We can use the numpy arange - https://docs.scipy.org/doc/numpy/reference/generated/numpy.arange.html , function which is most used to create arrays using start / stop / step arguments.
Syntax:
numpy.arange([start, ]stop, [step, ]dtype=None)
In case of datetime values, we need to specify the step value, and the correct type and unit of the timestep in the dtype argument
. dtype='datetime64[m]' will set the timestep unit to minutes;
. dtype='datetime64[h]' will set the timestep unit to hours;
. dtype='datetime64[D]' will set the timestep unit to days;
. dtype='datetime64[M]' will set the timestep unit to months;
. dtype='datetime64[Y]' will set the timestep unit to months;
For example:
import numpy as np
dates = np.arange('2017-06-01', '2017-06-02', 15, dtype='datetime64[m]') # 15 is the timestep value, dtype='datetime64[m] means that the step is datetime minutes
This example will create an array of 96 values, between 01jun2017 and 02jun2017, with a time step of 15 minutes.
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