Merging series of 2D DataFrames to 3D xarray

I have a series of 2D DataFrames that should be merged into a 3D xarray. The structure of DataFrames looks like this:

My goal is to have a 3D xarray that will have such structure:

Dimensions:  (xyz: 2, thickness: 2, angle: 2)
Coordinates:
  * xyz       (xyz) object '5 6 4' '8 -1 3'
  * thickness (thickness) int 5 10
  * angle     (angle) float64 0.1 0.2
Data variables:
    I don't know how the variables should be sorted

For now I changed DataFrames into xarrays in such a manner:

xa = xarray.Dataset.from_dataframe(df).set_coords("angle")

The 2D xarrays look like this:

Dimensions:    (thickness: 2)
Coordinates:
  * thickness  (thickness) int 5 10
    angles     (thickness) float64 0.1 0.1
Data variables:
    5 6 4      (thickness) float64 105.87 211.74
    8 -1 3     (thickness) float64 459.62 919.24

Then when I try to merge the xarrays with .merge, I got an error MergeError: conflicting values for variable '0 0 0 ' on objects to be combined. You can skip this check by specifying compat='override'.

I wanted to know:

1. How to turn angles into a dimension? Seems that it’s something different than coordinates.
2. How to make this list of xyz coordinates (‘5 6 4’, ‘8 -1 3’) into another dimension called ‘xyz’?

>Solution :

Pandas MultiIndex levels become xarray dimensions and coordinates. DataFrame columns become Dataset variables; series are converted to DataArrays.

So the key is to arrange your data properly in pandas first. You could do this with a number of the pandas reshaping methods, but here’s one:

da = (
    df.rename_axis("thickness")
    .set_index("angle", append =True)
    .rename_axis("xyz", axis=1)
    .stack("xyz")
    .to_xarray()
)