Groupby +有条件的从另一列创建新的

本文介绍了Groupby +有条件的从另一列创建新的的处理方法，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

我正在尝试在新列（ 2nd_visit_date）中捕获用户的 visit_num == 2的日期

I am trying to capture the date of the "visit_num==2" of "users" in a new column ("2nd_visit_date")

下面是代码（包括我要创建的新列）

Here's the code (including the new column I want to create)

df=pd.DataFrame({'user':[1,1,2,2,2,3,3,3,3,3,4,4], 'date':['1995-09-01','1995-09-02','1995-10-03','1995-10-04','1995-10-05','1995-11-07','1995-11-08','1995-11-09','1995-11-10','1995-11-15','1995-12-18','1995-12-20'], 'visit_num':[1,2,1,2,3,1,2,3,4,5,1,2], '2nd_visit_date':['1995-09-02','1995-09-02','1995-10-04','1995-10-04','1995-10-04','1995-11-08','1995-11-08','1995-11-08','1995-11-08','1995-11-08','1995-12-20','1995-12-20']})

所以我得到：

user date visit_num 2nd_visit_date 1 1995-09-01 1 1995-09-02 1 1995-09-02 2 1995-09-02 2 1995-10-03 1 1995-10-04 2 1995-10-04 2 1995-10-04 2 1995-10-05 3 1995-10-04 3 1995-11-07 1 1995-11-08 3 1995-11-08 2 1995-11-08 3 1995-11-09 3 1995-11-08 3 1995-11-10 4 1995-11-08 3 1995-11-15 5 1995-11-08 4 1995-12-18 1 1995-12-20 4 1995-12-20 2 1995-12-20

我尝试了以下代码，但没有用：

I tried the following code, but it did not work:

df["2nd_visit_date"] = df.groupby("user")["date"].transform(df['visit_num']==2)

任何帮助将不胜感激。谢谢。

Any help will be very much appreciated. Thanks.

推荐答案

让我们说这是您原始的 df ：

Let say this is your original df:

df user date visit_num 0 1 1995-09-01 1 1 1 1995-09-02 2 2 2 1995-10-03 1 3 2 1995-10-04 2 4 2 1995-10-05 3 5 3 1995-11-07 1 6 3 1995-11-08 2 7 3 1995-11-09 3 8 3 1995-11-10 4 9 3 1995-11-15 5 10 4 1995-12-18 1 11 4 1995-12-20 2

您可以先为第二次访问创建一个数据框（并更改列名）：

You can first create a dataframe for second visits (and change column name):

df_2 = df[df.visit_num==2][['user', 'date']] df_2.columns = ['user', '2nd_visit_date'] df_2 user 2nd_visit_date 1 1 1995-09-02 3 2 1995-10-04 6 3 1995-11-08 11 4 1995-12-20

并将其与原始 df 合并p>

And merge it with your original df

pd.merge(df, df_2, on='user', how='left') user date visit_num 2nd_visit_date 0 1 1995-09-01 1 1995-09-02 1 1 1995-09-02 2 1995-09-02 2 2 1995-10-03 1 1995-10-04 3 2 1995-10-04 2 1995-10-04 4 2 1995-10-05 3 1995-10-04 5 3 1995-11-07 1 1995-11-08 6 3 1995-11-08 2 1995-11-08 7 3 1995-11-09 3 1995-11-08 8 3 1995-11-10 4 1995-11-08 9 3 1995-11-15 5 1995-11-08 10 4 1995-12-18 1 1995-12-20 11 4 1995-12-20 2 1995-12-20