title: Count occurrences in custom time frames
body: <p>I am currently working with session logs and are interested in counting the number of occurrences of specific events in a custom time frame (first 1 [5, 10, after 10]) minute. To simplify: the start of a session is defined as the time of the first occurrence of a relevant event.
  I have already filtered the sessions by only the relevant events and the dataframe looks similar to this.</p>
  <p><strong>Input</strong></p>
  <pre><code>import pandas as pd
  data_in = {'SessionId': ['A', 'A', 'A', 'A', 'A', 'B', 'B', 'B', 'C', 'C'],
          'Timestamp': ['2020-08-24 12:46:30.726000+00:00', '2020-08-24 12:46:38.726000+00:00', '2020-08-24 12:49:30.726000+00:00', '2020-08-24 12:50:49.726000+00:00', '2020-08-24 12:58:30.726000+00:00', '2021-02-12 16:12:12.726000+00:00', '2021-02-12 16:15:24.726000+00:00', '2021-02-12 16:31:07.726000+00:00', '2020-12-03 23:58:17.726000+00:00', '2020-12-04 00:03:44.726000+00:00'],
          'event': ['match', 'match', 'match', 'match', 'match', 'match', 'match', 'match', 'match', 'match']
  }
  df_in = pd.DataFrame(data_in)
  df_in
  </code></pre>
  <p><strong>Desired Output:</strong></p>
  <pre><code>data_out = {'SessionId': ['A', 'B', 'C'],
          '#events_first_1_minute': [2, 1, 1],
          '#events_first_5_minute': [4, 2, 1],
          '#events_first_10_minute': [4, 2, 2],
          '#events_after_10_minute': [5, 3, 2]
  }
  df_out = pd.DataFrame(data_out)
  df_out
  </code></pre>
  <p>I already played around with groupby and pd.Grouper. I get the number of relevant events per session in total, but I don´t see any option for custom time bins. Another idea was also to get rid of the date part and focus only on the time, but there are of course also sessions that started on a day and ended on the other (SessionId: C).<br>
  Any help is appreciated!</p>