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  • expand dimension by re-allocating larger arrays with more space "at the end of the corresponding dimension", block copying previously existing data, and autofill newly created entry by a default value (note: alternative to reindex, but much faster for extending large arrays along, for example, the time dimension) · 3 ✖

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  • COLLABORATOR · 3 ✖
id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
1340952624 https://github.com/pydata/xarray/issues/7363#issuecomment-1340952624 https://api.github.com/repos/pydata/xarray/issues/7363 IC_kwDOAMm_X85P7VAw headtr1ck 43316012 2022-12-07T13:15:38Z 2022-12-07T13:15:38Z COLLABORATOR

No worries :)

 
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   expand dimension by re-allocating larger arrays with more space "at the end of the corresponding dimension", block copying previously existing data, and autofill newly created entry by a default value (note: alternative to reindex, but much faster for extending large arrays along, for example, the time dimension) 1479121713
1340942358 https://github.com/pydata/xarray/issues/7363#issuecomment-1340942358 https://api.github.com/repos/pydata/xarray/issues/7363 IC_kwDOAMm_X85P7SgW headtr1ck 43316012 2022-12-07T13:08:54Z 2022-12-07T13:08:54Z COLLABORATOR

You all totally ignored my comment, haha. But glad that this solution works :)

 
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   expand dimension by re-allocating larger arrays with more space "at the end of the corresponding dimension", block copying previously existing data, and autofill newly created entry by a default value (note: alternative to reindex, but much faster for extending large arrays along, for example, the time dimension) 1479121713
1339717048 https://github.com/pydata/xarray/issues/7363#issuecomment-1339717048 https://api.github.com/repos/pydata/xarray/issues/7363 IC_kwDOAMm_X85P2nW4 headtr1ck 43316012 2022-12-06T17:21:20Z 2022-12-06T17:21:20Z COLLABORATOR

Maybe pad could also work? Something like da.pad(x=(0, 1), constant_values=np.nan) and then overwriting the nans da[{"x": -1}] = 1 with new values.

I'm actually not sure if you can use the new values directly in the constant_values argument, cannot try it now though.

 
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   expand dimension by re-allocating larger arrays with more space "at the end of the corresponding dimension", block copying previously existing data, and autofill newly created entry by a default value (note: alternative to reindex, but much faster for extending large arrays along, for example, the time dimension) 1479121713

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