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  • benbovy · 3 ✖

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  • Inconsistent dropping of `DatetimeIndex.freq` attribute from `DataArray`s index. · 3 ✖

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id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
926882280 https://github.com/pydata/xarray/issues/5815#issuecomment-926882280 https://api.github.com/repos/pydata/xarray/issues/5815 IC_kwDOAMm_X843Pxno benbovy 4160723 2021-09-24T19:51:29Z 2021-09-24T19:51:29Z MEMBER

In your example it is safer to set freq when creating time_index.

Hmm time_index.freq seem to be ignored when passing the index to the DataArray constructor. We should probably fix that.

 
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   Inconsistent dropping of `DatetimeIndex.freq` attribute from `DataArray`s index. 1006413760
926877228 https://github.com/pydata/xarray/issues/5815#issuecomment-926877228 https://api.github.com/repos/pydata/xarray/issues/5815 IC_kwDOAMm_X843PwYs benbovy 4160723 2021-09-24T19:41:46Z 2021-09-24T19:41:46Z MEMBER

Beware when you set index attributes in place like xa_copy.get_index('time').freq = freq. Currently, Xarray internals are such that depending on how you access the index it may return distinct (although intially identical) objects, e.g.,

```python

xa_copy.get_index('time').freq = freq xa_copy._indexes['time'].index # freq set here DatetimeIndex(['2013-01-01', '2013-02-01', '2013-03-01', '2013-04-01', '2013-05-01', '2013-06-01', '2013-07-01', '2013-08-01', '2013-09-01', '2013-10-01'], dtype='datetime64[ns]', name='time', freq='MS') xa_copy._coords['time']._data.array # freq not set here DatetimeIndex(['2013-01-01', '2013-02-01', '2013-03-01', '2013-04-01', '2013-05-01', '2013-06-01', '2013-07-01', '2013-08-01', '2013-09-01', '2013-10-01'], dtype='datetime64[ns]', name='time', freq=None) xa_copy._coords['time']._data.array is xa_copy._indexes['time'].index False ```

Hopefully, this will become less likely after the ongoing explicit index refactor in Xarray. But in general I would not advise changing in-place pandas indexes that are already bound to xarray objects. In your example it is safer to set freq when creating time_index.

 
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   Inconsistent dropping of `DatetimeIndex.freq` attribute from `DataArray`s index. 1006413760
926855056 https://github.com/pydata/xarray/issues/5815#issuecomment-926855056 https://api.github.com/repos/pydata/xarray/issues/5815 IC_kwDOAMm_X843Pq-Q benbovy 4160723 2021-09-24T19:00:36Z 2021-09-24T19:00:36Z MEMBER

This is the behavior that we get when directly indexing the pandas.DatetimeIndex:

```python

idx = xa_orig.get_index('time') idx DatetimeIndex(['2013-01-01', '2013-02-01', '2013-03-01', '2013-04-01', '2013-05-01', '2013-06-01', '2013-07-01', '2013-08-01', '2013-09-01', '2013-10-01'], dtype='datetime64[ns]', name='time', freq='MS') idx[0:2] # slice of ints -> preserve freq DatetimeIndex(['2013-01-01', '2013-02-01'], dtype='datetime64[ns]', name='time', freq='MS') idx[[0, 1]] # list of ints -> drop freq DatetimeIndex(['2013-01-01', '2013-02-01'], dtype='datetime64[ns]', name='time', freq=None) ```

I think that this behavior makes sense since indexing the index with an arbitrary list of integer positions may not yield labels with a meaningful, unique frequency. With a slice it can be updated easily (pandas does it), e.g.,

```python

idx[0:2:4] DatetimeIndex(['2013-01-01'], dtype='datetime64[ns]', name='time', freq='4MS') ```

Not sure what's happening with the copy of the DataArray, though.

 
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   Inconsistent dropping of `DatetimeIndex.freq` attribute from `DataArray`s index. 1006413760

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