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/1143#issuecomment-417337306,https://api.github.com/repos/pydata/xarray/issues/1143,417337306,MDEyOklzc3VlQ29tbWVudDQxNzMzNzMwNg==,12130884,2018-08-30T14:21:55Z,2018-08-30T14:21:55Z,NONE,"Pardon me for extending this discussion.

I encountered the same problem when calculating timedelta in a dataframe. It even ended with an error when I tried to call the days attribute. I am using Numpy 1.6.1

AttributeError: 'Series' object has no attribute 'days'

Problem

df_trans['DELTA'] = df_trans['DATE2'] - df_trans['DATE1']

print df_trans['DELTA'].dtype
>>timedelta64[ns]

print df_trans['DELTA']
>> 0    8 days, 00:00:00
>> 1   15 days, 00:00:00
>> 2    5 days, 00:00:00

df_trans['DELTA'] = df_trans['DELTA'].astype('timedelta64[D]')
print df_trans['DELTA'].dtype
>> Name: DELTA, dtype: timedelta64[D]

print df_trans['DELTA']
>> 0    8 days, 00:00:00
>> 1   15 days, 00:00:00
>> 2    5 days, 00:00:00
>>Nothing changed at all

print df_trans['DELTA'].days
>> AttributeError: 'Series' object has no attribute 'days'  

I get rid of the problem by putting it in to a list for the conversion.

                Ss_timedelta = df_trans['DATE2'] - df_trans['DATE1']
                ls_timedelta = Ss_timedelta.values.astype('timedelta64[D]').tolist()
                for i in range(0, len(ls_timedelta)):
                        ls_timedelta[i] = ls_timedelta[i].days / 1000                        
                df_trans['HOLDDAYS'] = pd.Series(ls_timedelta)
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,192325490
https://github.com/pydata/xarray/issues/1143#issuecomment-264133419,https://api.github.com/repos/pydata/xarray/issues/1143,264133419,MDEyOklzc3VlQ29tbWVudDI2NDEzMzQxOQ==,5629061,2016-12-01T10:15:21Z,2016-12-01T10:15:21Z,NONE,"The pandas docs do seem to say that conversion to timedelta64[D] (or other frequencies) is possible - see: http://pandas.pydata.org/pandas-docs/stable/timedeltas.html#frequency-conversion

Also here's a more realistic example of why this is problematic for me - I have a sequence of dates and I want to calculate the difference between them in days: possible in pandas, but not possible in xarray without first reverting to pandas/numpy types

```
dates = pandas.Series([datetime.date(2016, 01, 10), datetime.date(2016, 01, 20), datetime.date(2016, 01, 25)]).astype('datetime64[ns]')

dates.diff().astype('timedelta64[D]').astype(float)
#returns 
#0     NaN
#1    10.0
#2     5.0
#dtype: float6

xarray.DataArray(dates).diff(dim = 'dim_0').astype('timedelta64[D]').astype(float)
#returns
#<xarray.DataArray (dim_0: 2)>
#array([  8.64000000e+14,   4.32000000e+14])
#Coordinates:
#  * dim_0    (dim_0) int64 1 2
```

Again the xarray result is in ns rather than days.

Thanks","{""total_count"": 1, ""+1"": 1, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,192325490
https://github.com/pydata/xarray/issues/1143#issuecomment-263626446,https://api.github.com/repos/pydata/xarray/issues/1143,263626446,MDEyOklzc3VlQ29tbWVudDI2MzYyNjQ0Ng==,5629061,2016-11-29T16:47:25Z,2016-11-29T16:47:25Z,NONE,"The conversion to timedelta64[ns] is done on this line of code:
https://github.com/pydata/xarray/blob/d66f673ab25fe0fc0483bd5d67479fc94a14e46d/xarray/core/variable.py#L169

Is there a reason behind the conversion, or could it be removed?","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,192325490