group_min = org_kernel_log.groupby(['serial_number','collect_time'],as_index=False).agg('sum')

这是一个使用 pandas 库中的 groupby 方法对 org_kernel_log 数据集按照 serial_number 和 collect_time 进行分组，并对每组数据进行求和操作的语句。其中，groupby 方法的 as_index 参数设置为 False，表示不将分组依据的列作为索引。agg 方法中的 'sum' 表示对每组数据进行求和操作。最终返回一个新的 DataFrame 对象 group_min。

相关问题

select DISTINCT COLLECT_TIME, PERF_VALUE, t1.unit, RULEA, RULEB, RULEC from ( select COLLECT_TIME, PERF_VALUE, UNIT as unit, EQP_OBJ_ID, OBJECT_TYPE, INDEX_TYPE, DEVICE_ID, INDEX_NAME from t_perf_sensor_history tpsh, t_perf_defined tpd where PERF_OBJ_ID = tpd.OBJ_ID ) as t1 left join t_alarm_rule tar on IF(tar.ALARM_OBJECT_TYPE = t1.DEVICE_ID, tar.ALARM_OBJECT_TYPE = t1.DEVICE_ID, tar.ALARM_OBJECT_TYPE = OBJECT_TYPE) where 1=1 and COLLECT_TIME BETWEEN DATE_SUB(NOW(), INTERVAL 62 DAY) AND now() order by COLLECT_TIME desc 怎末改才能避免重复数据

你可以在select子句中使用DISTINCT关键字去除重复数据，例如：

select DISTINCT COLLECT_TIME, PERF_VALUE, t1.unit, RULEA, RULEB, RULEC 
from ( 
    select COLLECT_TIME, PERF_VALUE, UNIT as unit, EQP_OBJ_ID, OBJECT_TYPE, INDEX_TYPE, DEVICE_ID, INDEX_NAME 
    from t_perf_sensor_history tpsh, t_perf_defined tpd 
    where PERF_OBJ_ID = tpd.OBJ_ID 
) as t1 
left join t_alarm_rule tar on IF(tar.ALARM_OBJECT_TYPE = t1.DEVICE_ID, tar.ALARM_OBJECT_TYPE = t1.DEVICE_ID, tar.ALARM_OBJECT_TYPE = OBJECT_TYPE) 
where 1=1 and COLLECT_TIME BETWEEN DATE_SUB(NOW(), INTERVAL 62 DAY) AND now() 
order by COLLECT_TIME desc

另外，你也可以使用 GROUP BY 子句来去除重复数据，例如：

select COLLECT_TIME, PERF_VALUE, t1.unit, RULEA, RULEB, RULEC 
from ( 
    select COLLECT_TIME, PERF_VALUE, UNIT as unit, EQP_OBJ_ID, OBJECT_TYPE, INDEX_TYPE, DEVICE_ID, INDEX_NAME 
    from t_perf_sensor_history tpsh, t_perf_defined tpd 
    where PERF_OBJ_ID = tpd.OBJ_ID 
) as t1 
left join t_alarm_rule tar on IF(tar.ALARM_OBJECT_TYPE = t1.DEVICE_ID, tar.ALARM_OBJECT_TYPE = t1.DEVICE_ID, tar.ALARM_OBJECT_TYPE = OBJECT_TYPE) 
where 1=1 and COLLECT_TIME BETWEEN DATE_SUB(NOW(), INTERVAL 62 DAY) AND now() 
group by COLLECT_TIME, PERF_VALUE, t1.unit, RULEA, RULEB, RULEC 
order by COLLECT_TIME desc

注意，使用 GROUP BY 语句时需要将 select 子句中的每个非聚合列都包含在 GROUP BY 子句中。

select gmm.mine name mine,sum(number) sum from ( select gdi.mine,MAX(cumulative flow) as number from gs device history 2023 gdh3left join gs device info gdi on gdi.device num = gdh3.device numwhere gdh3.collect time BETWEEN '2023-05-25 23:45:00' AND "2023-05-26 00:00:00' and gdi.state = 1 and gdi.audit = 1group by gdi.device num) aleft join gs mine manage gmm on gmm.mine id = a.mine 0 group by a.mine优化这条sql

首先，这条 SQL 语句存在一些语法错误，可能需要进行修正。以下是修正后的 SQL 语句：

SELECT gmm.mine_name AS mine, SUM(number) AS sum 
FROM (
    SELECT gdi.mine, MAX(cumulative_flow) AS number 
    FROM gs_device_history_2023 gdh3 
    LEFT JOIN gs_device_info gdi ON gdi.device_num = gdh3.device_num 
    WHERE gdh3.collect_time BETWEEN '2023-05-25 23:45:00' AND '2023-05-26 00:00:00' 
        AND gdi.state = 1 AND gdi.audit = 1 
    GROUP BY gdi.device_num
) a 
LEFT JOIN gs_mine_manage gmm ON gmm.mine_id = a.mine 
GROUP BY a.mine

接下来，可以考虑对该 SQL 语句进行优化，以下是一些可能的优化方法：

1. 索引优化：对 `gs_device_history_2023` 表的 `collect_time` 字段、`gs_device_info` 表的 `device_num` 字段、`gs_mine_manage` 表的 `mine_id` 字段建立索引，可以加快查询速度。
2. 子查询优化：将子查询转为临时表，可以避免重复计算。
3. JOIN 优化：使用 INNER JOIN 替代 LEFT JOIN，可以减少 JOIN 表格的大小，加快查询速度。

具体的优化方法需要根据实际情况进行调整。