Method reset_index

xarray/core/dataset.py:4890–5000

Reset the specified index(es) or multi-index level(s). This legacy method is specific to pandas (multi-)indexes and 1-dimensional "dimension" coordinates. See the more generic :py:meth:`~Dataset.drop_indexes` and :py:meth:`~Dataset.set_xindex` method to respectively

(
        self,
        dims_or_levels: Hashable | Sequence[Hashable],
        *,
        drop: bool = False,
    )

        )

    def reset_index(
        self,
        dims_or_levels: Hashable | Sequence[Hashable],
        *,
        drop: bool = False,
    ) -> Self:
        """Reset the specified index(es) or multi-index level(s).

        This legacy method is specific to pandas (multi-)indexes and
        1-dimensional "dimension" coordinates. See the more generic
        :py:meth:`~Dataset.drop_indexes` and :py:meth:`~Dataset.set_xindex`
        method to respectively drop and set pandas or custom indexes for
        arbitrary coordinates.

        Parameters
        ----------
        dims_or_levels : Hashable or Sequence of Hashable
            Name(s) of the dimension(s) and/or multi-index level(s) that will
            be reset.
        drop : bool, default: False
            If True, remove the specified indexes and/or multi-index levels
            instead of extracting them as new coordinates (default: False).

        Returns
        -------
        obj : Dataset
            Another dataset, with this dataset's data but replaced coordinates.

        See Also
        --------
        Dataset.set_index
        Dataset.set_xindex
        Dataset.drop_indexes
        """
        if isinstance(dims_or_levels, str) or not isinstance(dims_or_levels, Sequence):
            dims_or_levels = [dims_or_levels]

        invalid_coords = set(dims_or_levels) - set(self._indexes)
        if invalid_coords:
            raise ValueError(
                f"{tuple(invalid_coords)} are not coordinates with an index"
            )

        drop_indexes: set[Hashable] = set()
        drop_variables: set[Hashable] = set()
        seen: set[Index] = set()
        new_indexes: dict[Hashable, Index] = {}
        new_variables: dict[Hashable, Variable] = {}

        def drop_or_convert(var_names):
            if drop:
                drop_variables.update(var_names)
            else:
                base_vars = {
                    k: self._variables[k].to_base_variable() for k in var_names
                }
                new_variables.update(base_vars)