I have the following dataframe:

```
import pandas as pd
index = pd.date_range('2013-1-1',periods=10,freq='15Min')
data = pd.DataFrame(data=[1,2,3,4,5,6,7,8,9,0], columns=['value'], index=index)

```

How can I generate a mask based on the index value? I know I can do something like:

```
data['value'] > 3
Out[40]: 
2013-01-01 00:00:00    False
2013-01-01 00:15:00    False
2013-01-01 00:30:00    False
2013-01-01 00:45:00     True
2013-01-01 01:00:00     True
2013-01-01 01:15:00     True
2013-01-01 01:30:00     True
2013-01-01 01:45:00     True
2013-01-01 02:00:00     True
2013-01-01 02:15:00    False
Freq: 15T, Name: value, dtype: bool

```

I want to generate a mask to only consider some rows where the index is in a certain range. I was thinking of doing something like `data['index'].time() > datetime.time(1,15)` to generate a mask. Except of course `data['index']` fails because index is not the name of a column. How can you reference the index value for a row in a mask?