xarray.DataArray.unstack#

DataArray.unstack(dim=None, *, fill_value=<NA>, sparse=False)[source]#

Unstack existing dimensions corresponding to MultiIndexes.

The dimensions are unstacked into multiple new dimensions.

New dimensions will be added at the end.

Parameters:

dim (str, Iterable of Hashable or None, optional) – Dimension(s) over which to unstack. By default unstacks all MultiIndexes.
fill_value (scalar or dict-like, default: nan) – Value to be filled. If a dict-like, maps variable names to fill values. Use the data array’s name to refer to its name. If not provided or if the dict-like does not contain all variables, the dtype’s NA value will be used.
sparse (bool, default: False) – Use sparse-array if True

Returns:

unstacked (DataArray) – Array with unstacked data.

See also

DataArray.stack

Notes

The order of the new dimension coordinates follows the levels of the MultiIndex. A MultiIndex created by pandas (e.g. when converting a pd.Series with a MultiIndex) usually has sorted levels, so the resulting coordinates will be sorted. A MultiIndex created by DataArray.stack() preserves the original order. Use DataArray.sel() after unstacking to select a specific order if needed.

Examples

>>> arr = xr.DataArray(
...     np.arange(6).reshape(2, 3),
...     coords=[("x", ["a", "b"]), ("y", [0, 1, 2])],
... )
>>> arr
<xarray.DataArray (x: 2, y: 3)> Size: 48B
array([[0, 1, 2],
       [3, 4, 5]])
Coordinates:
  * x        (x) <U1 8B 'a' 'b'
  * y        (y) int64 24B 0 1 2
>>> stacked = arr.stack(z=("x", "y"))
>>> stacked.indexes["z"]
MultiIndex([('a', 0),
            ('a', 1),
            ('a', 2),
            ('b', 0),
            ('b', 1),
            ('b', 2)],
           name='z')
>>> roundtripped = stacked.unstack()
>>> arr.identical(roundtripped)
True