Python 日期和时间列的串联以及对索引的赋值

我有这些数据

               Date   Time     Open     High      Low    Close  Volume
0      2013.07.09   7:00  101.056  101.151  101.016  101.130    1822
1      2013.07.09   8:00  101.130  101.257  101.128  101.226    2286
2      2013.07.09   9:00  101.226  101.299  101.175  101.180    2685
3      2013.07.09  10:00  101.178  101.188  101.019  101.154    2980

我把日期和时间结合起来就是这个

                 Datetime  
0     2013-07-09 07:00:00  
1     2013-07-09 08:00:00 

所以我想在我的数据和索引中将DateTime替换为Date和Time。我期望的结果是这样的

Datetime     Time     Open     High      Low    Close  Volume

这是我的密码

import pandas as pd

df = pd.read_csv(r"C:\Users\Administrator\Desktop\Csv for PYthon\USDJPY60.csv")
df['Datetime'] = pd.to_datetime(df['Date'].apply(str)+' '+df['Time'])
print(df)

您只能在以下位置使用参数index_col和parse_dates：

但如果需要时间列：

仅对于索引，两列均未更改：

df.index = pd.to_datetime(df['Date']+' '+df['Time'])
print (df)
                           Date   Time     Open     High      Low    Close  \
2013-07-09 07:00:00  2013.07.09   7:00  101.056  101.151  101.016  101.130   
2013-07-09 08:00:00  2013.07.09   8:00  101.130  101.257  101.128  101.226   
2013-07-09 09:00:00  2013.07.09   9:00  101.226  101.299  101.175  101.180   
2013-07-09 10:00:00  2013.07.09  10:00  101.178  101.188  101.019  101.154   

                     Volume  
2013-07-09 07:00:00    1822  
2013-07-09 08:00:00    2286  
2013-07-09 09:00:00    2685  
2013-07-09 10:00:00    2980  

---

SyntaxError:无效的syntaxwhat返回无效语法？你用什么解决办法？@jezrael Lol。。。当OP需要帮助但不愿意帮助他们时，运行文件'C:/Users/Administrator/Documents/Downloads/Compressed/course3_Downloads/untitled7.py'，wdir='C:/Users/Administrator/Documents/Downloads/Compressed/course3_Downloads/untitled7.py'，wdir='C:/Users/Administrator/Documents/Downloads/course3_Downloads/untitled7.py，第5行df=pd.read_csvrC:\Users\Administrator\Desktop\Csv for PYthon\USDJPY60.Csv，index_col=['Datetime']，parse_dates=['Date'，'Time']]^SyntaxError:无效syntax@KornTawe-看起来不错，请再次尝试重写代码，因为可能复制了一些坏字符。这显然有帮助。

df = pd.read_csv(r"C:\Users\Administrator\Desktop\Csv for PYthon\USDJPY60.csv"), 
                   index_col=['Date'],
                   parse_dates=['Date'])

df.index = df.index + pd.to_timedelta(df['Time'] + ':00')
print (df)
                      Time     Open     High      Low    Close  Volume
2013-07-09 07:00:00   7:00  101.056  101.151  101.016  101.130    1822
2013-07-09 08:00:00   8:00  101.130  101.257  101.128  101.226    2286
2013-07-09 09:00:00   9:00  101.226  101.299  101.175  101.180    2685
2013-07-09 10:00:00  10:00  101.178  101.188  101.019  101.154    2980

df.index = pd.to_datetime(df['Date']+' '+df['Time'])
print (df)
                           Date   Time     Open     High      Low    Close  \
2013-07-09 07:00:00  2013.07.09   7:00  101.056  101.151  101.016  101.130   
2013-07-09 08:00:00  2013.07.09   8:00  101.130  101.257  101.128  101.226   
2013-07-09 09:00:00  2013.07.09   9:00  101.226  101.299  101.175  101.180   
2013-07-09 10:00:00  2013.07.09  10:00  101.178  101.188  101.019  101.154   

                     Volume  
2013-07-09 07:00:00    1822  
2013-07-09 08:00:00    2286  
2013-07-09 09:00:00    2685  
2013-07-09 10:00:00    2980