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  • byersiiasa · 1 ✖

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  • Time Dimension, Big problem with methods 'groupby' and 'to_netcdf' · 1 ✖

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  • NONE · 1 ✖
id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
315782686 https://github.com/pydata/xarray/issues/1480#issuecomment-315782686 https://api.github.com/repos/pydata/xarray/issues/1480 MDEyOklzc3VlQ29tbWVudDMxNTc4MjY4Ng== byersiiasa 17701232 2017-07-17T15:04:56Z 2017-07-17T15:04:56Z NONE

As far as I know I can imagine this is the intended functionality.

The examples given in the documentation seems to have a different behaviour. That is, the timestamps are retained and the first date of each month is used.

I cannot find where this is the case, apart from when using .resample. Could you put a link to the doc page?

The issue is perhaps more with the example that you present (of only 1 year data) and expected behaviour. Normally groupby('time.month') would be applied to multiple years of data. i.e. group data by month and find the monthly averages for Jan-Dec for 30 years of data, e.g. a climatology.

And so in this case it absolutely makes sense to keep the months as 1 to 12, or something similar (perhaps 'Jan','Feb'etc). Applying a datestring of the first day of the month wouldn't make sense because which year would you choose when you have 30 years of data?

If you do want a time series of monthly means, then .resample is the function you want and it will give you the datestamps in the format that you desire.

 
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   Time Dimension, Big problem with methods 'groupby' and 'to_netcdf' 243270042

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