如何从具有相同分钟的行中获取均值/方差bpm值，并使用缺失分钟之前的值填充缺失分钟？
这是数据：
a={'dateTime': {0: '11/17/19 02:28:05', 1: '11/17/19 02:28:17', 2: '11/17/19 02:28:31', 3: '11/17/19 02:28:42', 4: '11/17/19 02:29:29', 5: '11/17/19 02:29:46', 6: '11/17/19 02:30:43', 7: '11/17/19 02:32:13', 8: '11/17/19 02:49:39', 9: '11/17/19 02:49:49', 10: '11/17/19 02:49:54', 11: '11/17/19 02:49:59', 12: '11/17/19 02:50:04', 13: '11/17/19 02:50:09', 14: '11/17/19 02:50:14', 15: '11/17/19 02:50:24', 16: '11/17/19 02:50:29', 17: '11/17/19 02:50:34', 18: '11/17/19 02:50:39', 19: '11/17/19 02:50:44', 20: '11/17/19 02:50:49', 21: '11/17/19 02:51:04', 22: '11/17/19 02:51:09', 23: '11/17/19 03:04:05', 24: '11/17/19 03:04:33', 25: '11/17/19 11:14:27', 26: '11/17/19 11:14:42', 27: '11/17/19 11:14:52', 28: '11/17/19 11:15:01', 29: '11/17/19 11:15:06', 30: '11/17/19 11:15:21'}, 'bpm': {0: 113, 1: 70, 2: 70, 3: 70, 4: 70, 5: 70, 6: 70, 7: 70, 8: 70, 9: 67, 10: 62, 11: 57, 12: 58, 13: 60, 14: 60, 15: 62, 16: 63, 17: 65, 18: 66, 19: 67, 20: 65, 21: 66, 22: 67, 23: 69, 24: 70, 25: 70, 26: 70, 27: 70, 28: 70, 29: 70, 30: 70}, 'confidence': {0: 0, 1: 0, 2: 0, 3: 0, 4: 0, 5: 0, 6: 0, 7: 0, 8: 0, 9: 1, 10: 1, 11: 2, 12: 2, 13: 2, 14: 1, 15: 1, 16: 1, 17: 1, 18: 1, 19: 1, 20: 1, 21: 1, 22: 1, 23: 0, 24: 0, 25: 0, 26: 0, 27: 1, 28: 1, 29: 0, 30: 1}}
ab=pd.DataFrame(a)
print(ab)
dateTime bpm confidence
0 11/17/19 02:28:05 113 0
1 11/17/19 02:28:17 70 0
2 11/17/19 02:28:31 70 0
3 11/17/19 02:28:42 70 0
4 11/17/19 02:29:29 70 0
5 11/17/19 02:29:46 70 0
6 11/17/19 02:30:43 70 0
7 11/17/19 02:32:13 70 0
8 11/17/19 02:49:39 70 0
9 11/17/19 02:49:49 67 1
10 11/17/19 02:49:54 62 1
11 11/17/19 02:49:59 57 2
平均值输出示例：
dateTime bpm
1 11/17/19 02:28 80
2 11/17/19 02:29 70
3 11/17/19 02:30 70
4 11/17/19 02:31 70
5 11/17/19 02:32 70
6 11/17/19 02:33 70
7 11/17/19 02:34 70
8 11/17/19 02:35 70
9 11/17/19 02:36 70
10 11/17/19 02:37 70
11 11/17/19 02:38 70
12 11/17/19 02:39 70
13 11/17/19 02:40 70
14 11/17/19 02:41 70
15 11/17/19 02:42 70
16 11/17/19 02:43 70
17 11/17/19 02:44 70
18 11/17/19 02:45 70
19 11/17/19 02:46 70
20 11/17/19 02:47 70
21 11/17/19 02:48 70
22 11/17/19 02:49 64
23 11/17/19 02:50 62
24 11/17/19 02:51 66
答案 0 :(得分：1)
我相信您需要DataFrame.resample和mean并通过ffill来填充缺失值：
ab['dateTime'] = pd.to_datetime(ab['dateTime'])
ab = ab.resample('Min', on='dateTime').mean().ffill()
print(ab)
bpm confidence
dateTime
2019-11-17 02:28:00 80.75 0.000000
2019-11-17 02:29:00 70.00 0.000000
2019-11-17 02:30:00 70.00 0.000000
2019-11-17 02:31:00 70.00 0.000000
2019-11-17 02:32:00 70.00 0.000000
... ...
2019-11-17 11:11:00 69.50 0.000000
2019-11-17 11:12:00 69.50 0.000000
2019-11-17 11:13:00 69.50 0.000000
2019-11-17 11:14:00 70.00 0.333333
2019-11-17 11:15:00 70.00 0.666667
[528 rows x 2 columns]
如果需要为每列指定不同的功能，请使用Resampler.agg和字典：
ab['dateTime'] = pd.to_datetime(ab['dateTime'])
ab = ab.resample('Min', on='dateTime').agg({'bpm':'mean', 'confidence':'var'}).ffill()