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2177359290 PR_kwDOAMm_X85pJtWE 8817 Update documentation for clarity staadecker 13053829 closed 0     3 2024-03-09T19:03:19Z 2024-03-09T22:33:19Z 2024-03-09T22:33:16Z CONTRIBUTOR   0 pydata/xarray/pulls/8817

Closes #8794 

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     xarray 13221727 pull
2159674260 I_kwDOAMm_X86AugOU 8794 Difficult to convert `pd.DataFrame` to `Dataset` with multi-index staadecker 13053829 closed 0     2 2024-02-28T19:17:14Z 2024-03-09T22:33:17Z 2024-03-09T22:33:17Z CONTRIBUTOR      

What is your issue?

No direct way to create multi-index dataset

Unless I'm missing something, there is no easy way to convert a Pandas dataframe to a dataset with a multi-index. For example, xr.Dataset.from_dataframe automatically converts any Pandas multi-index into separate dimensions.

Workaround is inefficient

One workaround is doing xr.Dataset.from_dataframe(df).stack(...) however this is very inefficient for sparse multi-indices.

 
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   completed xarray 13221727 issue
2159328369 I_kwDOAMm_X86AtLxx 8793 `.fillna()` slower than expected for sparse data arrays with `fill_value=nan` staadecker 13053829 open 0     1 2024-02-28T16:14:10Z 2024-02-28T16:14:15Z   CONTRIBUTOR      

What is your issue?

Expected behavior

.fillna(0) should be near instantaneous when applied to a sparse DataArray with fill_value=nan

Why

.fillna(0) only needs to update the fill_value to 0.

Current behaviour

The normal .where() operation is applied on the DataArray instead of using the shortcut described above.

Question

What would be required to improve the performance of fillna()? I'm happy to try taking a stab at it if pointed in the right direction.

 
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     xarray 13221727 issue

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