Python Change Dimension and Coordinates Xarray Dataset

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I have a xarray Dataset that looks like this below. I need to be able to plot by latitude and longitude any of the three variables in "Data variables: si10, si10_u, avg". However, I cannot figure out how to change the dimensions to latitude and longitude from index_id. Or, to delete "index_id" in Coordinates. I've tried that and then 'latitude' and 'longitude' disappear from "Coordinates". Thank you for suggestions.

Here is my xarray Dataset:

<xarray.Dataset>
Dimensions:    (index: 2448, index_id: 2448)
Coordinates:
  * index_id   (index_id) MultiIndex
  - latitude   (index_id) float64 58.0 58.0 58.0 58.0 ... 23.0 23.0 23.0 23.0
  - longitude  (index_id) float64 -130.0 -129.0 -128.0 ... -65.0 -64.0 -63.0
Dimensions without coordinates: index
Data variables:
    si10       (index) float32 1.7636629 1.899161 ... 5.9699616 5.9121003
    si10_u     (index) float32 1.6784391 1.7533684 ... 6.13361 6.139127
    avg        (index) float32 1.721051 1.8262646 ... 6.0517855 6.025614
python-xarray coordinates dataset python
1 Answers

You have two issues. First, you need to replace 'index' with 'index_id' so your data is indexed consistently. Second, to unstack 'index_id', you're looking for xr.Dataset.unstack:

ds = ds.unstack('index_id')

As an example... here's a dataset like yours

In [16]: y = np.arange(58, 23, -1)
    ...: x = np.arange(-130, -63, 1)

In [17]: ds = xr.Dataset(
    ...:     data_vars={
    ...:         v: (("index",), np.random.random(len(x) * len(y)))
    ...:         for v in ["si10", "si10_u", "avg"]
    ...:     },
    ...:     coords={
    ...:         "index_id": pd.MultiIndex.from_product(
    ...:             [y, x], names=["latitude", "longitude"],
    ...:         ),
    ...:     },
    ...: )

In [18]: ds
Out[18]:
<xarray.Dataset>
Dimensions:    (index: 2345, index_id: 2345)
Coordinates:
  * index_id   (index_id) MultiIndex
  - latitude   (index_id) int64 58 58 58 58 58 58 58 58 ... 24 24 24 24 24 24 24
  - longitude  (index_id) int64 -130 -129 -128 -127 -126 ... -68 -67 -66 -65 -64
Dimensions without coordinates: index
Data variables:
    si10       (index) float64 0.9412 0.7395 0.6843 ... 0.03979 0.4259 0.09203
    si10_u     (index) float64 0.7359 0.1984 0.5919 ... 0.5535 0.2867 0.4093
    avg        (index) float64 0.04257 0.1442 0.008705 ... 0.1911 0.2669 0.1498

First, reorganize your data to have consistent dims:

In [19]: index_id = ds['index_id']
In [20]: ds = (
    ...:     ds.drop("index_id")
    ...:     .rename({"index": "index_id"})
    ...:     .assign_coords(index_id=index_id)
    ...: )

Then, ds.unstack reorganizes the data to be the combinatorial product of all dimensions in the MultiIndex:

In [21]: ds.unstack("index_id")
Out[21]:
<xarray.Dataset>
Dimensions:    (latitude: 35, longitude: 67)
Coordinates:
  * latitude   (latitude) int64 24 25 26 27 28 29 30 31 ... 52 53 54 55 56 57 58
  * longitude  (longitude) int64 -130 -129 -128 -127 -126 ... -67 -66 -65 -64
Data variables:
    si10       (latitude, longitude) float64 0.9855 0.1467 ... 0.6569 0.9479
    si10_u     (latitude, longitude) float64 0.4672 0.2664 ... 0.4894 0.128
    avg        (latitude, longitude) float64 0.3738 0.01793 ... 0.1264 0.21
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