issue_comments

Hi, I also vote for this function, My typical use-case.

There is some structure in 3D space and I need to "flatten it" to 2D. Let us say it is axially symetric so I assign R and Z coordinate to points (or r and theta in polar). And I want to simplify this using interp; however, it requuires unique coordinates.

I have some solution here: https://stackoverflow.com/questions/51058379/drop-duplicate-times-in-xarray

and adapted this into actuall function:

```python def distribure_uniform(ds, N_points=512):

ds_theta = ds.sortby("theta").swap_dims({"idx": "theta"})
_, index = np.unique(ds_theta['theta'], return_index=True)

ds_theta = ds_theta.isel(theta=index)

ds_theta = ds_theta.interp(
    theta=np.linspace(ds.theta.min(), ds.theta.max(), N_points))

ds_theta = ds_theta.swap_dims({"theta": "idx"})
return ds_theta

```

In an idal case I would like to write something like this:

```python def distribure_uniform(ds, N_points=512):

ds_theta= ds.unique("theta", sorted=False, sort=True)

ds_theta = ds_theta.swap_dims({"idx": "theta"})
ds_theta = ds_theta.interp(
    theta=np.linspace(ds.theta.min(), ds.theta.max(), N_points))
ds_theta = ds_theta.swap_dims({"theta": "idx"})
return ds_theta

```

Right, it would return a 1D numpy or dask array.

I suppose I'm used to simply typing pd.Series().unique() rather than np.unique(pd.Series()).

I use it in for loops primarily. for season in da['time.season'].unique(): vs for season in np.unique(da['time.season'].data):

What would .unique() return on xarray.DataArray? For consistency with pandas, I guess it would return a 1D numpy or dask array?

I don't see a lot of value in adding this to xarray, given that all the xarray metadata gets lost by the unique() operation. You might as well just write np.unique(my_data_array.data).