How do I bin in one minute intervals using datetime objects?

Question

I have a list of times (as datetime objects) with a record of counts (photons as it were). I would like to bin the counts into one-minute bins. I thought I could do this with a histogram, but numpy histogram does not play with datetime objects. How do I bin this data into one-minute intervals?

Here is a sample of my data:

Times = ['2019-02-04T06:11:31', '2019-02-04T06:11:33',
'2019-02-04T06:11:35', '2019-02-04T06:11:37',
'2019-02-04T06:11:39', '2019-02-04T06:11:41',
'2019-02-04T06:11:43', '2019-02-04T06:11:45',
'2019-02-04T06:11:47', '2019-02-04T06:11:49',
'2019-02-04T06:11:51', '2019-02-04T06:11:53',
'2019-02-04T06:11:55', '2019-02-04T06:11:57',
'2019-02-04T06:11:59', '2019-02-04T06:12:01',
'2019-02-04T06:12:03', '2019-02-04T06:12:05',
'2019-02-04T06:12:07', '2019-02-04T06:12:09',
'2019-02-04T06:12:11', '2019-02-04T06:12:13',
'2019-02-04T06:12:15', '2019-02-04T06:12:17',
'2019-02-04T06:12:19', '2019-02-04T06:12:21',
'2019-02-04T06:12:23', '2019-02-04T06:12:25',
'2019-02-04T06:12:27', '2019-02-04T06:12:29',
'2019-02-04T06:12:31', '2019-02-04T06:12:33',
'2019-02-04T06:12:35', '2019-02-04T06:12:37',
'2019-02-04T06:12:39', '2019-02-04T06:12:41']

Counts = [1628, 1613, 1622, 1650, 1527, 1622, 1585, 1529, 1580,
          1497, 1523, 1450, 1453, 1479, 1454, 1423, 1495, 1429,
          1429, 1455, 1512, 1544, 1441, 1463, 1463, 1453, 1427,
          1378, 1409, 1409, 1457, 1461, 1476, 1419, 1386, 1425]

I've thought about using pandas dataframes, but I'm struggling to implement it and I'm not sure if this is the correct path.

Answer 1

You can discard the seconds part from the time, and add up the counts in a minute using a dictionary:

dict_counts = {} # key = timestamp, value = cumulative count

for t,c in zip(Times, Counts):
    t = t.split(':')
    new_t = t[0] + ':' + t[1]
    dict_counts[new_t] =  dict_counts.get(new_t, 0) + c

NewTimes = list(dict_counts.keys())
NewCounts = list(dict_counts.values())

print(NewTimes)
print(NewCounts)

Output:

['2019-02-04T06:11', '2019-02-04T06:12']
[23212, 30354]