Class PandasMultiIndexingAdapter

xarray/core/indexing.py:2075–2154

Handles explicit indexing for a pandas.MultiIndex. This allows creating one instance for each multi-index level while preserving indexing efficiency (memoized + might reuse another instance with the same multi-index).

class PandasMultiIndexingAdapter(PandasIndexingAdapter):
    """Handles explicit indexing for a pandas.MultiIndex.

    This allows creating one instance for each multi-index level while
    preserving indexing efficiency (memoized + might reuse another instance with
    the same multi-index).
    """

    __slots__ = ("_dtype", "adapter", "array", "level")

    array: pd.MultiIndex
    _dtype: np.dtype | pd.api.extensions.ExtensionDtype
    level: str | None

    def __init__(
        self,
        array: pd.MultiIndex,
        dtype: DTypeLike | pd.api.extensions.ExtensionDtype | None = None,
        level: str | None = None,
    ):
        super().__init__(array, dtype)
        self.level = level

    def __array__(
        self,
        dtype: DTypeLike | None = None,
        /,
        *,
        copy: bool | None = None,
    ) -> np.ndarray:
        dtype = self._get_numpy_dtype(dtype)

        if self.level is not None:
            return np.asarray(
                self.array.get_level_values(self.level).values, dtype=dtype
            )
        else:
            return super().__array__(dtype, copy=copy)

    @property
    def _in_memory(self) -> bool:
        # The pd.MultiIndex's data is fully in memory, but it has a different
        # layout than the level and dimension coordinate arrays. Marking this
        # adapter class as a "lazy" array will prevent costly conversion when,
        # e.g., formatting the Xarray reprs.
        return False

    def _convert_scalar(self, item: Any):
        if isinstance(item, tuple) and self.level is not None:
            idx = tuple(self.array.names).index(self.level)
            item = item[idx]
        return super()._convert_scalar(item)

    def _index_get(
        self, indexer: ExplicitIndexer, func_name: str
    ) -> PandasIndexingAdapter | np.ndarray:
        result = super()._index_get(indexer, func_name)
        if isinstance(result, type(self)):