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/3245#issuecomment-662783599,https://api.github.com/repos/pydata/xarray/issues/3245,662783599,MDEyOklzc3VlQ29tbWVudDY2Mjc4MzU5OQ==,1217238,2020-07-23T01:58:00Z,2020-07-23T01:58:00Z,MEMBER,"Agreed! Sparse also needs a special case.

On Wed, Jul 22, 2020 at 6:35 PM Deepak Cherian <notifications@github.com>
wrote:

> IMO a good first pass for as_numpy would be to use np.asarray with a
> special case for cupy where it would use .get.
>
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","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,484240082
https://github.com/pydata/xarray/issues/3245#issuecomment-662583820,https://api.github.com/repos/pydata/xarray/issues/3245,662583820,MDEyOklzc3VlQ29tbWVudDY2MjU4MzgyMA==,1217238,2020-07-22T17:26:59Z,2020-07-22T17:26:59Z,MEMBER,"> 3\. for now we are OK with special `as_sparse`, `as_cupy`, `as_pint` methods in xarray as long as all that logic is contained in one place: `as_duck_array.py`?

+1 for `as_numpy()`, too

One question about `as_numpy`: should it convert `pint` arrays into NumPy by stripping units? Or should it convert the arrays underlying pint and keep the units? I guess the first would probably make more sense for `DataArray.as_numpy()`. The second behavior could be achieved with `DataArray.pint.as_numpy()`.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,484240082
https://github.com/pydata/xarray/issues/3245#issuecomment-555590777,https://api.github.com/repos/pydata/xarray/issues/3245,555590777,MDEyOklzc3VlQ29tbWVudDU1NTU5MDc3Nw==,1217238,2019-11-19T16:30:18Z,2019-11-19T16:30:18Z,MEMBER,"> Also isn't the function `astype` not `as_type`?

Yes, but I think that's mostly works because it's so short. In general the rule is to use underscores when it improves clarity. `assparse` could read in at least two different ways, and one of which is not really appropriate.","{""total_count"": 3, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 3}",,484240082
https://github.com/pydata/xarray/issues/3245#issuecomment-555588678,https://api.github.com/repos/pydata/xarray/issues/3245,555588678,MDEyOklzc3VlQ29tbWVudDU1NTU4ODY3OA==,1217238,2019-11-19T16:25:55Z,2019-11-19T16:25:55Z,MEMBER,"I like `as_sparse` and `as_dense`, because it makes clear that the objects are still xarray objects. I agree with @fujiisoup that `to_sparse``todense` could be confused to return sparse arrays directly.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,484240082
https://github.com/pydata/xarray/issues/3245#issuecomment-524488632,https://api.github.com/repos/pydata/xarray/issues/3245,524488632,MDEyOklzc3VlQ29tbWVudDUyNDQ4ODYzMg==,1217238,2019-08-23T23:08:28Z,2019-08-23T23:08:28Z,MEMBER,"Xarray's .data is basically the equivalent of pandas's .array.

On Fri, Aug 23, 2019 at 2:44 PM Maximilian Roos <notifications@github.com>
wrote:

> I do think the pandas approach is pretty good here - don't break anything
> relying on .values - instead have .array to get the backing array (in
> whatever form it might be), and to_numpy() coercing to a raw numpy array
>
> ref
> https://pandas.pydata.org/pandas-docs/stable/whatsnew/v0.24.0.html#accessing-the-values-in-a-series-or-index
>
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","{""total_count"": 2, ""+1"": 2, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,484240082
https://github.com/pydata/xarray/issues/3245#issuecomment-524410610,https://api.github.com/repos/pydata/xarray/issues/3245,524410610,MDEyOklzc3VlQ29tbWVudDUyNDQxMDYxMA==,1217238,2019-08-23T18:09:25Z,2019-08-23T18:09:25Z,MEMBER,"The main downside of  `to_numpy()` is that it isn't obvious whether the result should be a DataArray containing a NumPy array or a raw NumPy array. In particular, pandas uses `to_numpy()` for converting into a raw NumPy, which is the opposite of our intent here.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,484240082
https://github.com/pydata/xarray/issues/3245#issuecomment-524407909,https://api.github.com/repos/pydata/xarray/issues/3245,524407909,MDEyOklzc3VlQ29tbWVudDUyNDQwNzkwOQ==,1217238,2019-08-23T18:00:45Z,2019-08-23T18:00:45Z,MEMBER,"> * Should we formalize the convention that `.values` always return a numpy array i.e. it will call `todense()` implicitly and that `.data` will always return the underlying container: sparse/dask etc.?

I think `.values` should either return a NumPy array or raise an exception, based upon whether or not the underlying duck array supports coercion with `np.array()`.

We should have a separate API (maybe `.to_dense()` or `.to_numpy_data()`?) for explicitly converting into NumPy arrays. This should not be called automatically inside xarray.

Basically, we should leave the  decision about whether automatic coercion is safe up to the authors of duck array libraries.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,484240082
https://github.com/pydata/xarray/issues/3245#issuecomment-524125410,https://api.github.com/repos/pydata/xarray/issues/3245,524125410,MDEyOklzc3VlQ29tbWVudDUyNDEyNTQxMA==,1217238,2019-08-23T00:19:15Z,2019-08-23T00:19:15Z,MEMBER,I think it is intentional for automatic coercion to NumPy arrays to fail. Making `plot()` and `.values` work should probably require explicitly writing `datarray.to_dense()` first.,"{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,484240082