Converting an array datatime.datetime to float

Question

Is it possible to convert something like this;

array([datetime.datetime(2014, 2, 1, 0, 0, 0, 100000),
       datetime.datetime(2014, 2, 1, 0, 0, 0, 300000),
       datetime.datetime(2014, 2, 1, 0, 0, 0, 500000), ...,
       datetime.datetime(2014, 2, 1, 19, 30, 0, 500000),
       datetime.datetime(2014, 2, 1, 19, 30, 0, 700000),
       datetime.datetime(2014, 2, 1, 19, 30, 0, 900000)], dtype=object)

to this:

array([  1.39277301e+09,   1.39277301e+09,   1.39277301e+09, ...,
         1.39285442e+09,   1.39285442e+09,   1.39285442e+09])

I would basically like to convert the datetime.datetime into a timestamp, with dtype = float.

What's the rule or mapping you want to use for converting datetimes to floats? i.e. why should datetime.datetime(2014, 2, 1, 0, 0, 0, 100000) become 1.39277301e+09? — Max Power
– Max Power, Commented Apr 25, 2017 at 5:01

Stephen Rauch · Accepted Answer · 2017-04-25 05:03:17Z

5

The best way to get a timestamp is by subtracting epoch from your datetime as:

Code:

import datetime as dt

times = np.array([
    dt.datetime(2014, 2, 1, 0, 0, 0, 100000),
    dt.datetime(2014, 2, 1, 0, 0, 0, 300000),
    dt.datetime(2014, 2, 1, 0, 0, 0, 500000),
])

# get a datetime that is equal to epoch
epoch = dt.datetime(1970, 1, 1)

for t in [(d - epoch).total_seconds() for d in times]:
    print('%.6f' % t)

Results:

1391212800.100000
1391212800.300000
1391212800.500000

answered Apr 25, 2017 at 5:03

Stephen Rauch♦

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Comments

Burhan Khalid · Accepted Answer · 2017-04-25 05:02:56Z

1

How about something like this:

(Python 2.x):

import datetime

temp_array = numpy.array([(i - datetime.datetime(1970, 1, 1)).total_seconds() for i in old_array])
new_array = temp_array.astype('float')

For Python 3.x:

import datetime

temp_array = numpy.array([(i - datetime.datetime(1970, 1, 1)) / datetime.timedelta(seconds=1) for i in old_array])
new_array = temp_array.astype('float')

answered Apr 25, 2017 at 5:02

Burhan Khalid

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dogoncouch · Accepted Answer · 2017-04-25 05:13:41Z

1

mktime() will convert into a timestamp, but it seems to lose accuracy beyond seconds.

>>> import datetime
>>> from time import mktime

>>> x = datetime.datetime.now()
>>> y = mktime(x.timetuple())
>>> print(y)
1493096455.0

>>> type(y)
<type 'float'>

>>> z = y/1000000000
>>> print(z)
1.493096455

answered Apr 25, 2017 at 5:13

dogoncouch

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Comments

hpaulj · Accepted Answer · 2017-04-25 05:23:36Z

In [475]: x
Out[475]: 
array([datetime.datetime(2014, 2, 1, 0, 0, 0, 100000),
       datetime.datetime(2014, 2, 1, 0, 0, 0, 300000),
       datetime.datetime(2014, 2, 1, 0, 0, 0, 500000),
       datetime.datetime(2014, 2, 1, 19, 30, 0, 500000),
       datetime.datetime(2014, 2, 1, 19, 30, 0, 700000),
       datetime.datetime(2014, 2, 1, 19, 30, 0, 900000)], dtype=object)
In [476]: x.astype('datetime64[us]')
Out[476]: 
array(['2014-02-01T00:00:00.100000', '2014-02-01T00:00:00.300000',
       '2014-02-01T00:00:00.500000', '2014-02-01T19:30:00.500000',
       '2014-02-01T19:30:00.700000', '2014-02-01T19:30:00.900000'], dtype='datetime64[us]')

Each is saved as an 8 byte float which is interpreted as a datetime.

In [477]: _.dtype
Out[477]: dtype('<M8[us]')
In [478]: __.itemsize
Out[478]: 8

You can change the units (year, day, etc); do math, etc

In [479]: x1=x.astype('datetime64[us]')
In [480]: np.diff(x1)
Out[480]: array([     200000,      200000, 70200000000,      200000,      200000], dtype='timedelta64[us]')

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