issue_comments: 521221473

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https://github.com/pydata/xarray/issues/3213#issuecomment-521221473 https://api.github.com/repos/pydata/xarray/issues/3213 521221473 MDEyOklzc3VlQ29tbWVudDUyMTIyMTQ3Mw== 6213168 2019-08-14T12:15:39Z 2019-08-14T12:20:59Z MEMBER

+1 for the introduction of to_sparse() / to_dense(), but let's please avoid the mistakes that were done with chunk(). DataArray.chunk() is extremely frustrating when you have non-index coords and, 9 times out of 10, you only want to chunk the data and you have to go through the horrid python a = DataArray(a.data.chunk(), dims=a.dims, coords=a.coords, attrs=a.attrs, name=a.name) Exactly the same issue would apply to to_sparse().

Possibly we could define them as ```python class DataArray: def to_sparse( self, data: bool = True, coords: Union[Iterable[Hashable], bool] = False )

class Dataset: def to_sparse( self, data_vars: Union[Iterable[Hashable], bool] = True, coords: Union[Iterable[Hashable], bool] = False ) ``` same for to_dense() and chunk() (the latter would require a DeprecationWarning for a few release before switching the default for coords from True to False - only to be triggered in presence of dask-backed coords).

 
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