Query same time value every day in Pandas timeseries

Question

I would like to get the 07h00 value every day, from a multiday DataFrame that has 24 hours of minute data in it each day.

import numpy as np
import pandas as pd

aframe = pd.DataFrame([np.arange(10000), np.arange(10000) * 2]).T
aframe.index = pd.date_range("2015-09-01", periods = 10000, freq = "1min")

aframe.head()
Out[174]: 
                     0  1
2015-09-01 00:00:00  0  0
2015-09-01 00:01:00  1  2
2015-09-01 00:02:00  2  4
2015-09-01 00:03:00  3  6
2015-09-01 00:04:00  4  8

aframe.tail()
Out[175]: 
                        0      1
2015-09-07 22:35:00  9995  19990
2015-09-07 22:36:00  9996  19992
2015-09-07 22:37:00  9997  19994
2015-09-07 22:38:00  9998  19996
2015-09-07 22:39:00  9999  19998

In this 10 000 row DataFrame spanning 7 days, how would I get the 7am value each day as efficiently as possible? Assume I might have to do this for very large tick databases so I value speed and low memory usage highly.

I know I can index with strings such as:

aframe.ix["2015-09-02 07:00:00"]
Out[176]: 
0    1860
1    3720
Name: 2015-09-02 07:00:00, dtype: int64

But what I need is basically a wildcard style query for example

aframe.ix["* 07:00:00"]

Answer 1

You can use indexer_at_time:

>>> locs = aframe.index.indexer_at_time('7:00:00')
>>> aframe.iloc[locs]
                        0      1
2015-09-01 07:00:00   420    840
2015-09-02 07:00:00  1860   3720
2015-09-03 07:00:00  3300   6600
2015-09-04 07:00:00  4740   9480
2015-09-05 07:00:00  6180  12360
2015-09-06 07:00:00  7620  15240
2015-09-07 07:00:00  9060  18120

There's also indexer_between_time if you need select all indices that lie between two particular time of day.

Both of these methods return the integer locations of the desired values; the corresponding rows of the Series or DataFrame can be fetched with iloc, as shown above.