python - 当来自多个列的条件在 python 中匹配时添加一行？

Question

我正在尝试在数据框中添加一行。条件是当用户再次返回应用程序(300 秒后)时，我需要添加一行。下面是我的代码。它工作正常但需要大量执行时间，因为真实数据框有 1000 万行。

for i in range(1,len(df)):
    if df['user_id'][i]==df['user_id'][i-1] and (df['start_time'][i]-df['start_time'][i-1]).seconds>300:
        df.loc[len(df)]=[df['user_id'][i],df['start_time'][i],'psuedo_App_start_2']

输入:

user_id   start_time        event
100       03/04/19 6:11     psuedo_App_start
100       03/04/19 6:11     notification_receive
100       03/04/19 8:56     notification_dismiss
10        03/04/19 22:05    psuedo_App_start
10        03/04/19 22:05    subcategory_click
10        03/04/19 22:06    subcategory_click

输出应该是这样的:

user_id   start_time        event
100       03/04/19 6:11     psuedo_App_start
100       03/04/19 6:11     notification_receive
100       03/04/19 8:56     psuedo_App_start_2
100       03/04/19 8:56     notification_dismiss
10        03/04/19 22:05    psuedo_App_start
10        03/04/19 22:05    subcategory_click
10        03/04/19 22:06    subcategory_click

从输出中可以看出，为 user_id = 100 添加了一行，因为他在 8.56 回来，即 300 秒后。

Answer 1

首先按 2 个条件过滤 - 比较 user_id by DataFrameGroupBy.shift每组的编辑值，以及每组的差异 DataFrameGroupBy.diff ，然后通过 DataFrame.assign 重新分配 evet 列, 最后 concat在一起并按 DataFrame.sort_values 排序:

#MM/DD/YY HH:MM
#df['start_time'] = pd.to_datetime(df['start_time'])
#DD/MM/YY HH:MM
#df['start_time'] = pd.to_datetime(df['start_time'], dayfirst=True)

m1 = df['user_id'].eq(df.groupby('user_id')['user_id'].shift())
m2 = df.groupby('user_id')['start_time'].diff().dt.total_seconds() > 300

df1 = df[m1 & m2].assign(event='psuedo_App_start_2')

df1 = (pd.concat([df, df1], ignore_index=True)
         .sort_values(['user_id','start_time'], ascending=[False, True]))
print (df1)
   user_id          start_time                 event
0      100 2019-03-04 06:11:00      psuedo_App_start
1      100 2019-03-04 06:11:00  notification_receive
2      100 2019-03-04 08:56:00  notification_dismiss
6      100 2019-03-04 08:56:00    psuedo_App_start_2
3       10 2019-03-04 22:05:00      psuedo_App_start
4       10 2019-03-04 22:05:00     subcategory_click
5       10 2019-03-04 22:06:00     subcategory_click