xarray/core/variable.py
def isel(
        self: VariableType,
        indexers: Mapping[Hashable, Any] = None,
        missing_dims: str = "raise",
        **indexers_kwargs: Any,
    ) -> VariableType:
        """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
            - "warning": 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]
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
            - "warning": 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.
        
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.

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