我正在更新我必须使用 pandas 的实现并利用其功能，我将不胜感激。我有一个像这样的pandas事件数据框：
ID Start End
0 243552 2010-12-12 23:00:53 2010-12-12 23:37:14
1 243621 2010-12-12 23:25:58 2010-12-13 02:20:40
2 243580 2010-12-12 23:39:19 2010-12-13 07:22:39
3 243579 2010-12-12 23:42:53 2010-12-13 05:40:14
4 243491 2010-12-12 23:43:53 2010-12-13 07:48:14
...
...
ID 的Dtypes为int64，开始和结束为datetime64[ns]。请注意，数据框在 Start 列中排序，但不一定要在 End 列中排序。
我希望在输入时间戳 t1 和 t2 之间的某个时间范围内分析此数据，以便用户输入相等的时间跨度，并生成一个新的数据帧，由这些时期的时间戳。
我想要做的是将每个时段的数据分组，产生5列： Start_count ， End_count ， Span_avg ， Start_inter_avg 和 End_inter_avg 。例如，考虑一个10分钟的分组，我想得到这个：
Start_count End_count Span_avg Start_inter_avg End_inter_avg
Period
2010-12-12 23:10:00 1 0 00:36:21 00:00:00 00:00:00
2010-12-12 23:20:00 0 0 0 00:00:00 00:00:00
2010-12-12 23:30:00 1 0 02:54:42 00:00:00 00:00:00
2010-12-12 23:40:00 1 1 07:43:20 00:00:00 00:00:00
2010-12-12 23:50:00 2 0 07:00:51 00:01:00 00:00:00
...
...
dtypes的位置：{{> 1}用于 Start_count 和 End_count ，而int64用于 Span_avg ， Start_inter_avg 和 End_inter_avg 。我想要生成的数据帧的列是：
timedelta64[ns]; ]Period - 10 min, Period]，第2个中包含 Start 值的条目在每个条目中计算差异结束 - 开始，第3个）平均这些值。]Period - 10 min, Period]中包含 Start 值的条目，然后排序他们（好吧，他们已经排序），第二）计算连续时间戳之间的时间差，第三）平均这些差异。 （因此，如果在某个时期内有3个开始时间戳，[a，b，c]，则会有2个时间差，[ba，cb]，最终值将等于（（ BA）+（CB））/ 2）。例如，在分组30分钟时得到的表格＆＃39;期间应该是：
]Period - 10 min, Period]您可以试用此 test.csv 文件：
Start_count End_count Span_avg Start_inter_avg End_inter_avg
Period
2010-12-12 23:30:00 2 0 01:45:31.500 00:25:05 00:00:00
2010-12-13 00:00:00 3 1 07:15:00.666 00:02:17 00:00:00
...
...
尝试解决方案（回答部分问题）
这是我尝试解决方案。我只执行前3列，我得到 Start_count 和 End_count ID,Start,End
243552,2010-12-12 23:00:53,2010-12-12 23:37:14
243621,2010-12-12 23:25:58,2010-12-13 02:20:40
243580,2010-12-12 23:39:19,2010-12-13 07:22:39
243579,2010-12-12 23:42:53,2010-12-13 05:40:14
243491,2010-12-12 23:43:53,2010-12-13 07:48:14
243490,2010-12-12 23:43:58,2010-12-13 01:18:40
243465,2010-12-13 00:07:53,2010-12-13 07:26:14
243515,2010-12-13 00:35:58,2010-12-13 03:41:40
243572,2010-12-13 00:46:58,2010-12-13 03:47:40
243520,2010-12-13 01:15:53,2010-12-13 05:14:14
243609,2010-12-13 01:29:53,2010-12-13 08:10:14
243482,2010-12-13 01:44:19,2010-12-13 05:57:39
243563,2010-12-13 01:49:53,2010-12-13 06:04:14
243414,2010-12-13 02:06:16,2010-12-13 02:46:48
243441,2010-12-13 02:15:16,2010-12-13 03:11:48
243548,2010-12-13 02:33:58,2010-12-13 02:49:40
243447,2010-12-13 05:01:42,2010-12-13 21:55:21
243531,2010-12-13 05:53:25,2010-12-13 07:49:59
243583,2010-12-13 05:53:25,2010-12-13 09:00:59
243593,2010-12-13 06:06:25,2010-12-13 09:50:59
243460,2010-12-13 06:14:42,2010-12-13 18:14:44
243596,2010-12-13 06:15:10,2010-12-13 21:47:25
243575,2010-12-13 06:22:42,2010-12-13 20:51:21
243514,2010-12-13 06:24:14,2010-12-13 08:34:07
243421,2010-12-13 06:31:14,2010-12-13 10:57:07
243471,2010-12-13 06:35:23,2010-12-13 14:11:13
243518,2010-12-13 06:36:48,2010-12-13 17:35:39
243565,2010-12-13 06:37:43,2010-12-13 17:16:22
243564,2010-12-13 06:48:16,2010-12-13 16:18:15
243424,2010-12-13 06:48:48,2010-12-13 16:19:39
243437,2010-12-13 06:58:46,2010-12-13 17:11:30
243573,2010-12-13 07:00:14,2010-12-13 09:46:07
243585,2010-12-13 07:01:35,2010-12-13 09:01:38
243483,2010-12-13 07:02:16,2010-12-13 16:36:15
243425,2010-12-13 07:04:21,2010-12-13 16:03:50
243570,2010-12-13 07:07:48,2010-12-13 08:51:04
243507,2010-12-13 07:10:03,2010-12-13 15:58:48
243535,2010-12-13 07:10:23,2010-12-13 11:31:13
243502,2010-12-13 07:13:21,2010-12-13 19:06:50
243525,2010-12-13 07:13:21,2010-12-13 19:34:50
243486,2010-12-13 07:13:56,2010-12-13 17:49:38
243451,2010-12-13 07:15:58,2010-12-13 17:34:03
243485,2010-12-13 07:17:35,2010-12-13 09:40:38
243487,2010-12-13 07:19:01,2010-12-13 10:39:35
243522,2010-12-13 07:19:25,2010-12-13 18:03:02
243481,2010-12-13 07:19:48,2010-12-13 11:08:04
243545,2010-12-13 07:20:42,2010-12-13 20:38:44
243492,2010-12-13 07:23:07,2010-12-13 17:38:42
243611,2010-12-13 07:23:23,2010-12-13 12:58:13
243508,2010-12-13 07:25:25,2010-12-13 18:29:02
243620,2010-12-13 07:25:46,2010-12-13 17:51:30
243466,2010-12-13 07:27:40,2010-12-13 19:05:58
243582,2010-12-13 07:29:29,2010-12-13 20:08:10
243568,2010-12-13 07:31:17,2010-12-13 15:30:37
243461,2010-12-13 07:32:24,2010-12-13 20:47:52
243623,2010-12-13 07:33:10,2010-12-13 10:34:20
243498,2010-12-13 07:33:25,2010-12-13 16:22:02
243427,2010-12-13 07:33:48,2010-12-13 20:00:39
243526,2010-12-13 07:34:10,2010-12-13 09:46:20
243472,2010-12-13 07:36:10,2010-12-13 20:36:25
243479,2010-12-13 07:36:48,2010-12-13 19:30:39
243494,2010-12-13 07:39:07,2010-12-13 17:03:42
243433,2010-12-13 07:39:35,2010-12-13 09:19:38
243503,2010-12-13 07:40:06,2010-12-13 13:53:08
243429,2010-12-13 07:40:35,2010-12-13 10:54:38
243422,2010-12-13 07:43:23,2010-12-13 10:35:10
243618,2010-12-13 07:46:19,2010-12-13 11:56:40
243445,2010-12-13 07:48:14,2010-12-13 10:15:07
243554,2010-12-13 07:49:14,2010-12-13 09:11:57
243542,2010-12-13 07:49:17,2010-12-13 18:53:37
243501,2010-12-13 07:50:40,2010-12-13 19:29:58
243529,2010-12-13 07:51:18,2010-12-13 17:14:15
243457,2010-12-13 07:53:55,2010-12-13 15:33:27
243613,2010-12-13 07:53:58,2010-12-13 17:00:03
243562,2010-12-13 07:54:01,2010-12-13 14:17:09
243571,2010-12-13 07:54:48,2010-12-13 18:39:39
243541,2010-12-13 07:58:53,2010-12-13 16:02:23
243510,2010-12-13 07:59:10,2010-12-13 19:04:51
243470,2010-12-13 07:59:46,2010-12-13 17:06:30
243448,2010-12-13 07:59:48,2010-12-13 18:38:39
243606,2010-12-13 08:03:21,2010-12-13 18:07:50
243430,2010-12-13 08:04:08,2010-12-13 17:49:41
243495,2010-12-13 08:04:25,2010-12-13 18:15:02
243591,2010-12-13 08:07:08,2010-12-13 17:33:54
243551,2010-12-13 08:07:10,2010-12-13 18:18:25
243459,2010-12-13 08:10:14,2010-12-13 10:53:07
243558,2010-12-13 08:11:00,2010-12-13 11:56:01
243605,2010-12-13 08:13:20,2010-12-13 16:38:14
243452,2010-12-13 08:15:23,2010-12-13 13:50:13
243446,2010-12-13 08:17:06,2010-12-13 14:00:08
243516,2010-12-13 08:17:20,2010-12-13 15:03:14
243450,2010-12-13 08:18:17,2010-12-13 16:21:37
243473,2010-12-13 08:19:22,2010-12-13 12:07:49
243438,2010-12-13 08:20:10,2010-12-13 19:34:25
243464,2010-12-13 08:21:03,2010-12-13 14:44:48
243536,2010-12-13 08:21:29,2010-12-13 17:32:15
243476,2010-12-13 08:21:58,2010-12-13 17:34:03
243595,2010-12-13 08:24:19,2010-12-13 11:38:40
243532,2010-12-13 08:27:10,2010-12-13 20:28:25
243497,2010-12-13 08:27:20,2010-12-13 14:12:14
dtype，我按周期时间戳的第一个边界索引数据（不同于我问，但是好的，总的来说我想知道它是否可以用更简单，更短，更优雅的方式完成。
float64答案 0 :(得分：1)
这是一个难题，但这是解决方案：
import pandas as pd
period = "10min"
df = pd.read_csv("test.csv", parse_dates=[1, 2])
span = df.End - df.Start
start_period = df.Start.dt.floor(period)
end_period = df.End.dt.floor(period)
start_count = start_period.value_counts(sort=False)
end_count = end_period.value_counts(sort=False)
span_average = pd.to_timedelta(
span.dt.total_seconds().groupby(start_period).mean().round(),
unit="s").rename("Span_average")
def average_span(s):
if len(s) > 1:
return (s.max() - s.min()).total_seconds() / (len(s) - 1)
else:
return 0
start_inter_avg = pd.to_timedelta(
df.Start.groupby(start_period).agg(average_span).round(),
unit="s").rename("Start_inter_avg")
end_inter_avg = pd.to_timedelta(
df.End.groupby(end_period).agg(average_span).round(),
unit="s").rename("End_inter_avg")
res = pd.concat([start_count, end_count, span_average, start_inter_avg, end_inter_avg],
axis=1).resample(period).asfreq().fillna(0)
输出：
Start End Span_average Start_inter_avg End_inter_avg
2010-12-12 23:00:00 1.0 0.0 00:36:21 00:00:00 00:00:00
2010-12-12 23:10:00 0.0 0.0 00:00:00 00:00:00 00:00:00
2010-12-12 23:20:00 1.0 0.0 02:54:42 00:00:00 00:00:00
2010-12-12 23:30:00 1.0 1.0 07:43:20 00:00:00 00:00:00
2010-12-12 23:40:00 3.0 0.0 05:12:08 00:00:32 00:00:00
2010-12-12 23:50:00 0.0 0.0 00:00:00 00:00:00 00:00:00
2010-12-13 00:00:00 1.0 0.0 07:18:21 00:00:00 00:00:00
2010-12-13 00:10:00 0.0 0.0 00:00:00 00:00:00 00:00:00