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/5815#issuecomment-926882280,https://api.github.com/repos/pydata/xarray/issues/5815,926882280,IC_kwDOAMm_X843Pxno,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.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,1006413760
https://github.com/pydata/xarray/issues/5815#issuecomment-926877228,https://api.github.com/repos/pydata/xarray/issues/5815,926877228,IC_kwDOAMm_X843PwYs,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`.
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,1006413760
https://github.com/pydata/xarray/issues/5815#issuecomment-926855056,https://api.github.com/repos/pydata/xarray/issues/5815,926855056,IC_kwDOAMm_X843Pq-Q,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.","{""total_count"": 1, ""+1"": 1, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,1006413760