issue_comments

I agree that at least the index should show the reflect the order of the ranking found using argsort.

```python

import xarray as xr import numpy as np

np.random.rand(10)

ds = xr.DataArray(np.random.rand(10) )

ds.argsort().dim_0 ```

returns array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])

however it should return an array with the order of the elements ranked using argsort e.g.

array([3, 8, 5, 1, 7, 2, 9, 4, 6, 0])

Where ds[3] is the smallest value in ds and ds[0] is the biggest etc.

The current behaviour is confusing / nonsenical. Return the index unchanged implies that the initial array is already ordered.

```

INSTALLED VERSIONS

commit: None python: 3.10.9 | packaged by conda-forge | (main, Feb 2 2023, 20:20:04) [GCC 11.3.0] python-bits: 64 OS: Linux OS-release: 4.18.0-425.3.1.el8.nci.x86_64 machine: x86_64 processor: x86_64 byteorder: little LC_ALL: None LANG: None LOCALE: ('en_US', 'UTF-8') libhdf5: 1.12.2 libnetcdf: 4.8.1

xarray: 2023.2.0 pandas: 1.5.3 numpy: 1.23.5 scipy: 1.9.3 netCDF4: 1.6.0 pydap: None h5netcdf: None h5py: None Nio: None zarr: None cftime: 1.6.2 nc_time_axis: None PseudoNetCDF: None rasterio: 1.3.4 cfgrib: None iris: 3.3.1 bottleneck: None dask: 2022.11.0 distributed: 2022.11.0 matplotlib: 3.4.3 cartopy: 0.21.1 seaborn: None numbagg: None fsspec: 2022.11.0 cupy: None pint: None sparse: 0.13.0 flox: None numpy_groupies: None setuptools: 65.5.1 pip: 23.0.1 conda: None pytest: None mypy: None IPython: 8.6.0 sphinx: None

```

I think this issue is still relevant.

In order to maintain a list of currently relevant issues, we mark issues as stale after a period of inactivity

If this issue remains relevant, please comment here or remove the stale label; otherwise it will be marked as closed automatically

I'm not a fan of auto-flattening either, but that's what nd.argsort() does...

One option is to have DataArray.arg{min,max,sort}() all take an optional flag argument specifying whether to return integer indices or index labels. But I think my preference would be be to have six separate functions: DataArray.{idx,}arg{min,max,sort}() (or some such nomenclature that includes arg in all six functions).

Note that ndarray.argsort(axis=None) sorts the flattened array, so the returned DataArray should respect this

I'm not a huge fan of auto-flattening for xarray, but I can see this logic.

Alternative suggestion: have DataArray.argsort() return an ndarray filled with labels from the sorted dimension

I would probably implement a new method for this, maybe idxsort for symmetry with idxmax (https://github.com/pydata/xarray/issues/60). Though that name could be read several different ways. Maybe sortby_labels()?

I think that makes sense, though I don't quite understand what would go in its place. Another possibility -- perhaps a bad one -- is to permute the values in the sorted dimension so that they match the resulting values (i.e. something like result.coords[dim] = np.take(da.coords[dim].values, result.values, axis=axis)).

Note that ndarray.argsort(axis=None) sorts the flattened array, so the returned DataArray should respect this

Alternative suggestion: have DataArray.argsort() return an ndarray filled with labels from the sorted dimension, i.e. something like: class DataArray: def argsort(self, **kwargs): # TODO: update kwargs['axis'] based 'axis' and 'dim', and remove 'dim' if kwargs['axis'] is None: kwargs['axis'] = -1 return self.stack(dim=self.dims).argsort(**kwargs) return np.take(self.coords[self.dims[kwargs['axis']].values, self.values.argsort(**kwargs))

BTW, I'm just thinking in terms of ndarrays. Someone more knowledgeable than me may want to consider how to make it work intelligently with dask.

It seems like another reasonable choice would be for DataArray.argsort() to keep its current value but drop coordinate labels along the sorting dimension. This would give a consistent result for use with broadcasting indexing (https://github.com/pydata/xarray/pull/1473).

I think this internal utility function is equivalent to your second argsort: https://github.com/pydata/xarray/blob/2949558b75a65404a500a237ec54834fd6946d07/xarray/core/nputils.py#L38-L55

In practice, we might use bottleneck.rankdata() or nanrankdata(): https://kwgoodman.github.io/bottleneck-doc/reference.html#non-reduce-with-axis