Class PandasIndexingAdapter

xarray/core/indexing.py:1898–2072

Wrap a pandas.Index to preserve dtypes and handle explicit indexing.

class PandasIndexingAdapter(IndexingAdapter):
    """Wrap a pandas.Index to preserve dtypes and handle explicit indexing."""

    __slots__ = ("_dtype", "array")

    array: pd.Index
    _dtype: np.dtype | pd.api.extensions.ExtensionDtype

    def __init__(
        self,
        array: pd.Index,
        dtype: DTypeLike | pd.api.extensions.ExtensionDtype | None = None,
    ):
        from xarray.core.indexes import safe_cast_to_index

        self.array = safe_cast_to_index(array)

        if dtype is None:
            if is_allowed_extension_array(array):
                cast(pd.api.extensions.ExtensionDtype, array.dtype)
                self._dtype = array.dtype
            else:
                self._dtype = get_valid_numpy_dtype(array)
        elif is_allowed_extension_array_dtype(dtype):
            self._dtype = cast(pd.api.extensions.ExtensionDtype, dtype)
        elif HAS_STRING_DTYPE and isinstance(dtype, pd.StringDtype):
            self._dtype = np.dtypes.StringDType(na_object=dtype.na_value)
        else:
            self._dtype = np.dtype(cast(DTypeLike, dtype))

    @property
    def _in_memory(self) -> bool:
        # prevent costly conversion of a memory-saving pd.RangeIndex into a
        # large numpy array.
        return not isinstance(self.array, pd.RangeIndex)

    @property
    def dtype(self) -> np.dtype | pd.api.extensions.ExtensionDtype:  # type: ignore[override]
        return self._dtype

    def _get_numpy_dtype(self, dtype: np.typing.DTypeLike | None = None) -> np.dtype:
        if dtype is None:
            if is_valid_numpy_dtype(self.dtype):
                return cast(np.dtype, self.dtype)
            else:
                return get_valid_numpy_dtype(self.array)
        else:
            return np.dtype(dtype)

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