在多个条件下左连接两个熊猫数据框

问题描述

我有两个数据框。一个是在网上商店中用户的搜索查询（102377行），另一个是用户在搜索中的点击次数（8004行）。

    queries:
index   term                         timestamp
...
10      tight                        2018-09-27 20:09:23
11      differential pressure        2018-09-27 20:09:30
12      soot pump                    2018-09-27 20:09:32
13      gas pressure                 2018-09-27 20:09:46
14      case                         2018-09-27 20:11:29
15      backpack                     2018-09-27 20:18:35
...

clicks
    index   term             timestamp               artnr
    ...
    245     soot pump        2018-09-27 20:09:25    9150.0
    246     dungarees        2018-09-27 20:10:38    7228.0
    247     db23             2018-09-27 20:10:40    7966.0
    248     db23             2018-09-27 20:10:55    7971.0
    249     sealing blister  2018-09-27 20:12:05    7971.0
    250     backpack         2018-09-27 20:18:40    8739.0
    ...

我想做的是将点击添加到查询中。如果querys.term等于clicks.term，并且clicks.timestamp与querys.timestamp之间的差在10秒以下且在0秒以上，则应将clicks数据框的条件替换为clicks数据框的artnr，使其看起来像：

    queries:
index   term                         timestamp
...
10      tight                        2018-09-27 20:09:23
11      differential pressure        2018-09-27 20:09:30
12      9150.0                       2018-09-27 20:09:32
13      gas pressure                 2018-09-27 20:09:46
14      case                         2018-09-27 20:11:29
15      8739.0                       2018-09-27 20:18:35
...

我的第一种方法是：

df_Q['term'] = np.where(((((df_CS.timestamp-df_Q.timestamp).dt.total_seconds() <= 10.0) & 
                       (df_CS.timestamp-df_Q.timestamp).dt.total_seconds() >= 0) & 
                       (df_CS.term.str == df_Q.term.str)),df_CS['artnr'],df_CS['term'])

但这只会产生以下错误：

ValueError：操作数不能与形状一起广播 （102377，）（8004，）（8004，）

有人知道如何通过左联接或其他解决方案来解决这个问题吗？

解决方法

queries = pd.DataFrame({'term': ['tight','differential pressure','soot pump','gas pressure','case','backpack'],'timestamp': ['2018-09-27 20:09:23','2018-09-27 20:09:30','2018-09-27 20:09:32','2018-09-27 20:09:46','2018-09-27 20:11:29','2018-09-27 20:18:35']})
print(queries)
                    term            timestamp
0                  tight  2018-09-27 20:09:23
1  differential pressure  2018-09-27 20:09:30
2              soot pump  2018-09-27 20:09:32
3           gas pressure  2018-09-27 20:09:46
4                   case  2018-09-27 20:11:29
5               backpack  2018-09-27 20:18:35

clicks = pd.DataFrame({'term': ['soot pump','dungarees','db23','sealing blister','timestamp': ['2018-09-27 20:09:25','2018-09-27 20:10:38','2018-09-27 20:10:40','2018-09-27 20:10:55','2018-09-27 20:12:05','2018-09-27 20:18:40'],'artnr':[9150.0,7228.0,7966.0,7971.0,8739.0]})
print(clicks)
              term            timestamp   artnr
0        soot pump  2018-09-27 20:09:25  9150.0
1        dungarees  2018-09-27 20:10:38  7228.0
2             db23  2018-09-27 20:10:40  7966.0
3             db23  2018-09-27 20:10:55  7971.0
4  sealing blister  2018-09-27 20:12:05  7971.0
5         backpack  2018-09-27 20:18:40  8739.0

首先，对时间戳上的两个数据帧进行排序

queries['timestamp'] = pd.to_datetime(queries['timestamp'])
clicks['timestamp'] = pd.to_datetime(clicks['timestamp'])

queries.sort_values('timestamp',ascending=True,inplace=True)
clicks.sort_values('timestamp',inplace=True)

然后仅当“时间戳记”的时差在10秒以内时，才使用pd.merge_asof（）加入“期限”列。

df = pd.merge_asof(
     queries,# left data
     clicks,# right data
     on="timestamp",# column to check time differnece
     by="term",# column to join on
     tolerance=pd.Timedelta("10s"),# time difference
     direction='forward',# join only if timestamp in right data after timestamp in left data
     )

如果未找到匹配项，则“ artnr”列将为NA。因此，请使用'artnr'的非NA值替换'term'

df['term'][df['artnr'].notna()] = df['artnr']
print(df)

                    term           timestamp   artnr
0                  tight 2018-09-27 20:09:23     NaN
1  differential pressure 2018-09-27 20:09:30     NaN
2              soot pump 2018-09-27 20:09:32     NaN
3           gas pressure 2018-09-27 20:09:46     NaN
4                   case 2018-09-27 20:11:29     NaN
5                   8739 2018-09-27 20:18:35  8739.0