使用 Python/Pandas 以 csv 中的开始时间和结束时间日期时间列按小时分组

Question

I'm just getting my toes wet in Pandas and gotten pretty stuck. I want to aggregate events (get the count) in a CSV by hour and have a start time and and end time in the event.

ie an example would be:

event, start, end
soccer, 2020-01-20 00:34:00, 2020-01-20 02:34:00,
football, 2020-01-20 00:34:00, 2020-01-20 01:34:00
etc

expected output:

00:00:00 - 2 (both began in 0th hour and went to 1st hour)
01:00:00 - 2 (both were live in 1st hour)
02:00:00 - 1 (only soccer occurred in 02 hour)

How would you go about this? I've been trying reindexing, resampling, time difference, time indexes — all with no luck.

Answer 1

What you want is effectively a frequency distribution of hours during which events are taking place. First, you need to generate the samples from which to take the distribution by creating a range and then exploding it:

hours = events.apply(lambda row: range(row['end'].hour - row['start'].hour + 1), axis=1).explode()

0    0
0    1
0    2
1    0
1    1
dtype: object

Don't forget to add one to the difference between end and start to account for fencepost error . Then just get value counts for the sample. To get the frequency in order of hours instead of by descending count, pass sort=False .

hours.value_counts(sort=False)

0    2
1    2
2    1
dtype: int64