Python 工作和维护的日期时间解释；中断时间计算
我从csv中提取数据，并在使用python准备数据后转换为以下格式。 我想进一步准备如下，将其存储为数据库中的表 如下表所示，从0分钟到52分钟的第8小时为其工作时间（状态：1） 从第8小时53分钟到59分钟休息（小吃休息）（状态：2） 我如何转换它Python 工作和维护的日期时间解释；中断时间计算,python,pandas,datetime,Python,Pandas,Datetime,我从csv中提取数据，并在使用python准备数据后转换为以下格式。 我想进一步准备如下，将其存储为数据库中的表 如下表所示，从0分钟到52分钟的第8小时为其工作时间（状态：1） 从第8小时53分钟到59分钟休息（小吃休息）（状态：2） 我如何转换它 Existing +------+-------+------------+------+------+------+----------+--------+--------+-------+-----+ | | plant | d
Existing
+------+-------+------------+------+------+------+----------+--------+--------+-------+-----+
| | plant | date | shop | line | hour | startmin | endmin | status | shift | uph |
+------+-------+------------+------+------+------+----------+--------+--------+-------+-----+
| 8 | HEF1 | 03-01-2020 | E | 1 | 8 | 0 | 52 | 1 | 2 | 25 |
| 9 | HEF1 | 03-01-2020 | E | 1 | 8 | 53 | 59 | 2 | 2 | 25 |
| 10 | HEF1 | 03-01-2020 | E | 1 | 9 | 0 | 59 | 1 | 2 | 25 |
| 11 | HEF1 | 03-01-2020 | E | 1 | 10 | 0 | 59 | 1 | 2 | 25 |
| 9645 | HEF2 | 27-01-2020 | E | 1 | 7 | 0 | 59 | 1 | 1 | 58 |
| 9646 | HEF2 | 27-01-2020 | E | 1 | 8 | 0 | 52 | 1 | 1 | 58 |
| 9647 | HEF2 | 27-01-2020 | E | 1 | 8 | 53 | 59 | 2 | 1 | 58 |
+------+-------+------------+------+------+------+----------+--------+--------+-------+-----+
Required
+-------+---------------------+------+------+------+--------+-------+-----+
| plant | datetime | shop | line | hour | status | shift | uph |
+-------+---------------------+------+------+------+--------+-------+-----+
| HEF1 | 03-01-2020 08:00:00 | E | 1 | 8 | 1 | 2 | 25 |
| HEF1 | 03-01-2020 08:01:00 | E | 1 | 8 | 1 | 2 | 25 |
| HEF1 | 03-01-2020 08:02:00 | E | 1 | 8 | 1 | 2 | 25 |
| HEF1 | 03-01-2020 08:03:00 | E | 1 | 8 | 1 | 2 | 25 |
| HEF1 | 03-01-2020 08:04:00 | E | 1 | 8 | 1 | 2 | 25 |
| HEF1 | 03-01-2020 08:05:00 | E | 1 | 8 | 1 | 2 | 25 |
| HEF1 | 03-01-2020 08:06:00 | E | 1 | 8 | 1 | 2 | 25 |
| HEF1 | 03-01-2020 08:07:00 | E | 1 | 8 | 1 | 2 | 25 |
| HEF1 | 03-01-2020 08:08:00 | E | 1 | 8 | 1 | 2 | 25 |
| HEF1 | 03-01-2020 08:09:00 | E | 1 | 8 | 1 | 2 | 25 |
| HEF1 | 03-01-2020 08:10:00 | E | 1 | 8 | 1 | 2 | 25 |
| HEF1 | 03-01-2020 08:11:00 | E | 1 | 8 | 1 | 2 | 25 |
| HEF1 | 03-01-2020 08:12:00 | E | 1 | 8 | 1 | 2 | 25 |
| HEF1 | 03-01-2020 08:13:00 | E | 1 | 8 | 1 | 2 | 25 |
| HEF1 | 03-01-2020 08:14:00 | E | 1 | 8 | 1 | 2 | 25 |
| HEF1 | 03-01-2020 08:15:00 | E | 1 | 8 | 1 | 2 | 25 |
| HEF1 | 03-01-2020 08:16:00 | E | 1 | 8 | 1 | 2 | 25 |
| HEF1 | 03-01-2020 08:17:00 | E | 1 | 8 | 1 | 2 | 25 |
| HEF1 | 03-01-2020 08:18:00 | E | 1 | 8 | 1 | 2 | 25 |
| HEF1 | 03-01-2020 08:19:00 | E | 1 | 8 | 1 | 2 | 25 |
| HEF1 | 03-01-2020 08:20:00 | E | 1 | 8 | 1 | 2 | 25 |
| HEF1 | 03-01-2020 08:21:00 | E | 1 | 8 | 1 | 2 | 25 |
| HEF1 | 03-01-2020 08:22:00 | E | 1 | 8 | 1 | 2 | 25 |
| HEF1 | 03-01-2020 08:23:00 | E | 1 | 8 | 1 | 2 | 25 |
| HEF1 | 03-01-2020 08:24:00 | E | 1 | 8 | 1 | 2 | 25 |
| HEF1 | 03-01-2020 08:25:00 | E | 1 | 8 | 1 | 2 | 25 |
| HEF1 | 03-01-2020 08:26:00 | E | 1 | 8 | 1 | 2 | 25 |
| HEF1 | 03-01-2020 08:27:00 | E | 1 | 8 | 1 | 2 | 25 |
| HEF1 | 03-01-2020 08:28:00 | E | 1 | 8 | 1 | 2 | 25 |
| HEF1 | 03-01-2020 08:29:00 | E | 1 | 8 | 1 | 2 | 25 |
| HEF1 | 03-01-2020 08:30:00 | E | 1 | 8 | 1 | 2 | 25 |
| HEF1 | 03-01-2020 08:31:00 | E | 1 | 8 | 1 | 2 | 25 |
| HEF1 | 03-01-2020 08:32:00 | E | 1 | 8 | 1 | 2 | 25 |
| HEF1 | 03-01-2020 08:33:00 | E | 1 | 8 | 1 | 2 | 25 |
| HEF1 | 03-01-2020 08:34:00 | E | 1 | 8 | 1 | 2 | 25 |
| HEF1 | 03-01-2020 08:35:00 | E | 1 | 8 | 1 | 2 | 25 |
| HEF1 | 03-01-2020 08:36:00 | E | 1 | 8 | 1 | 2 | 25 |
| HEF1 | 03-01-2020 08:37:00 | E | 1 | 8 | 1 | 2 | 25 |
| HEF1 | 03-01-2020 08:38:00 | E | 1 | 8 | 1 | 2 | 25 |
| HEF1 | 03-01-2020 08:39:00 | E | 1 | 8 | 1 | 2 | 25 |
| HEF1 | 03-01-2020 08:40:00 | E | 1 | 8 | 1 | 2 | 25 |
| HEF1 | 03-01-2020 08:41:00 | E | 1 | 8 | 1 | 2 | 25 |
| HEF1 | 03-01-2020 08:42:00 | E | 1 | 8 | 1 | 2 | 25 |
| HEF1 | 03-01-2020 08:43:00 | E | 1 | 8 | 1 | 2 | 25 |
| HEF1 | 03-01-2020 08:44:00 | E | 1 | 8 | 1 | 2 | 25 |
| HEF1 | 03-01-2020 08:45:00 | E | 1 | 8 | 1 | 2 | 25 |
| HEF1 | 03-01-2020 08:46:00 | E | 1 | 8 | 1 | 2 | 25 |
| HEF1 | 03-01-2020 08:47:00 | E | 1 | 8 | 1 | 2 | 25 |
| HEF1 | 03-01-2020 08:48:00 | E | 1 | 8 | 1 | 2 | 25 |
| HEF1 | 03-01-2020 08:49:00 | E | 1 | 8 | 1 | 2 | 25 |
| HEF1 | 03-01-2020 08:50:00 | E | 1 | 8 | 1 | 2 | 25 |
| HEF1 | 03-01-2020 08:51:00 | E | 1 | 8 | 1 | 2 | 25 |
| HEF1 | 03-01-2020 08:52:00 | E | 1 | 8 | 1 | 2 | 25 |
| HEF1 | 03-01-2020 08:53:00 | E | 1 | 8 | 2 | 2 | 25 |
| HEF1 | 03-01-2020 08:54:00 | E | 1 | 8 | 2 | 2 | 25 |
| HEF1 | 03-01-2020 08:55:00 | E | 1 | 8 | 2 | 2 | 25 |
| HEF1 | 03-01-2020 08:56:00 | E | 1 | 8 | 2 | 2 | 25 |
| HEF1 | 03-01-2020 08:57:00 | E | 1 | 8 | 2 | 2 | 25 |
| HEF1 | 03-01-2020 08:58:00 | E | 1 | 8 | 2 | 2 | 25 |
| HEF1 | 03-01-2020 08:59:00 | E | 1 | 8 | 2 | 2 | 25 |
| HEF1 | 03-01-2020 09:00:00 | E | 1 | 8 | 1 | 2 | 25 |
| HEF1 | 03-01-2020 09:01:00 | E | 1 | 8 | 1 | 2 | 25 |
+-------+---------------------+------+------+------+--------+-------+-----+
首先创建开始和结束时间戳：
df['start_ts'] = pd.to_datetime(df['date'].astype(str) +' '+ df['hour'].astype(str)+':'+df['startmin'].astype(str))
df['end_ts'] = pd.to_datetime(df['date'].astype(str) +' '+ df['hour'].astype(str)+':'+df['endmin'].astype(str))
df['t_range'] = [pd.date_range(start=x[0], end=x[1], freq='min') for x in zip(df['start_ts'], df['end_ts'])]
df.explode('t_range')
根据需要删除和重命名列首先创建开始和结束时间戳：
df['start_ts'] = pd.to_datetime(df['date'].astype(str) +' '+ df['hour'].astype(str)+':'+df['startmin'].astype(str))
df['end_ts'] = pd.to_datetime(df['date'].astype(str) +' '+ df['hour'].astype(str)+':'+df['endmin'].astype(str))
df['t_range'] = [pd.date_range(start=x[0], end=x[1], freq='min') for x in zip(df['start_ts'], df['end_ts'])]
df.explode('t_range')