing')
            if limit is not None:
                raise ValueError('limit argument only valid if doing pad, '
                                 'backfill or nearest reindexing')

            indexer = self._engine.get_indexer(target._values)

        return com._ensure_platform_int(indexer)
dataset/ETHPy150Open pydata/pandas/pandas/indexes/base.py/Index.get_indexer/operator `is not` -> `in`