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?
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