遍历每个df行,然后将数据帧中的一列解析为每个uniqueid的行

时间:2019-12-05 15:59:56

标签: loops dataframe tuples rows columnsorting

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user_id site_id到达出发区域_访问次数
1 A 2019-11-25T13:19:04.600Z 2019-11-25T13:20:05.308Z [{“ pings”:43,“到达”:“ 2019-11-25T13:19:07.114Z”,“ zone_id” :686,“出发”:“ 2019-11-25T13:19:20.064Z”},{“ pings”:33,“到达”:“ 2019-11-25T13:19:20.064Z”,“ zone_id”:687 ,“出发”:“ 2019-11-25T13:19:29.82Z”},{“ pings”:27,“到达”:“ 2019-11-25T13:19:29.82Z”,“ zone_id”:688,“出发“:” 2019-11-25T13:19:46.0​​65Z“},{” pings“:26,”到达“:” 2019-11-25T13:19:46.0​​65Z“,” zone_id“:690,”出发“ :“” 2019-11-25T13:19:53.093Z“},{” pings“:44,”到达“:” 2019-11-25T13:19:53.093Z“,” zone_id“:691,”出发“:” 2019-11-25T13:20:03.807Z“}]

2 A 2019-11-25T13:20:17.141Z 2019-11-25T13:20:27.879Z [{“ pings”:22,“到达”:“ 2019-11-25T13:20:20.052Z”, “ zone_id”:685,“出发”:“ 2019-11-25T13:20:25.37Z”}]

格式化我想要的数据
user_id site_id到达出发地点Pings到达区离开_区域zone_id

1 A 2019-11-25T13:19:04.600Z 2019-11-25T13:20:05.308Z 43 2019-11-25T13:19:07.114Z 2019-11-25T13:19:20.064Z 686

1 A 2019-11-25T13:19:04.600Z 2019-11-25T13:20:05.308Z 33 2019-11-25T13:19:20.064Z 2019-11-25T13:19:29.82Z 687

1个2019-11-25T13:19:04.600Z 2019-11-25T13:20:05.308Z 27 2019-11-25T13:19:29.82Z 2019-11-25T13:20:03.807Z 691

2 A 2019-11-25T13:20:17.141Z 2019-11-25T13:20:27.879Z 22 2019-11-25T13:20:20.052Z 2019-11-25T13:20:25.37Z 685

0 个答案:

没有答案