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如何減去對應於python中不同時間間隔的pandas列？

[英]How do I subtract pandas columns that correspond to different time intervals in python?

 原文  2016-04-17 23:37:44  8   2    python/ python-2.7/ date/ datetime/ pandas

問題描述

如何從python中的同一csv中減去不同的時間間隔？

例如，如果我想從09:30:00高中減去09:15:00高。

我嘗試了幾種不同的方法，但一直都在努力。

這就是我嘗試過的。 

 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

也， 

 sub13 = a915 - a930

也， 

a915 = exm.at_time("09:15:00")
a930 = exm.at_time("09:30:00")

exm.sub13 = a915 - a930

也， 

sub13 = a915 - a930

甚至無法獲得一個獨立的專欄

感謝你的幫助！！！！

日期，時間，打開，高，低，關閉，音量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

2 個解決方案

解決方案1
 1  2016-04-18 02:01:56

您可以在datetime時間中使用strptime來為您的時間生成日期時間對象，然后減去它們以獲得差值。 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

解決方案2
 1 已采納  2016-04-18 05:36:23

我認為您可以先通過參數parse_dates將DATE和TIME列轉換為datetime ，並在read_csv從這個新的DATE_TIME列設置索引： 

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

如果需要減數Series （列），則需要相同的indexes ，因為會得到NAN ： 

print a915 - a930
DATE_TIME
1997-02-03 09:15:00   NaN
1997-02-03 09:30:00   NaN
Name: HIGH, dtype: float64

如果您只需要減去HIGH列中的值，則可以通過values將Series （列）轉換為numpy arrays ： 

print a915.values - a930.values
[-1.]

但是，如果你需要添加新列sub13 ，你需要改變index的Series a930由a915 。 然后可以減去值，輸出在索引為a915 1997-02-03 09:15:00 。 其他值缺失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

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