查找id在每个位置花费的时间

Posted 2023-03-25

【中文标题】查找id在每个位置花费的时间

【英文标题】：Finding the time spent by id in each location

【发布时间】：2017-03-16 03:35:29

【问题描述】：

我试图找出每个 id 在起始位置花费了多长时间。

例如，在下面的数据集中，id 286 的起始 Geohash 是“abcdef”。 Geohash "abcdef" 出现在 ID 286 的 3 个位置。 因此，ID 286 花费的总时间是 (2017-02-13 12:33:02.063 UTC - 2017-02-13 12:24:36 UTC) 和 (2017-02-13 12:34:29 UTC - 2017-02-13 12:33:08 UTC)。

        Id         DateTime                      Latitude     Longitude   Geohash
      0 286        2017-02-13 12:24:36 UTC       40.769230  -73.01205     abcdef
      1 286        2017-02-13 12:33:02.063 UTC   40.769230  -73.01202     abcdef
      2 286        2017-02-13 12:33:05.063 UTC   40.769230  -73.01202     cvzvvv
      3 286        2017-02-13 12:33:08 UTC       40.769280  -73.01212     abcdef
      4 286        2017-02-13 12:34:29 UTC       40.769306  -73.01207     hsffds
      5 368        2017-02-13 00:23:07.063 UTC   33.392820  -111.8262     weruio
      6 141        2017-02-13 00:00:41 UTC       33.287117  -111.84150    oqruqq

不知道pandas dataframe中是否有实现这个操作的函数。

任何帮助将不胜感激。 ！！

【参考方案1】：

以下是 BigQuery 标准 SQL

#standardSQL
SELECT 
  Id, Geohash, MIN(DateTime) AS StartDateTime, SUM(TimeSpent) AS TimeSpent
FROM (
  SELECT 
    Id, Geohash, DateTime, 
    TIMESTAMP_DIFF(LEAD(DateTime) OVER(PARTITION BY Id ORDER BY DateTime), DateTime, SECOND) AS TimeSpent,
    FIRST_VALUE(Geohash) OVER(PARTITION BY Id ORDER BY DateTime) AS FirstGeohash
  FROM yourTable
)
WHERE Geohash = FirstGeohash
GROUP BY Id, Geohash  

您可以使用示例中的虚拟数据对其进行测试：

#standardSQL
WITH yourTable AS (
  SELECT 286 AS Id, TIMESTAMP '2017-02-13 12:24:36 UTC' AS DateTime, 40.769230 AS Latitude, -73.01205 AS Longitude, 'abcdef' AS Geohash UNION ALL
  SELECT 286, TIMESTAMP '2017-02-13 12:33:02.063 UTC', 40.769230, -73.01202, 'abcdef' UNION ALL
  SELECT 286, TIMESTAMP '2017-02-13 12:33:05.063 UTC', 40.769230, -73.01202, 'cvzvvv' UNION ALL
  SELECT 286, TIMESTAMP '2017-02-13 12:33:08 UTC', 40.769280, -73.01212, 'abcdef' UNION ALL
  SELECT 286, TIMESTAMP '2017-02-13 12:34:29 UTC', 40.769306, -73.01207, 'hsffds' UNION ALL
  SELECT 368, TIMESTAMP '2017-02-13 00:23:07.063 UTC', 33.392820, -111.8262, 'weruio' UNION ALL
  SELECT 141, TIMESTAMP '2017-02-13 00:00:41 UTC', 33.287117, -111.84150, 'oqruqq'
)
SELECT 
  Id, Geohash, MIN(DateTime) AS StartDateTime, SUM(TimeSpent) AS TimeSpent
FROM (
  SELECT 
    Id, Geohash, DateTime, 
    TIMESTAMP_DIFF(LEAD(DateTime) OVER(PARTITION BY Id ORDER BY DateTime), DateTime, SECOND) AS TimeSpent,
    FIRST_VALUE(Geohash) OVER(PARTITION BY Id ORDER BY DateTime) AS FirstGeohash
  FROM yourTable
)
WHERE Geohash = FirstGeohash
GROUP BY Id, Geohash  

结果如下

Id  Geohash     StartDateTime           TimeSpent    
286  abcdef     2017-02-13 12:24:36 UTC       590    
368  weruio     2017-02-13 00:23:07 UTC      null    
141  oqruqq     2017-02-13 00:00:41 UTC      null    

请注意：590 以上是三页上花费的时间总和（以秒为单位） - 不仅仅是你问题中所述的两页 - 我认为这只是你这边的错字

【参考方案2】：

如果我理解正确，你想要这样的东西：

def timedelta(df):
    df = df.sort_values(by='DateTime')
    return df.iloc[0]['DateTime'] - df.iloc[-1]['DateTime']

df.groupby(['Id', 'Geohash']).apply(timedelta)