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  • kripnerl · 1 ✖

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  • Add "unique()" method, mimicking pandas · 1 ✖

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  • NONE · 1 ✖
id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
709991825 https://github.com/pydata/xarray/issues/2795#issuecomment-709991825 https://api.github.com/repos/pydata/xarray/issues/2795 MDEyOklzc3VlQ29tbWVudDcwOTk5MTgyNQ== kripnerl 38673295 2020-10-16T11:34:05Z 2020-10-16T11:34:05Z NONE

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

```

 
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   Add "unique()" method, mimicking pandas 415774106

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