isel

UnitsAwareDataArray.isel(indexers: Mapping[Any, Any] | None = None, drop: bool = False, missing_dims: ErrorOptionsWithWarn = 'raise', **indexers_kwargs: Any) → Self

Return a new DataArray whose data is given by selecting indexes along the specified dimension(s).

Parameters:

indexers (dict, optional) – A dict with keys matching dimensions and values given by integers, slice objects or arrays. indexer can be a integer, slice, array-like or DataArray. If DataArrays are passed as indexers, xarray-style indexing will be carried out. See Indexing and selecting data for the details. One of indexers or indexers_kwargs must be provided.
drop (bool, default: False) – If drop=True, drop coordinates variables indexed by integers instead of making them scalar.
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
**indexers_kwargs ({dim: indexer, ...}, optional) – The keyword arguments form of indexers.

Returns:

indexed

Return type:

xarray.DataArray

See also

Dataset.isel DataArray.sel

xarray-tutorial:intermediate/indexing/indexing: Tutorial material on indexing with Xarray objects
xarray-tutorial:fundamentals/02.1_indexing_Basic: Tutorial material on basics of indexing

Examples

>>> da = xr.DataArray(np.arange(25).reshape(5, 5), dims=("x", "y"))
>>> da
<xarray.DataArray (x: 5, y: 5)> Size: 200B
array([[ 0,  1,  2,  3,  4],
       [ 5,  6,  7,  8,  9],
       [10, 11, 12, 13, 14],
       [15, 16, 17, 18, 19],
       [20, 21, 22, 23, 24]])
Dimensions without coordinates: x, y

>>> tgt_x = xr.DataArray(np.arange(0, 5), dims="points")
>>> tgt_y = xr.DataArray(np.arange(0, 5), dims="points")
>>> da = da.isel(x=tgt_x, y=tgt_y)
>>> da
<xarray.DataArray (points: 5)> Size: 40B
array([ 0,  6, 12, 18, 24])
Dimensions without coordinates: points