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There's a longer discussion in #6377 so let's close this.

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If I were to make a PR, where would this method reside? Would it be under dataset.py and dataarray.py? Also, would I simply call np.select inside the method, and if so, how would I add support for dask?

My minimal example atm: ``` import xarray as xr import numpy as np import hvplot.xarray

ds = xr.tutorial.open_dataset('air_temperature').isel(time=0)

ds['air_cats'] = ( ('lat', 'lon'), np.select([ds['air'].values >= 273.15, ds['air'].values < 273.15], ['above freezing', 'below freezing']) ) ds.hvplot('lon', 'lat', hover_cols=['air_cats']) ```

No, not from me at least.

Has any progress been made on adding a builtin function for this? Thanks.

Agree that map is not a good name (and I find the pandas API difficult in this area - each time I'm looking up what relabel / rename / map / select / filter does)

How about match / switch / case?

We would definitely use this. I agree it'd probably be used less in xarray than in pandas; though I'm keen to expand the API, in a deliberate and careful way, to some of the traditional pandas use-cases (but a small vote among many)

I would lean slightly against adding a dedicated method for this (but could be convinced if others are interested). Usually we copy pandas or numpy APIs, but DataFrame.map is a not a great name -- map means too many other things (e.g., consider the buildin map).

It might make sense to copy the design of numpy.select instead: https://docs.scipy.org/doc/numpy/reference/generated/numpy.select.html

e.g., you could write something like xarray.select([ds < 0, ds < 10], [5, 10], default=15)

Thanks for the quick replies! Is there interest in making this a built-in function? If so, I can help contribute a PR.

Also wondering about a way to wrap logic to that mapping.

Like below 0, replace with -1, between 0 and 10, replace with 5, and above 10, replace with 15 which is possible with three np.place statements I think, but have to think in backwards logic with ds.where().

I would divide this into two steps: (1) write a function that does this on NumPy arrays and (2) apply it to xarray objects using apply_ufunc, e.g.,

```python import numpy as np import xarray as xr

def remap(array, mapping): return np.array([mapping[k] for k in array.ravel()]).reshape(array.shape)

ds = xr.Dataset({'test': ('t', [0, 1, 2])}) xr.apply_ufunc(remap, ds, kwargs=dict(mapping={0: 50, 1: 29, 2: 10})) outputs: <xarray.Dataset> Dimensions: (t: 3) Dimensions without coordinates: t Data variables: test (t) int64 50 29 10 ```

I guess I'm thinking about more complex cases such as changing 0 -> 50, 1 -> 29, 2 -> 10

ds = xr.Dataset({'test': [0, 1, 2]}) ds.where((ds != 1) & (ds != 2), 50)

Thoughts on simplifying this?

The usual way to do this in xarray would be to use where(), e.g., xarray.where(ds == 0, 1, ds) or ds.where(ds == 0, 1).