html_url,issue_url,id,node_id,user,created_at,updated_at,author_association,body,reactions,performed_via_github_app,issue
https://github.com/pydata/xarray/issues/1388#issuecomment-656361865,https://api.github.com/repos/pydata/xarray/issues/1388,656361865,MDEyOklzc3VlQ29tbWVudDY1NjM2MTg2NQ==,2448579,2020-07-09T21:32:52Z,2020-07-09T21:32:52Z,MEMBER,I think this is fixed now thanks to @johnomotani ,"{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,224878728
https://github.com/pydata/xarray/issues/1388#issuecomment-362951129,https://api.github.com/repos/pydata/xarray/issues/1388,362951129,MDEyOklzc3VlQ29tbWVudDM2Mjk1MTEyOQ==,1217238,2018-02-04T23:54:10Z,2018-02-04T23:54:10Z,MEMBER,"I think it would be fine to add a special case to preserve coordinates corresponding to min/max values with xarray's `min()` and `max()` methods, but I don't feel strongly about this. The exact coordinates could be surprising if there are multiple min/max values.

I agree that it does not make sense to preserve coordinates along aggregated dimensions for argmin/argmax, but we can preserve other coordinates. So I like @fujiisoup's example behavior above.

I suppose we now have two candidate APIs for returning multiple indices from a method like argmin/argmax:
1. Add an additional dimension, e.g., `argmaxdim` for keeping track of multiple indices.
2. Return a dict or Dataset with multiple indices.

I think my favorite option is (2) with `da.argmin_indices()` returning a Dataset, which will allow `da[da.argmin_indices()]` to work after we finish switching `__iter__` to only iterate over data variables (https://github.com/pydata/xarray/issues/884#issuecomment-338445322). One downside of this approach is that it is not obvious how we woudl define `argmin_indices()` to work on a Dataset, but that's probably OK given that you cannot use a Dataset (yet) for indexing.

My concern with adding an additional dimension is that it is always a little surprising and error-prone when we invent new dimension names not supplied by the user (for example, this can lead to conflicting names). Also, consolidating indices will not work as well with `idxmin()`, which might put indices of different dtypes in the same array.

Either way, I would like a separate dedicated method for returning multiple indexing arrays. It's convenient (and what users expect) for argmax to return a single array if taking the max only over one dimension. However, if we switch to add an `argmaxdim` or return a dict/Dataset for multiple dimensions, then we will end up with an annoying inconsistency between the 1D and N-D versions. It would be better to say `argmax(dim)` is only for one dimension (and raise an error if this is not true) and have the separate `argmax_indices(dims)` that is consistently defined for any number of dimensions.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,224878728
https://github.com/pydata/xarray/issues/1388#issuecomment-362902669,https://api.github.com/repos/pydata/xarray/issues/1388,362902669,MDEyOklzc3VlQ29tbWVudDM2MjkwMjY2OQ==,6815844,2018-02-04T12:20:33Z,2018-02-04T12:52:29Z,MEMBER,"@gajomi 

Sorry for my late response and thank you for the proposal.

But aside from my previous proposal, I was thinking whether such aggregation methods (including `argmin`) should propagate the coordinate.
For example, as you pointed out, in theory, we may be able to track `x`-coordinate at the argmin index after `da.argmin(dim='x')`.
But it is not reasonable for `da.mean(dim='x')`.
It may be reasonable for `da.max(dim='x')` but not for `da.median(dim='x')`.

Such specific rules may be confusing and bring additional complexity.
I think the rule 
**we do not track coordinates after aggregations**
would be much simpler and easier to understand.

If we adopt the above rule, I think the `argmin` would give just an array of indices,
```python
In [1]: import xarray as xr
   ...: da = xr.DataArray([[0, 3, 2], [2, 1, 4]], dims=['x', 'y'],
   ...:                   coords={'x': [1, 2], 'y': ['a', 'b', 'c']})
   ...:

In [4]: da.argmin(dim='x')
Out[4]: 
<xarray.DataArray (y: 3)>
array([0, 1, 0])
Coordinates:
  * y        (y) <U1 'a' 'b' 'c'

In [3]: da.isel(x=da.argmin(dim='x'))
Out[3]: 
<xarray.DataArray (y: 3)>
array([0, 1, 2])
Coordinates:
    x        (y) int64 1 2 1
  * y        (y) <U1 'a' 'b' 'c'

```

I think your logic would be useful even we do not track the coordinate.

I would appreciate any feedback.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,224878728
https://github.com/pydata/xarray/issues/1388#issuecomment-338397633,https://api.github.com/repos/pydata/xarray/issues/1388,338397633,MDEyOklzc3VlQ29tbWVudDMzODM5NzYzMw==,6815844,2017-10-21T13:51:04Z,2017-10-21T13:51:04Z,MEMBER,"I am thinking again how `argmin` should work with our new vectorizing indexing #1639 .
It would be great if `arr.isel(**arr.argmin(dim)) == arr.min(dim)` could be satisfied even with a multi-dimensional array, although the behavior is different from `numpy.argmin`.
(Maybe our current `min` should be replaced by `arr.isel(**arr.argmin(dim))` so that it preserves the coordinates.)

(We discussed the name for this new method in #1469 but here I just use `argmin` for the simplicity.)

For example with a three dimensional array with `dims=['x', 'y', 'z']`, such as 
`arr = xr.DataArray(np.random.randn(4, 3, 2), dims=['x', 'y', 'z'])`
I am thinking that...
+ `arr.argmin()` would return a `xr.Dataset` which contains 'x', 'y', 'z' as its `data_vars`.
  1. `ds = arr.argmin(dims=None)` case:
     - `ds['x']`, `ds['y']`, `ds['z']` would be 0d-integers.
  2. `ds = arr.argmin(dims=['x', 'y'])` case:
     - `ds['x']`, `ds['y']`, `ds['z']` would be 1d-integer-arrays. 
     - The dimension of these three arrays would be 'z_argmin', where `ds['z_argmin'] == arr['z']`.
  3. `ds = arr.argmin(dims='x')` case:
     - `ds['x']`, `ds['y']`, `ds['z']` would be 2d-integer-arrays. 
     - The dimensions of these three arrays are 'y_argmin' and 'z_argmin', where `ds['y_argmin'] == arr['y']` and `ds['z_argmin'] == arr['z']`.

The above proposal for ii (and iii) is not quite clean, as if it is used as an argument of `isel`, it appends a new coordinate 'z_argmin', which is just a duplicate of 'arr['z']', i.e.
`arr.isel(**arr.argmin(dims=['x', 'y']))['z_argmin'] == arr['z']`.

Any thoughts are welcome.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,224878728
https://github.com/pydata/xarray/issues/1388#issuecomment-309280411,https://api.github.com/repos/pydata/xarray/issues/1388,309280411,MDEyOklzc3VlQ29tbWVudDMwOTI4MDQxMQ==,6815844,2017-06-18T14:18:40Z,2017-06-19T12:56:03Z,MEMBER,"I'm working to fix this and I would like to make some design decisions;

1. What should `max()` look like?
I guess this method should work also for multi-dimensional data.
To satisfy the `arr.isel_points(**arr.argmin_indices(dim)) == arg.min(dim)` relation,
the result array should have proper coordinates?

2. Multiple `dim` arguments
Currently, __doc__ says `argmin` accepts multiple axes, but `np.argmin` does not.
Can we limit `argmin`'s arguments only `str` not sequence of `str`s?

Edit:

3. Multi-dimensional array to `isel_points`
Currently, `isel_points` only accepts 1-dimensional array, while the result of `argmin_indexes` can be multi-dimensional, e.g.
```python
xr.DataArray(np.random.randn(4, 3, 2), dims=['x', 'y', 'z']).argmin_indexes(dims=['x'])
```
Do we need special treatment for this (maybe in `isel_points`) or just raise an Error (current behavior)?","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,224878728
https://github.com/pydata/xarray/issues/1388#issuecomment-298256260,https://api.github.com/repos/pydata/xarray/issues/1388,298256260,MDEyOklzc3VlQ29tbWVudDI5ODI1NjI2MA==,1217238,2017-04-30T20:50:49Z,2017-04-30T20:50:49Z,MEMBER,"I agree that `arr[arr.argmin(dim)] == arr.min(dim)` is a useful invariant, but currently xarray's indexing works a little differently from NumPy. Probably `arr.isel_points(**arr.argmin(dim)) == arg.min(dim)` is the better invariant for now, and in the future `arr[arr.argmin(dim)]` will change to work consistently (#974).

The main downside of returning a tuple or dict from `argmin()` is that it makes the common case of taking the max/min over one dimension a little harder. So possibly it would be better to write *two methods*:

- `argmin` would work like it does currently (returning an xarray object), but error if reducing over multiple dimensions.
- `argmin_indices` would return a dict suitable for use in indexing.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,224878728
https://github.com/pydata/xarray/issues/1388#issuecomment-297845435,https://api.github.com/repos/pydata/xarray/issues/1388,297845435,MDEyOklzc3VlQ29tbWVudDI5Nzg0NTQzNQ==,1217238,2017-04-27T21:33:06Z,2017-04-27T21:33:06Z,MEMBER,"Agreed, the current implementation of `argmin()` only gives the correct result when given one dimension. It's not entirely obvious what `argmin()` should yield when done to multiple dimensions, certainly this is not very useful. I would prefer raising an error to this current behavior.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,224878728