to_dask_dataframe

UnitsAwareDataArray.to_dask_dataframe(dim_order: Sequence[Hashable] | None = None, set_index: bool = False) → DaskDataFrame

Convert this array into a dask.dataframe.DataFrame.

Parameters:

• dim_order (Sequence of Hashable or None , optional) – Hierarchical dimension order for the resulting dataframe. Array content is transposed to this order and then written out as flat vectors in contiguous order, so the last dimension in this list will be contiguous in the resulting DataFrame. This has a major influence on which operations are efficient on the resulting dask dataframe.

• set_index (bool, default: False) – If set_index=True, the dask DataFrame is indexed by this dataset’s coordinate. Since dask DataFrames do not support multi-indexes, set_index only works if the dataset only contains one dimension.

Return type:

dask.dataframe.DataFrame

Examples

>>> da = xr.DataArray(
...     np.arange(4 * 2 * 2).reshape(4, 2, 2),
...     dims=("time", "lat", "lon"),
...     coords={
...         "time": np.arange(4),
...         "lat": [-30, -20],
...         "lon": [120, 130],
...     },
...     name="eg_dataarray",
...     attrs={"units": "Celsius", "description": "Random temperature data"},
... )
>>> da.to_dask_dataframe(["lat", "lon", "time"]).compute()
    lat  lon  time  eg_dataarray
0   -30  120     0             0
1   -30  120     1             4
2   -30  120     2             8
3   -30  120     3            12
4   -30  130     0             1
5   -30  130     1             5
6   -30  130     2             9
7   -30  130     3            13
8   -20  120     0             2
9   -20  120     1             6
10  -20  120     2            10
11  -20  120     3            14
12  -20  130     0             3
13  -20  130     1             7
14  -20  130     2            11
15  -20  130     3            15