如何从熊猫的列中拆分数据？

我认为如果用strings填充第二级，则需要slicers：

idx = pd.IndexSlice
df1 = df.loc[idx[:, '00:00:00'],:]
print (df1)
                       col
Date       Time           
2017-12-01 00:00:00  21.64
2017-12-02 00:00:00  22.78

df2 = df.loc[idx[:, '00:15:00':'03:00:00'], :]
print (df2)
                       col
Date       Time           
2017-12-01 00:15:00  21.72
           00:30:00  21.57
           00:45:00  21.47
           01:00:00  21.42
           01:15:00  21.44
           01:30:00  21.48
           01:45:00  21.32
           02:00:00  21.27
           02:15:00  21.29
           02:30:00  21.20
           02:45:00  21.18
           03:00:00  21.19
2017-12-02 00:15:00  22.67
           00:30:00  22.54
           00:45:00  22.55

另一种解决方案是按遮罩过滤：

mask = df.index.get_level_values(1) =='00:00:00'
df1 = df[mask]
print (df1)
                       col
Date       Time           
2017-12-01 00:00:00  21.64
2017-12-02 00:00:00  22.78

df2 = df[~mask]

如果第二级是python乘以chane值进行比较：

import datetime

idx = pd.IndexSlice
df1 = df.loc[idx[:, datetime.time(0, 0)],:]
print (df1)
                       col
2017-12-01 00:00:00  21.64
2017-12-02 00:00:00  22.78

df2 = df.loc[idx[:, datetime.time(0, 15, 0):datetime.time(3, 0, 0)], :]
print (df2)
                       col
2017-12-01 00:15:00  21.72
           00:30:00  21.57
           00:45:00  21.47
           01:00:00  21.42
           01:15:00  21.44
           01:30:00  21.48
           01:45:00  21.32
           02:00:00  21.27
           02:15:00  21.29
           02:30:00  21.20
           02:45:00  21.18
           03:00:00  21.19
2017-12-02 00:15:00  22.67
           00:30:00  22.54
           00:45:00  22.55

mask = df.index.get_level_values(1) == datetime.time(0, 0)
df1 = df[mask]

df2 = df[~mask]