I would like to get the mean of the following dates. I thought about converting all the data to seconds and then averaging them. But there is probably a better way to do it.
date = ['2016-02-23 09:36:26', '2016-02-24 10:00:32', '2016-02-24 11:28:22', '2016-02-24 11:27:20', '2016-02-24 11:24:15', '2016-02-24 11:20:25', '2016-02-24 11:17:43', '2016-02-24 11:12:03', '2016-02-24 11:09:11', '2016-02-24 11:08:44', '2016-02-24 11:05:28', '2016-02-24 11:03:23', '2016-02-24 10:58:08', '2016-02-24 10:53:59', '2016-02-24 10:49:34', '2016-02-24 10:43:33', '2016-02-24 10:35:27', '2016-02-24 10:31:50', '2016-02-24 10:31:17', '2016-02-24 10:30:05', '2016-02-24 10:29:21']
Nasty solution:
import datetime
import time
import numpy as np
date = ['2016-02-23 09:36:26', '2016-02-24 10:00:32', '2016-02-24 11:28:22', '2016-02-24 11:27:20', '2016-02-24 11:24:15', '2016-02-24 11:20:25', '2016-02-24 11:17:43', '2016-02-24 11:12:03', '2016-02-24 11:09:11', '2016-02-24 11:08:44', '2016-02-24 11:05:28', '2016-02-24 11:03:23', '2016-02-24 10:58:08', '2016-02-24 10:53:59', '2016-02-24 10:49:34', '2016-02-24 10:43:33', '2016-02-24 10:35:27', '2016-02-24 10:31:50', '2016-02-24 10:31:17', '2016-02-24 10:30:05', '2016-02-24 10:29:21']
sec = [time.mktime(datetime.datetime.strptime(d, "%Y-%m-%d %H:%M:%S").timetuple()) for d in date]
mean = datetime.datetime.fromtimestamp(np.mean(sec))
print(mean)
Simple approach to finding the average of an array We would first count the total number of elements in an array followed by calculating the sum of these elements and then dividing the obtained sum by the total number of values to get the Average / Arithmetic mean.
Calculate average by date in ExcelSelect a blank cell, enter the formula =AVERAGEIF(J2:J24,P2,M2:M24) into it, and press the Enter key. Then you will get the average of the specified date.
Notes: 1. Except the above array formula, here is also a normal formula:=SUMPRODUCT(--(A2:A15>=E1),--(A2:A15<=E2),B2:B15)/SUMPRODUCT(--(A2:A15>=E1),--(A2:A15<=E2)), and then just press Enter key. 2.
In NumPy all datetime64[s]
s are internally represented by 8-byte integers.
The ints represent the number of seconds since the Epoch.
So you could convert the date
list to a NumPy array of datetime64[s]
dtype,
view it as dtype i8
(8-byte ints), take the mean, and then convert the int back into a datetime64[s]
.
import numpy as np
date = ['2016-02-23 09:36:26', '2016-02-24 10:00:32', '2016-02-24 11:28:22', '2016-02-24 11:27:20', '2016-02-24 11:24:15', '2016-02-24 11:20:25', '2016-02-24 11:17:43', '2016-02-24 11:12:03', '2016-02-24 11:09:11', '2016-02-24 11:08:44', '2016-02-24 11:05:28', '2016-02-24 11:03:23', '2016-02-24 10:58:08', '2016-02-24 10:53:59', '2016-02-24 10:49:34', '2016-02-24 10:43:33', '2016-02-24 10:35:27', '2016-02-24 10:31:50', '2016-02-24 10:31:17', '2016-02-24 10:30:05', '2016-02-24 10:29:21']
mean = (np.array(date, dtype='datetime64[s]')
.view('i8')
.mean()
.astype('datetime64[s]'))
print(mean)
prints
2016-02-24T09:43:40-0500
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