Reputation: 1073
Let's say I want to store datetime values over 100 iterations of a for loop in a numpy array like so:
import numpy as np
import time
from datetime import datetime
Startmult = np.zeros((1,100))
for i in range(100):
Startmult[i] = datetime.now()
Whenever I do this, I get the following error:
float() argument must be a string or a number, not 'datetime.datetime'
Is there a way to overcome this error?
Thank you for your help!
EDIT: Also, now I would like to do the following, but can't seem to find a solution:
CPUtime = np.zeros((100), dtype='datetime64[s]')
for i in range(100):
Start = datetime.now()
CPUtime[i] = datetime.now()-Start
In now get the following error:
Could not convert object to NumPy datetime
Upvotes: 4
Views: 12502
Reputation: 221714
As pointed out in the error message, we need to use a compatible dtype there, float
won't do. One of the compatible ones would be datetime64
and with it we need to specify the precision as well. Let's say we want for seconds
. Then, the array initialization part would be -
Startmult = np.zeros((100), dtype='datetime64[s]')
All available time units are listed here
.
Here's a sample run for nano-sec
run on 100
elements case and verifying with the first and last output elements -
In [447]: Startmult = np.zeros((100), dtype='datetime64[ns]')
...: for i in range(100):
...: Startmult[i] = datetime.now()
...:
In [448]: Startmult[0] # first element
Out[448]: numpy.datetime64('2017-08-31T22:39:45.722306000')
In [449]: Startmult[-1] # last element
Out[449]: numpy.datetime64('2017-08-31T22:39:45.723201000')
Edit : If you are trying to store the seconds elapsed between two points in time as floating pt numbers, you could do something like this -
CPUtime = np.zeros((100))
for i in range(100):
Start = datetime.now()
time_diff = datetime.now()-Start
CPUtime[i] = time_diff.total_seconds()
For u-sec
precision, use : time_diff.microseconds
.
Upvotes: 7
Reputation: 2338
your need to convert current datetime in integer format (only seconds)
Upvotes: 2