xarray.DataArray.isel

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xarray.DataArray.isel#

DataArray.isel(indexers=None, drop=False, missing_dims='raise', **indexers_kwargs)[source]#

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 (xarray.DataArray)

See also

Dataset.isel DataArray.sel

Indexing

Tutorial material on indexing with Xarray objects

Indexing and Selecting Data

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
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