如何从python中的相同csv中减去不同的时间间隔？
例如，如果我想从09:30:00 HIGH减去09:15:00 HIGH。
我尝试了几种不同的方式，但一直在努力。
这就是我的尝试。
exm = pd.read_csv('exm')
a915 = exm.HIGH.at_time("09:15:00")
a930 = exm.HIGH.at_time("09:30:00")
exm.sub13 = a915 - a930
此外，
sub13 = a915 - a930
另外，
a915 = exm.at_time("09:15:00")
a930 = exm.at_time("09:30:00")
exm.sub13 = a915 - a930
此外，
sub13 = a915 - a930
甚至不能让它拉出一个独立的列
感谢您的帮助!!!!
日期，时间，OPEN，HIGH，LOW，CLOSE，VOLUME
02/03 / 1997,09：04：00,3046.00,3048.50,3046.00,3047.50,505
02/03 / 1997,09：05：00,3047.00,3048.00,3046.00,3047.00,162
02/03 / 1997,09：06：00,3047.50,3048.00,3047.00,3047.50,98
02/03 / 1997,09：07：00,3047.50,3047.50,3047.00,3047.50,228
02/03 / 1997,09：08：00,3048.00,3048.00,3047.50,3048.00,136
02/03 / 1997,09：09：00,3048.00,3048.00,3046.50,3046.50,174
02/03 / 1997,09：10：00,3046.50,3046.50,3045.00,3045.00,134
02/03 / 1997,09：11：00,3045.50,3046.00,3044.00,3045.00,43
02/03 / 1997,09：12：00,3045.00,3045.50,3045.00,3045.00,214
02/03 / 1997,09：13：00,3045.50,3045.50,3045.50,3045.50,8
02/03 / 1997,09：14：00,3045.50,3046.00,3044.50,3044.50,152
02/03 / 1997,09：15：00,3044.00,3044.00,3042.50,3042.50,126
02/03 / 1997,09：16：00,3043.50,3043.50,3043.00,3043.00,128
02/03 / 1997,09：17：00,3042.50,3043.50,3042.50,3043.50,23
02/03 / 1997,09：18：00,3043.50,3044.50,3043.00,3044.00,51
02/03 / 1997,09：19：00,3044.50,3044.50,3043.00,3043.00,18
02/03 / 1997,09：20：00,3043.00,3045.00,3043.00,3045.00,23
02/03 / 1997,09：21：00,3045.00,3045.00,3044.50,3045.00,51
02/03 / 1997,09：22：00,3045.00,3045.00,3045.00,3045.00,47
02/03 / 1997,09：23：00,3045.50,3046.00,3045.00,3045.00,77
02/03 / 1997,09：24：00,3045.00,3045.00,3045.00,3045.00,131
02/03 / 1997,09：25：00,3044.50,3044.50,3043.50,3043.50,138
02/03 / 1997,09：26：00,3043.50,3043.50,3043.50,3043.50,6
02/03 / 1997,09：27：00,3043.50,3043.50,3043.00,3043.00,56
02/03 / 1997,09：28：00,3043.00,3044.00,3043.00,3044.00,32
02/03 / 1997,09：29：00,3044.50,3044.50,3044.50,3044.50,63
02/03 / 1997,09：30：00,3045.00,3045.00,3045.00,3045.00,28
02/03 / 1997,09：31：00,3045.00,3045.50,3045.00,3045.50,75
02/03 / 1997,09：32：00,3045.50,3045.50,3044.00,3044.00,54
02/03 / 1997,09：33：00,3043.50,3044.50,3043.50,3044.00,96
02/03 / 1997,09：34：00,3044.00,3044.50,3044.00,3044.50,27
02/03 / 1997,09：35：00,3044.50,3044.50,3043.50,3044.50,44
02/03 / 1997,09：36：00,3044.00,3044.00,3043.00,3043.00,61
02/03 / 1997,09：37：00,3043.50,3043.50,3043.50,3043.50,18
02/03 / 1997,09：38：00,3043.50,3045.00,3043.50,3045.00,156
答案 0 :(得分：1)
您可以在strptime中使用datetime为您的时间生成日期时间对象，然后减去它们以获得差异。例如：
>>> import datetime
>>> t1=datetime.datetime.strptime('01/01/2016 20:00:00', "%d/%m/%Y %H:%M:%S")
>>> t2=datetime.datetime.strptime('01/01/2016 21:00:00', "%d/%m/%Y %H:%M:%S")
>>> t2-t1
datetime.timedelta(0, 3600)
>>> (t2-t1).seconds
3600
答案 1 :(得分：1)
我认为您可以先通过参数DATE将列TIME和datetime转换为parse_dates，然后在{{3}中的新DATE_TIME列中设置索引}}：
import pandas as pd
import io
temp=u"""DATE,TIME,OPEN,HIGH,LOW,CLOSE,VOLUME
02/03/1997,09:04:00,3046.00,3048.50,3046.00,3047.50,505
02/03/1997,09:05:00,3047.00,3048.00,3046.00,3047.00,162
02/03/1997,09:06:00,3047.50,3048.00,3047.00,3047.50,98
02/03/1997,09:07:00,3047.50,3047.50,3047.00,3047.50,228
02/03/1997,09:08:00,3048.00,3048.00,3047.50,3048.00,136
02/03/1997,09:09:00,3048.00,3048.00,3046.50,3046.50,174
02/03/1997,09:10:00,3046.50,3046.50,3045.00,3045.00,134
02/03/1997,09:11:00,3045.50,3046.00,3044.00,3045.00,43
02/03/1997,09:12:00,3045.00,3045.50,3045.00,3045.00,214
02/03/1997,09:13:00,3045.50,3045.50,3045.50,3045.50,8
02/03/1997,09:14:00,3045.50,3046.00,3044.50,3044.50,152
02/03/1997,09:15:00,3044.00,3044.00,3042.50,3042.50,126
02/03/1997,09:16:00,3043.50,3043.50,3043.00,3043.00,128
02/03/1997,09:17:00,3042.50,3043.50,3042.50,3043.50,23
02/03/1997,09:18:00,3043.50,3044.50,3043.00,3044.00,51
02/03/1997,09:19:00,3044.50,3044.50,3043.00,3043.00,18
02/03/1997,09:20:00,3043.00,3045.00,3043.00,3045.00,23
02/03/1997,09:21:00,3045.00,3045.00,3044.50,3045.00,51
02/03/1997,09:22:00,3045.00,3045.00,3045.00,3045.00,47
02/03/1997,09:23:00,3045.50,3046.00,3045.00,3045.00,77
02/03/1997,09:24:00,3045.00,3045.00,3045.00,3045.00,131
02/03/1997,09:25:00,3044.50,3044.50,3043.50,3043.50,138
02/03/1997,09:26:00,3043.50,3043.50,3043.50,3043.50,6
02/03/1997,09:27:00,3043.50,3043.50,3043.00,3043.00,56
02/03/1997,09:28:00,3043.00,3044.00,3043.00,3044.00,32
02/03/1997,09:29:00,3044.50,3044.50,3044.50,3044.50,63
02/03/1997,09:30:00,3045.00,3045.00,3045.00,3045.00,28
02/03/1997,09:31:00,3045.00,3045.50,3045.00,3045.50,75"""
#after testing replace io.StringIO(temp) to filename
exm = pd.read_csv(io.StringIO(temp), parse_dates = [['DATE', 'TIME']], index_col=0)
print exm
OPEN HIGH LOW CLOSE VOLUME
DATE_TIME
1997-02-03 09:04:00 3046.0 3048.5 3046.0 3047.5 505
1997-02-03 09:05:00 3047.0 3048.0 3046.0 3047.0 162
1997-02-03 09:06:00 3047.5 3048.0 3047.0 3047.5 98
1997-02-03 09:07:00 3047.5 3047.5 3047.0 3047.5 228
1997-02-03 09:08:00 3048.0 3048.0 3047.5 3048.0 136
1997-02-03 09:09:00 3048.0 3048.0 3046.5 3046.5 174
1997-02-03 09:10:00 3046.5 3046.5 3045.0 3045.0 134
1997-02-03 09:11:00 3045.5 3046.0 3044.0 3045.0 43
1997-02-03 09:12:00 3045.0 3045.5 3045.0 3045.0 214
1997-02-03 09:13:00 3045.5 3045.5 3045.5 3045.5 8
1997-02-03 09:14:00 3045.5 3046.0 3044.5 3044.5 152
1997-02-03 09:15:00 3044.0 3044.0 3042.5 3042.5 126
1997-02-03 09:16:00 3043.5 3043.5 3043.0 3043.0 128
1997-02-03 09:17:00 3042.5 3043.5 3042.5 3043.5 23
1997-02-03 09:18:00 3043.5 3044.5 3043.0 3044.0 51
1997-02-03 09:19:00 3044.5 3044.5 3043.0 3043.0 18
1997-02-03 09:20:00 3043.0 3045.0 3043.0 3045.0 23
1997-02-03 09:21:00 3045.0 3045.0 3044.5 3045.0 51
1997-02-03 09:22:00 3045.0 3045.0 3045.0 3045.0 47
1997-02-03 09:23:00 3045.5 3046.0 3045.0 3045.0 77
1997-02-03 09:24:00 3045.0 3045.0 3045.0 3045.0 131
1997-02-03 09:25:00 3044.5 3044.5 3043.5 3043.5 138
1997-02-03 09:26:00 3043.5 3043.5 3043.5 3043.5 6
1997-02-03 09:27:00 3043.5 3043.5 3043.0 3043.0 56
1997-02-03 09:28:00 3043.0 3044.0 3043.0 3044.0 32
1997-02-03 09:29:00 3044.5 3044.5 3044.5 3044.5 63
1997-02-03 09:30:00 3045.0 3045.0 3045.0 3045.0 28
1997-02-03 09:31:00 3045.0 3045.5 3045.0 3045.5 75
a915 = exm.HIGH.at_time("09:15:00")
a930 = exm.HIGH.at_time("09:30:00")
print a915
DATE_TIME
1997-02-03 09:15:00 3044.0
print a930
DATE_TIME
1997-02-03 09:30:00 3045.0
Name: HIGH, dtype: float64
如果您需要减去Series（列），则需要indexes，因为您获得了NAN：
print a915 - a930
DATE_TIME
1997-02-03 09:15:00 NaN
1997-02-03 09:30:00 NaN
Name: HIGH, dtype: float64
如果您只需要减去HIGH列中的值，请将Series（列）转换为numpy arrays read_csv：
print a915.values - a930.values
[-1.]
但是，如果您需要添加新列sub13，则需要index更改Series a930 a915。然后，您可以减去值，并且输出在行中，索引为a915 - 1997-02-03 09:15:00。缺少其他值 - NaN：
print a915
DATE_TIME
1997-02-03 09:15:00 3044.0
Name: HIGH, dtype: float64
print pd.Series(a930.values, index=a915.index)
DATE_TIME
1997-02-03 09:15:00 3045.0
dtype: float64
exm['sub13'] = a915 - pd.Series(a930.values, index=a915.index)
print exm
OPEN HIGH LOW CLOSE VOLUME sub13
DATE_TIME
1997-02-03 09:04:00 3046.0 3048.5 3046.0 3047.5 505 NaN
1997-02-03 09:05:00 3047.0 3048.0 3046.0 3047.0 162 NaN
1997-02-03 09:06:00 3047.5 3048.0 3047.0 3047.5 98 NaN
1997-02-03 09:07:00 3047.5 3047.5 3047.0 3047.5 228 NaN
1997-02-03 09:08:00 3048.0 3048.0 3047.5 3048.0 136 NaN
1997-02-03 09:09:00 3048.0 3048.0 3046.5 3046.5 174 NaN
1997-02-03 09:10:00 3046.5 3046.5 3045.0 3045.0 134 NaN
1997-02-03 09:11:00 3045.5 3046.0 3044.0 3045.0 43 NaN
1997-02-03 09:12:00 3045.0 3045.5 3045.0 3045.0 214 NaN
1997-02-03 09:13:00 3045.5 3045.5 3045.5 3045.5 8 NaN
1997-02-03 09:14:00 3045.5 3046.0 3044.5 3044.5 152 NaN
1997-02-03 09:15:00 3044.0 3044.0 3042.5 3042.5 126 -1.0
1997-02-03 09:16:00 3043.5 3043.5 3043.0 3043.0 128 NaN
1997-02-03 09:17:00 3042.5 3043.5 3042.5 3043.5 23 NaN
1997-02-03 09:18:00 3043.5 3044.5 3043.0 3044.0 51 NaN
1997-02-03 09:19:00 3044.5 3044.5 3043.0 3043.0 18 NaN
1997-02-03 09:20:00 3043.0 3045.0 3043.0 3045.0 23 NaN
1997-02-03 09:21:00 3045.0 3045.0 3044.5 3045.0 51 NaN
1997-02-03 09:22:00 3045.0 3045.0 3045.0 3045.0 47 NaN
1997-02-03 09:23:00 3045.5 3046.0 3045.0 3045.0 77 NaN
1997-02-03 09:24:00 3045.0 3045.0 3045.0 3045.0 131 NaN
1997-02-03 09:25:00 3044.5 3044.5 3043.5 3043.5 138 NaN
1997-02-03 09:26:00 3043.5 3043.5 3043.5 3043.5 6 NaN
1997-02-03 09:27:00 3043.5 3043.5 3043.0 3043.0 56 NaN
1997-02-03 09:28:00 3043.0 3044.0 3043.0 3044.0 32 NaN
1997-02-03 09:29:00 3044.5 3044.5 3044.5 3044.5 63 NaN
1997-02-03 09:30:00 3045.0 3045.0 3045.0 3045.0 28 NaN
1997-02-03 09:31:00 3045.0 3045.5 3045.0 3045.5 75 NaN