本文介绍了 pandas :加入有条件的数据框的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！
问题描述
所以我有这个数据框(如下所示)，我试图通过将其复制到另一个 df 来加入自己.加盟条件如下；加盟条件:
So I have this dataframe (as below), I am trying to join itself by copying it into another df. The join condition as below; Join condition:
- PERSONID 和 Badge_ID 相同
- 但不同的 SITE_ID1
- 两行之间的时间差应小于 48 小时.
期待
PERSONID Badge_ID Reader_ID1_x SITE_ID1_x EVENT_TS1_x Reader_ID1_y SITE_ID1_x EVENT_TS1_y
2553-AMAGID 4229 141 99 2/1/2016 3:26 145 97 2/1/2016 3:29
2553-AMAGID 4229 248 99 2/1/2016 3:26 145 97 2/1/2016 3:29
2553-AMAGID 4229 145 97 2/1/2016 3:29 251 99 2/1/2016 3:29
2553-AMAGID 4229 145 97 2/1/2016 3:29 291 99 2/1/2016 3:29
这就是我累的地方，制作 df 的副本，然后使用下面的条件过滤每个 df，然后再次加入它们.但是以下条件不起作用:(在读入 df 之前，我在 SQL 中尝试了这个过滤器，但是对于 600k+ 行、带有索引的事件来说，这太慢了.
Here is what I tired, Make a copy of df and then filter each df with this condition like below and then join them back again. But the below condition doesn't work :( I tried this filters in SQL before reading into df but that's too slow for 600k+ rows, event with indexes.
df1 = df1[(df1['Badge_ID']==df2['Badge_ID']) and (df1['SITE_ID1']!=df2['SITE_ID1']) and ((df1['EVENT_TS1']-df2['EVENT_TS1'])<=datetime.timedelta(hours=event_time_diff))]
PERSONID Badge_ID Reader_ID1 SITE_ID1 EVENT_TS1
2553-AMAGID 4229 141 99 2/1/2016 3:26:10 AM
2553-AMAGID 4229 248 99 2/1/2016 3:26:10 AM
2553-AMAGID 4229 145 97 2/1/2016 3:29:56 AM
2553-AMAGID 4229 251 99 2/1/2016 3:29:56 AM
2553-AMAGID 4229 291 99 2/1/2016 3:29:56 AM
2557-AMAGID 4219 144 99 2/1/2016 2:36:30 AM
2557-AMAGID 4219 144 99 2/1/2016 2:40:00 AM
2557-AMAGID 4219 250 99 2/1/2016 2:40:00 AM
2557-AMAGID 4219 290 99 2/1/2016 2:40:00 AM
2557-AMAGID 4219 144 97 2/1/2016 4:02:06 AM
2557-AMAGID 4219 250 99 2/1/2016 4:02:06 AM
2557-AMAGID 4219 290 99 2/1/2016 4:02:06 AM
2557-AMAGID 4219 250 97 2/2/2016 1:36:30 AM
2557-AMAGID 4219 290 99 2/3/2016 2:38:30 AM
2559-AMAGID 4227 141 99 2/1/2016 4:33:24 AM
2559-AMAGID 4227 248 99 2/1/2016 4:33:24 AM
2560-AMAGID 4226 141 99 2/1/2016 4:10:56 AM
2560-AMAGID 4226 248 99 2/1/2016 4:10:56 AM
2560-AMAGID 4226 145 99 2/1/2016 4:33:52 AM
2560-AMAGID 4226 251 99 2/1/2016 4:33:52 AM
2560-AMAGID 4226 291 99 2/1/2016 4:33:52 AM
2570-AMAGID 4261 141 99 2/1/2016 4:27:02 AM
2570-AMAGID 4261 248 99 2/1/2016 4:27:02 AM
2986-AMAGID 4658 145 99 2/1/2016 3:14:54 AM
2986-AMAGID 4658 251 99 2/1/2016 3:14:54 AM
2986-AMAGID 4658 291 99 2/1/2016 3:14:54 AM
2986-AMAGID 4658 144 99 2/1/2016 3:26:30 AM
2986-AMAGID 4658 250 99 2/1/2016 3:26:30 AM
2986-AMAGID 4658 290 99 2/1/2016 3:26:30 AM
4133-AMAGID 6263 142 99 2/1/2016 2:44:08 AM
4133-AMAGID 6263 249 99 2/1/2016 2:44:08 AM
4133-AMAGID 6263 141 34 2/1/2016 2:44:20 AM
4133-AMAGID 6263 248 34 2/1/2016 2:44:20 AM
4414-AMAGID 6684 145 99 2/1/2016 3:08:06 AM
4414-AMAGID 6684 251 99 2/1/2016 3:08:06 AM
4414-AMAGID 6684 291 99 2/1/2016 3:08:06 AM
4414-AMAGID 6684 145 22 2/1/2016 3:19:12 AM
4414-AMAGID 6684 251 22 2/1/2016 3:19:12 AM
4414-AMAGID 6684 291 22 2/1/2016 3:19:12 AM
4414-AMAGID 6684 145 99 2/1/2016 4:14:28 AM
4414-AMAGID 6684 251 99 2/1/2016 4:14:28 AM
4414-AMAGID 6684 291 99 2/1/2016 4:14:28 AM
4484-AMAGID 6837 142 99 2/1/2016 2:51:14 AM
4484-AMAGID 6837 249 99 2/1/2016 2:51:14 AM
4484-AMAGID 6837 141 99 2/1/2016 2:51:26 AM
4484-AMAGID 6837 248 99 2/1/2016 2:51:26 AM
4484-AMAGID 6837 141 99 2/1/2016 3:05:12 AM
4484-AMAGID 6837 248 99 2/1/2016 3:05:12 AM
4484-AMAGID 6837 141 99 2/1/2016 3:08:58 AM
4484-AMAGID 6837 248 99 2/1/2016 3:08:58 AM
推荐答案
试试以下:
# Transform data in first dataframe
df1 = pd.DataFrame(data)
# Save the data in another datframe
df2 = pd.DataFrame(data)
# Rename column names of second dataframe
df2.rename(index=str, columns={'Reader_ID1': 'Reader_ID1_x', 'SITE_ID1': 'SITE_ID1_x', 'EVENT_TS1': 'EVENT_TS1_x'}, inplace=True)
# Merge the dataframes into another dataframe based on PERSONID and Badge_ID
df3 = pd.merge(df1, df2, how='outer', on=['PERSONID', 'Badge_ID'])
# Use df.loc() to fetch the data you want
df3.loc[(df3.Reader_ID1 < df3.Reader_ID1_x) & (df3.SITE_ID1 != df3.SITE_ID1_x) & (pd.to_datetime(df3['EVENT_TS1']) - pd.to_datetime(df3['EVENT_TS1_x'])<=datetime.timedelta(hours=event_time_diff))]
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