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  • DavidTsangHW · 1 ✖

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  • timedelta64[D] is always coerced to timedelta64[ns] · 1 ✖

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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
417337306 https://github.com/pydata/xarray/issues/1143#issuecomment-417337306 https://api.github.com/repos/pydata/xarray/issues/1143 MDEyOklzc3VlQ29tbWVudDQxNzMzNzMwNg== DavidTsangHW 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)

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   timedelta64[D] is always coerced to timedelta64[ns] 192325490

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