将pandas datetime对象从[HH：MM：ss]格式化为[HH：MM]

Question

我有一个pandas DataFrame有4列（时间，T1，T2，T3），并且使用定制的温度数据记录器在一天中获得它们各自的值。 Time列的格式为[HH：MM：ss]，但我想将其更改为[HH：MM]（截断秒数）以进行绘图。有没有一种简单的方法来实现这一目标？

这是我的代码:(使用Python 3.6）

import pandas as pd  
import matplotlib.pyplot as plt


df = pd.DataFrame(pd.read_excel('water_data_full_load.xlsx'))  
df.drop("Date", axis = 1, inplace =True)  
df.set_index('Time',inplace =True)  

df.rename_axis({"T1": "Twall", "T2":"Twater", "T3":"Tsurr"}, axis=1, inplace=True)

df['Time'] = df['Time'].apply(lambda x: x[:5])  
graph = df[['Twall','Twater','Tsurr']].plot()  
plt.xticks(rotation =45)  
plt.ylabel('Temperature ($^\circ$C)')  
plt.xlabel('Time of the day (HH:MM)')  
plt.show(graph)

Please refer to this image for the current DataFrame

Answer 1

import pandas as pd

df = pd.DataFrame({ 'Time': ['22:05:00','22:06:00','22:07:00']})

df['Time_to_plot'] = df['Time'].apply(lambda x: x[:5])

print df


       Time Time_to_plot
0  22:05:00        22:05
1  22:06:00        22:06
2  22:07:00        22:07

Answer 2

<强>更新

In [41]: df = pd.DataFrame({'Time': ['11:07:00','12:06:00','13:17:00'], 'Twall':[10,20,30]}).set_index('Time')

In [42]: df
Out[42]:
          Twall
Time
11:07:00     10
12:06:00     20
13:17:00     30

In [43]: df.index = df.index.astype(str).str.rsplit(':',n=1).str[0]

In [44]: df
Out[44]:
       Twall
Time
11:07     10
12:06     20
13:17     30

演示（矢量化方法） - 用于截断datetime dtype列中的秒数：

In [46]: df = pd.DataFrame(pd.date_range('2017-01-01', freq='99S', periods=10), columns=['date'])

In [47]: df
Out[47]:
                 date
0 2017-01-01 00:00:00
1 2017-01-01 00:01:39
2 2017-01-01 00:03:18
3 2017-01-01 00:04:57
4 2017-01-01 00:06:36
5 2017-01-01 00:08:15
6 2017-01-01 00:09:54
7 2017-01-01 00:11:33
8 2017-01-01 00:13:12
9 2017-01-01 00:14:51


In [49]: df['date'] = df['date'].values.astype('<M8[m]')

In [50]: df
Out[50]:
                 date
0 2017-01-01 00:00:00
1 2017-01-01 00:01:00
2 2017-01-01 00:03:00
3 2017-01-01 00:04:00
4 2017-01-01 00:06:00
5 2017-01-01 00:08:00
6 2017-01-01 00:09:00
7 2017-01-01 00:11:00
8 2017-01-01 00:13:00
9 2017-01-01 00:14:00