Leetcode MEDIUM 3126 - Server Utilization Time CTEs SELF JOIN - Explained by Everyday Data Science
Question: https://leetcode.com/problems/server-...
SQL Schema:
First pinned comment.
Pandas Schema:
data = [[4, '2023-11-29 20:22:50', 'start'], [4, '2023-11-30 04:19:50', 'stop'], [2, '2023-11-18 21:23:09', 'start'], [2, '2023-11-19 00:38:09', 'stop'], [5, '2023-11-27 18:38:16', 'start'], [5, '2023-11-28 02:46:16', 'stop'], [4, '2023-11-27 06:46:42', 'start'], [4, '2023-11-27 08:23:42', 'stop'], [5, '2023-11-11 05:09:45', 'start'], [5, '2023-11-11 06:42:45', 'stop'], [5, '2023-11-27 23:05:58', 'start'], [5, '2023-11-27 23:48:58', 'stop'], [5, '2023-11-14 16:29:34', 'start'], [5, '2023-11-14 19:11:34', 'stop'], [1, '2023-11-02 19:14:15', 'start'], [1, '2023-11-02 20:19:15', 'stop'], [5, '2023-11-03 06:55:30', 'start'], [5, '2023-11-03 11:03:30', 'stop'], [4, '2023-11-02 16:53:28', 'start'], [4, '2023-11-02 22:28:28', 'stop'], [2, '2023-11-20 07:06:41', 'start'], [2, '2023-11-20 15:58:41', 'stop'], [1, '2023-11-22 10:42:35', 'start'], [1, '2023-11-22 19:14:35', 'stop'], [3, '2023-11-23 03:05:39', 'start'], [3, '2023-11-23 10:54:39', 'stop'], [3, '2023-11-15 20:04:15', 'start'], [3, '2023-11-15 21:30:15', 'stop'], [3, '2023-11-12 08:21:42', 'start'], [3, '2023-11-12 17:07:42', 'stop'], [1, '2023-11-02 02:33:24', 'start'], [1, '2023-11-02 12:10:24', 'stop'], [4, '2023-11-14 03:55:29', 'start'], [4, '2023-11-14 08:53:29', 'stop'], [2, '2023-11-04 00:36:53', 'start'], [2, '2023-11-04 00:37:53', 'stop'], [4, '2023-11-08 01:54:18', 'start'], [4, '2023-11-08 09:56:18', 'stop'], [1, '2023-11-24 14:28:11', 'start'], [1, '2023-11-24 21:38:11', 'stop'], [3, '2023-11-20 22:55:03', 'start'], [3, '2023-11-21 02:19:03', 'stop'], [3, '2023-11-12 03:10:22', 'start'], [3, '2023-11-12 08:29:22', 'stop'], [2, '2023-11-02 14:55:46', 'start'], [2, '2023-11-02 16:56:46', 'stop'], [1, '2023-11-09 03:40:44', 'start'], [1, '2023-11-09 03:52:44', 'stop'], [5, '2023-11-16 17:43:28', 'start'], [5, '2023-11-16 20:45:28', 'stop'], [2, '2023-11-08 16:17:24', 'start'], [2, '2023-11-09 00:59:24', 'stop'], [4, '2023-11-25 04:46:06', 'start'], [4, '2023-11-25 11:56:06', 'stop'], [2, '2023-11-05 14:22:34', 'start'], [2, '2023-11-05 21:33:34', 'stop'], [1, '2023-11-02 09:49:22', 'start'], [1, '2023-11-02 15:01:22', 'stop'], [5, '2023-11-08 06:49:10', 'start'], [5, '2023-11-08 13:33:10', 'stop'], [1, '2023-11-22 21:00:23', 'start'], [1, '2023-11-22 22:43:23', 'stop'], [3, '2023-11-06 07:45:50', 'start'], [3, '2023-11-06 15:18:50', 'stop'], [3, '2023-11-02 22:13:17', 'start'], [3, '2023-11-03 03:50:17', 'stop'], [1, '2023-11-14 02:40:20', 'start'], [1, '2023-11-14 03:57:20', 'stop'], [1, '2023-11-08 21:13:00', 'start'], [1, '2023-11-09 01:22:00', 'stop'], [5, '2023-11-21 13:25:22', 'start'], [5, '2023-11-21 19:46:22', 'stop'], [5, '2023-11-16 04:37:55', 'start'], [5, '2023-11-16 12:53:55', 'stop'], [2, '2023-11-06 14:03:26', 'start'], [2, '2023-11-06 17:11:26', 'stop'], [2, '2023-11-11 08:18:54', 'start'], [2, '2023-11-11 12:34:54', 'stop'], [2, '2023-11-07 22:54:01', 'start'], [2, '2023-11-08 08:22:01', 'stop'], [5, '2023-11-23 14:08:03', 'start'], [5, '2023-11-23 22:23:03', 'stop'], [1, '2023-11-04 15:20:41', 'start'], [1, '2023-11-04 16:09:41', 'stop'], [3, '2023-11-05 01:24:32', 'start'], [3, '2023-11-05 10:09:32', 'stop'], [4, '2023-11-09 14:15:29', 'start'], [4, '2023-11-09 18:39:29', 'stop'], [3, '2023-11-28 21:43:00', 'start'], [3, '2023-11-29 06:06:00', 'stop'], [2, '2023-11-18 06:23:14', 'start'], [2, '2023-11-18 07:34:14', 'stop'], [5, '2023-11-09 17:27:02', 'start'], [5, '2023-11-10 03:00:02', 'stop'], [2, '2023-11-21 02:41:57', 'start'], [2, '2023-11-21 07:28:57', 'stop'], [3, '2023-11-08 00:36:12', 'start'], [3, '2023-11-08 01:47:12', 'stop'], [1, '2023-11-15 16:13:39', 'start'], [1, '2023-11-15 20:29:39', 'stop'], [3, '2023-11-03 06:25:01', 'start'], [3, '2023-11-03 11:14:01', 'stop'], [1, '2023-11-18 20:49:55', 'start'], [1, '2023-11-18 23:04:55', 'stop'], [3, '2023-11-13 05:02:21', 'start'], [3, '2023-11-13 12:44:21', 'stop'], [2, '2023-11-23 19:00:11', 'start'], [2, '2023-11-24 04:59:11', 'stop'], [4, '2023-11-20 01:16:38', 'start'], [4, '2023-11-20 01:35:38', 'stop'], [1, '2023-11-13 11:21:08', 'start'], [1, '2023-11-13 11:56:08', 'stop'], [4, '2023-11-21 23:09:45', 'start'], [4, '2023-11-22 00:26:45', 'stop'], [2, '2023-11-04 11:19:31', 'start'], [2, '2023-11-04 16:46:31', 'stop']]
servers = pd.DataFrame(columns=['server_id', 'status_time', 'session_status'], dtype={'server_id': 'int', 'status_time': 'datetime64[ns]', 'session_status': pd.CategoricalDtype(categories=['start', 'stop'])})
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