issues

Generally this isn't a problem, since the coords are carried over by the resulting DataArrays:

``` python In [11]:

ds = xray.Dataset({ 'a':pd.DataFrame(pd.np.random.rand(10,3)), 'b':pd.Series(pd.np.random.rand(10)) }) ds.coords['c'] = pd.Series(pd.np.random.rand(10)) ds Out[11]: <xray.Dataset> Dimensions: (dim_0: 10, dim_1: 3) Coordinates: * dim_0 (dim_0) int64 0 1 2 3 4 5 6 7 8 9 * dim_1 (dim_1) int64 0 1 2 c (dim_0) float64 0.9318 0.2899 0.3853 0.6235 0.9436 0.7928 ... Data variables: a (dim_0, dim_1) float64 0.5707 0.9485 0.3541 0.5987 0.406 0.7992 ... b (dim_0) float64 0.4106 0.2316 0.5804 0.6393 0.5715 0.6463 ... In [12]:

ds.apply(lambda x: x*2) Out[12]: <xray.Dataset> Dimensions: (dim_0: 10, dim_1: 3) Coordinates: c (dim_0) float64 0.9318 0.2899 0.3853 0.6235 0.9436 0.7928 ... * dim_0 (dim_0) int64 0 1 2 3 4 5 6 7 8 9 * dim_1 (dim_1) int64 0 1 2 Data variables: a (dim_0, dim_1) float64 1.141 1.897 0.7081 1.197 0.812 1.598 ... b (dim_0) float64 0.8212 0.4631 1.161 1.279 1.143 1.293 0.3507 ... ```

But if there's an operation that removes the coords from the DataArrays, the coords are not there on the result (notice c below). Should the Dataset retain them? Either always or with a keep_coords argument, similar to keep_attrs.

``` python In [13]:

ds = xray.Dataset({ 'a':pd.DataFrame(pd.np.random.rand(10,3)), 'b':pd.Series(pd.np.random.rand(10)) }) ds.coords['c'] = pd.Series(pd.np.random.rand(10)) ds Out[13]: <xray.Dataset> Dimensions: (dim_0: 10, dim_1: 3) Coordinates: * dim_0 (dim_0) int64 0 1 2 3 4 5 6 7 8 9 * dim_1 (dim_1) int64 0 1 2 c (dim_0) float64 0.4121 0.2507 0.6326 0.4031 0.6169 0.441 0.1146 ... Data variables: a (dim_0, dim_1) float64 0.4813 0.2479 0.5158 0.2787 0.06672 ... b (dim_0) float64 0.2638 0.5788 0.6591 0.7174 0.3645 0.5655 ... In [14]:

ds.apply(lambda x: x.to_pandas()*2) Out[14]: <xray.Dataset> Dimensions: (dim_0: 10, dim_1: 3) Coordinates: * dim_0 (dim_0) int64 0 1 2 3 4 5 6 7 8 9 * dim_1 (dim_1) int64 0 1 2 Data variables: a (dim_0, dim_1) float64 0.9627 0.4957 1.032 0.5574 0.1334 0.8289 ... b (dim_0) float64 0.5275 1.158 1.318 1.435 0.7291 1.131 0.1903 ... ```

I've held this view in low confidence for a while and wanted to socialize it to see whether there's something to it: Should we keep attrs in operations by default?

Advantages: - I think most of the time people want to keep attrs after operations - Is that right? Are there cases where it wouldn't be a reasonable default? e.g. good points here for not always keeping coords around - It's easy to remove them with a (currently unimplemented) drop_attrs method when people do want to remove them

Disadvantages: - Backward incompatible change with an expensive deprecate cycle (would be impractical to have a deprecation warning every time someone ran a function on an object with attrs I think? At least without adding a once filter warning) - ?

Here are some existing relevant discussions: - https://github.com/pydata/xarray/issues/3815#issuecomment-603974527 - https://github.com/pydata/xarray/issues/688 - https://github.com/pydata/xarray/pull/2482 - https://github.com/pydata/xarray/issues/3304

I think this is an easy situation to get into: - We make an incorrect-but-insignificant design decision; e.g. some methods don't keep attrs - We want to change that, but avoid breaking backward-compatibility - So we add kwargs and eventually a global config - But now we have a global config that requires global context and lots of kwargs! :(

I'm up for leaning towards breaking changes if it makes the library better: I think xarray will grow immensely, and so the narrow immediate pain is worth the broader future positive impact. Clearly if the immediate pain stops xarray growing, then it's not a good tradeoff.