python - filter/select rows of pandas dataframe by timestamp column

Question

I am new to pandas. I have dataframe with two columns dt (date-time stamp) and value.

Given two start and end data-time stamps: is there a easy way to create a new dataframe from original one that contains rows between the two date-time stamp?

                dt    value
84    7/23/2014 7:00  0.300
85    7/23/2014 7:05  0.300
86    7/23/2014 7:10  0.312
87    7/23/2014 7:15  0.300
88    7/23/2014 7:20  0.300
89    7/23/2014 7:25  0.300
90    7/23/2014 7:30  0.300
91    7/23/2014 7:35  0.300
92    7/23/2014 7:40  0.300
93    7/23/2014 7:45  0.216
94    7/23/2014 7:50  0.204
95    7/23/2014 7:55  0.228
96    7/23/2014 8:00  0.228
97    7/23/2014 8:05  0.228
98    7/23/2014 8:10  0.228
99    7/23/2014 8:15  0.240
100   7/23/2014 8:20  0.228
101   7/23/2014 8:25  0.216
102   7/23/2014 8:30  0.228
103   7/23/2014 8:35  0.324
104   7/23/2014 8:40  0.336
105   7/23/2014 8:45  0.324
106   7/23/2014 8:50  0.324
107   7/23/2014 8:55  0.324
108   7/23/2014 9:00  0.252
109   7/23/2014 9:05  0.252
110   7/23/2014 9:10  0.240
111   7/23/2014 9:15  0.240
112   7/23/2014 9:20  0.252
113   7/23/2014 9:25  0.240
..               ...    ...
198  7/23/2014 16:30  0.132
199  7/23/2014 16:35  0.120
200  7/23/2014 16:40  0.180
201  7/23/2014 16:45  0.216
202  7/23/2014 16:50  0.204
203  7/23/2014 16:55  0.192

Answer 1

So long as dt is a datetime dtype already you can filter using date strings, if not then you can convert doing this:

df['dt'] = pd.to_datetime(df['dt'])

Then filter:

In [115]:

df[(df['dt'] > '2014-07-23 07:30:00') & (df['dt'] < '2014-07-23 09:00:00')]
Out[115]:
                       dt  value
index                           
91    2014-07-23 07:35:00  0.300
92    2014-07-23 07:40:00  0.300
93    2014-07-23 07:45:00  0.216
94    2014-07-23 07:50:00  0.204
95    2014-07-23 07:55:00  0.228
96    2014-07-23 08:00:00  0.228
97    2014-07-23 08:05:00  0.228
98    2014-07-23 08:10:00  0.228
99    2014-07-23 08:15:00  0.240
100   2014-07-23 08:20:00  0.228
101   2014-07-23 08:25:00  0.216
102   2014-07-23 08:30:00  0.228
103   2014-07-23 08:35:00  0.324
104   2014-07-23 08:40:00  0.336
105   2014-07-23 08:45:00  0.324
106   2014-07-23 08:50:00  0.324
107   2014-07-23 08:55:00  0.324