python – 使用索引值访问Pandas Data Frame行

我有一个非常简单的Pandas数据框,其中包含一个索引(TimedeltaIndex类型)和一个名为TotalVolume的列.

>> print(df)
        TotalVolume
...
09:00:00  143846.153846
09:05:00   84353.846154
09:10:00   46946.153846
09:15:00   46765.384615
09:20:00   53076.923077
09:25:00   31642.307692
09:30:00   48269.230769
...

我希望能够以09:00:00查询这本词典,并获得143846.153846.有关信息,这是索引的结构：

>> print(df.index)
TimedeltaIndex(['07:00:00','07:05:00','07:10:00','07:15:00','07:20:00','07:25:00','07:30:00','07:35:00','07:40:00','07:45:00','07:50:00','07:55:00','08:00:00','08:05:00','08:10:00','08:15:00','08:20:00','08:25:00','08:30:00','08:35:00','08:40:00','08:45:00','08:50:00','08:55:00','09:00:00','09:05:00','09:10:00','09:15:00','09:20:00','09:25:00','09:30:00','09:35:00','09:40:00','09:45:00','09:50:00','09:55:00','10:00:00','10:05:00','10:10:00','10:15:00','10:20:00','10:25:00','10:30:00','10:35:00','10:40:00','10:45:00','10:50:00','10:55:00','11:00:00','11:05:00','11:10:00','11:15:00','11:20:00','11:25:00','11:30:00','11:35:00','11:40:00','11:45:00','11:50:00','11:55:00','12:00:00','12:05:00','12:10:00','12:15:00','12:20:00','12:25:00','12:30:00','12:35:00','12:40:00','12:45:00','12:50:00','12:55:00','13:00:00','13:05:00','13:10:00','13:15:00','13:20:00','13:25:00','13:30:00','13:35:00','13:40:00','13:45:00','13:50:00','13:55:00','14:00:00','14:05:00','14:10:00','14:15:00','14:20:00','14:25:00','14:30:00','14:35:00','14:40:00','14:45:00','14:50:00','14:55:00','15:00:00'],dtype='timedelta64[ns]',freq=None)

当我做,

print(df['09:00:00'])

我有

        TotalVolume
 09:00:00  143846.153846
 09:05:00   84353.846154
 09:10:00   46946.153846
 09:15:00   46765.384615
 09:20:00   53076.923077
 09:25:00   31642.307692
 09:30:00   48269.230769
 09:35:00   35715.384615
 09:40:00   38576.923077
 09:45:00   37211.538462
 09:50:00   41803.846154
 09:55:00   37503.846154

似乎过滤器没有按照我的意愿工作.它在09:05:00正常工作.

什么是最虔诚的方式呢？