Convert NumPy object array to datetime64

Question

data['RealTime'][:,0]
Out[23]: 
array([datetime.datetime(2017, 9, 12, 18, 13, 8, 826000),
       datetime.datetime(2017, 9, 12, 18, 13, 8, 846000),
       datetime.datetime(2017, 9, 12, 18, 13, 8, 866000), ...,
       datetime.datetime(2017, 9, 12, 18, 30, 40, 186000),
       datetime.datetime(2017, 9, 12, 18, 30, 40, 206000),
       datetime.datetime(2017, 9, 12, 18, 30, 40, 226000)], dtype=object)

how can I convert into an array of dtype datetime?

Is this Pandas or Numpy?

Martijn Pieters
– Martijn Pieters

2017-09-19 07:54:50 +00:00
Commented Sep 19, 2017 at 7:54 — Martijn Pieters
– Martijn Pieters, Commented Sep 19, 2017 at 7:54

cs95 · Accepted Answer · 2017-09-19 07:56:13Z

3

I know you have pandas, so you can just use pd.to_datetime:

out = pd.to_datetime(array)
print(out)

DatetimeIndex(['2017-09-12 18:13:08.826000', '2017-09-12 18:13:08.846000',
               '2017-09-12 18:13:08.866000', '2017-09-12 18:30:40.186000',
               '2017-09-12 18:30:40.206000', '2017-09-12 18:30:40.226000'],
              dtype='datetime64[ns]', freq=None)

You can retrieve a numpy array from out by accessing out.values.

With numpy, you'd do the same thing using astype:

out = array.astype("datetime64[ns]")
print(out)

array(['2017-09-12T18:13:08.826000000', '2017-09-12T18:13:08.846000000',
       '2017-09-12T18:13:08.866000000', '2017-09-12T18:30:40.186000000',
       '2017-09-12T18:30:40.206000000', '2017-09-12T18:30:40.226000000'], dtype='datetime64[ns]')

answered Sep 19, 2017 at 7:56

cs95

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