Method isel

xarray/core/variable.py:1108–1141

Return a new array indexed along the specified dimension(s). Parameters ---------- **indexers : {dim: indexer, ...} Keyword arguments with names matching dimensions and values given by integers, slice objects or arrays. missing_dims : {"raise"

(
        self,
        indexers: Mapping[Any, Any] | None = None,
        missing_dims: ErrorOptionsWithWarn = "raise",
        **indexers_kwargs: Any,
    )

            return self.isel({dim: np.concatenate(indices)})

    def isel(
        self,
        indexers: Mapping[Any, Any] | None = None,
        missing_dims: ErrorOptionsWithWarn = "raise",
        **indexers_kwargs: Any,
    ) -> Self:
        """Return a new array indexed along the specified dimension(s).

        Parameters
        ----------
        **indexers : {dim: indexer, ...}
            Keyword arguments with names matching dimensions and values given
            by integers, slice objects or arrays.
        missing_dims : {"raise", "warn", "ignore"}, default: "raise"
            What to do if dimensions that should be selected from are not present in the
            DataArray:
            - "raise": raise an exception
            - "warn": raise a warning, and ignore the missing dimensions
            - "ignore": ignore the missing dimensions

        Returns
        -------
        obj : Array object
            A new Array with the selected data and dimensions. In general,
            the new variable's data will be a view of this variable's data,
            unless numpy fancy indexing was triggered by using an array
            indexer, in which case the data will be a copy.
        """
        indexers = either_dict_or_kwargs(indexers, indexers_kwargs, "isel")

        indexers = drop_dims_from_indexers(indexers, self.dims, missing_dims)

        key = tuple(indexers.get(dim, slice(None)) for dim in self.dims)
        return self[key]

    def squeeze(self, dim=None):
        """Return a new object with squeezed data.