Group all rows that fall in the same time bin in pandas

I am trying to define which apps were used in different sessions. Basically, I am defining every 5 minutes as a session and would like to know how many mobile sessions are there in the dataset. Also, I would like to know which apps were launched in each session. All the rows in my data frame are time stamped. Here is an example from the dataset:
        timestamp               App
6773    2018-04-08 09:47:57.849 Chrome
6774    2018-04-08 09:48:17.573 YouTube
6775    2018-04-08 09:48:28.538 Instagram
6776    2018-04-08 09:48:37.381 Maps
6777    2018-04-08 09:48:46.680 Netflix
6778    2018-04-08 09:48:56.672 Google Play Store
6779    2018-04-08 09:56:58.880 Google
6780    2018-04-08 09:57:25.461 DB Navigator
6781    2018-04-08 11:28:38.762 Google
6782    2018-04-08 12:58:31.455 Google
6783    2018-04-08 14:31:18.131 Google
6784    2018-04-08 14:31:29.209 Google
6785    2018-04-08 14:58:42.875 Google
6786    2018-04-08 18:18:04.757 Chrome
6787    2018-04-08 21:08:41.368 Google
6788    2018-04-11 10:53:10.744 Google
6789    2018-04-14 19:54:37.441 Google
6790    2018-04-14 19:54:59.833 Google
6791    2018-04-14 19:55:10.844 YouTube
6792    2018-04-14 19:55:34.486 Google
6793    2018-04-14 20:23:00.315 Google
6794    2018-04-15 08:23:44.873 Google
6795    2018-04-15 08:24:07.257 Google
This is the desired output, where a new column named BinID is added defining the id of the current session.
        timestamp               App                     SessionID
6773    2018-04-08 09:47:57.849 Chrome                  1
6774    2018-04-08 09:48:17.573 YouTube                 1
6775    2018-04-08 09:48:28.538 Instagram               1
6776    2018-04-08 09:48:37.381 Maps                    1
6777    2018-04-08 09:48:46.680 Netflix                 1
6778    2018-04-08 09:48:56.672 Google Play Store       1
6779    2018-04-08 09:56:58.880 Google                  2
6780    2018-04-08 09:57:25.461 DB Navigator            2
6781    2018-04-08 11:28:38.762 Google                  3
6782    2018-04-08 12:58:31.455 Google                  4
6783    2018-04-08 14:31:18.131 Google                  5
6784    2018-04-08 14:31:29.209 Google                  5
6785    2018-04-08 14:58:42.875 Google                  6
6786    2018-04-08 18:18:04.757 Chrome                  7
6787    2018-04-08 21:08:41.368 Google                  8
6788    2018-04-11 10:53:10.744 Google                  9
6789    2018-04-14 19:54:37.441 Google                  10
6790    2018-04-14 19:54:59.833 Google                  10
6791    2018-04-14 19:55:10.844 YouTube                 10
6792    2018-04-14 19:55:34.486 Google                  10
6793    2018-04-14 20:23:00.315 Google                  11
6794    2018-04-15 08:23:44.873 Google                  12
6795    2018-04-15 08:24:07.257 Google                  12  

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