Count number of events per hour given in datetime/numpy
datetime, numpy, python
Solution
If you are using Pandas, and you have a time series that has a DateTime index, you can use the `resample` method, with `how='count'`. For example, in the following, `rng` is a range of timestamps, and `ts` is a series of values that uses `rng` as the index. (For your calculation, the values in the time series don't matter.) In this example, there are 360 "events" per hour, so the expected result is 360 for each hour except the last.
Create some sample data:
In [71]: import pandas as pd
In [72]: rng = pd.date_range('1/1/2011', periods=10000, freq='10S')
In [73]: ts = pd.Series(np.random.randint(0, 5, len(rng)), index=rng)
In [74]: ts
Out[74]:
2011-01-01 00:00:00 2
2011-01-01 00:00:10 0
2011-01-01 00:00:20 1
2011-01-01 00:00:30 4
2011-01-01 00:00:40 1
2011-01-01 00:00:50 1
2011-01-01 00:01:00 2
2011-01-01 00:01:10 0
2011-01-01 00:01:20 3
2011-01-01 00:01:30 4
2011-01-01 00:01:40 2
2011-01-01 00:01:50 4
2011-01-01 00:02:00 1
2011-01-01 00:02:10 0
2011-01-01 00:02:20 4
...
2011-01-02 03:44:10 2
2011-01-02 03:44:20 0
2011-01-02 03:44:30 3
2011-01-02 03:44:40 0
2011-01-02 03:44:50 0
2011-01-02 03:45:00 4
2011-01-02 03:45:10 3
2011-01-02 03:45:20 2
2011-01-02 03:45:30 0
2011-01-02 03:45:40 1
2011-01-02 03:45:50 0
2011-01-02 03:46:00 2
2011-01-02 03:46:10 0
2011-01-02 03:46:20 2
2011-01-02 03:46:30 2
Freq: 10S, Length: 10000
Use the `resample` method to count the number of events in each hour. The first argument, `'H'`, means we are resampling to an hourly rate.
In [75]: ts.resample('H', how='count')
Out[75]:
2011-01-01 00:00:00 360
2011-01-01 01:00:00 360
2011-01-01 02:00:00 360
2011-01-01 03:00:00 360
2011-01-01 04:00:00 360
2011-01-01 05:00:00 360
2011-01-01 06:00:00 360
2011-01-01 07:00:00 360
2011-01-01 08:00:00 360
2011-01-01 09:00:00 360
2011-01-01 10:00:00 360
2011-01-01 11:00:00 360
2011-01-01 12:00:00 360
2011-01-01 13:00:00 360
2011-01-01 14:00:00 360
2011-01-01 15:00:00 360
2011-01-01 16:00:00 360
2011-01-01 17:00:00 360
2011-01-01 18:00:00 360
2011-01-01 19:00:00 360
2011-01-01 20:00:00 360
2011-01-01 21:00:00 360
2011-01-01 22:00:00 360
2011-01-01 23:00:00 360
2011-01-02 00:00:00 360
2011-01-02 01:00:00 360
2011-01-02 02:00:00 360
2011-01-02 03:00:00 280
Freq: H, dtype: int64
Problem
Assume that I have an array of `timestamp`'s (given as `datetime` or `numpy.datetime64` for instance). Furthermore, each entry represents an occurrence. I want to bucket the events on a per-hour basis; i.e. count how many events occurred per hour. Is there a straightforward way to do it? Some Numpy/Pandas magic? Or is the only way is to convert the `timestamp`'s to seconds and bucket the seconds, as suggested here