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/1844#issuecomment-441034802,https://api.github.com/repos/pydata/xarray/issues/1844,441034802,MDEyOklzc3VlQ29tbWVudDQ0MTAzNDgwMg==,33062222,2018-11-22T13:43:23Z,2018-11-22T13:44:48Z,NONE,"For anyone stumbling upon this thread in the future, I would like to mention that I used the above grouping approach suggested by @spencerkclark for my dataset to calculate climatology with calendar day and it works smoothly. The only thing one should be careful is that you can't directly plot the data using

 `In[1]: da.groupby(month_day_str).mean('time').plot()` 
`Out[1]: TypeError: Plotting requires coordinates to be numeric or dates of type np.datetime64 or datetime.datetime.`

To get around it, either use group by the 
> modified_ordinal _day 

Or convert back the grouped coordinate month_day_str to numeric. However, after doing all this I found out that the CDO function also calculates climatology by the ordinal day of the year. So, to be consistent I would stick to that method but it's anyway good to know that there is a way around to group by day and month if required in Xarray.","{""total_count"": 1, ""+1"": 1, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,290023410