大熊猫：根据多个条件计算新列中的新值，适用于多个列而不循环

debugcn 发表于 Dev

Ratchainant Thammasudjarit

我的数据在以下数据框中

df = pd.DataFrame({'AccID':['001','001','001','002','002','003'],
                   'AccTypes':['A','B','C','A','B','C'],
                   'Status':['Closed','Active','Active','Active','Closed','Active'],
                   'Years':[5,15,10,20,25,30]})

AccID     AccTypes     Status     Years
001       A            Closed     5
001       B            Active     15
001       C            Active     10
002       A            Active     20
002       B            Closed     25
003       C            Active     30

我想创建另一个名为“ ActiveYears”的列，该列的每个值都是给定活动AccID的最大活动年份，而不管AccTypes如何。预期的输出如下：

AccID     AccTypes     Status     Years     ActiveYears     Explanations
001       A            Closed     5         5               # Status = Closed, we set ActiveYears = Years
001       B            Active     15        15              # Status = Active, we select the maximum year of AccID = 001 with active status
001       C            Active     10        15              # Status = Active, we select the maximum year of AccID = 001 with active status
002       A            Active     20        20              # Status = Active, we select the maximum year of AccID = 002 with active status
002       B            Closed     25        20              # Status = Closed, we set ActiveYears = Years
003       C            Active     30        30              # Status = Active, we select the maximum year of AccID = 003 with active status

我可以通过循环来做到这一点，但这还不够优雅。我可以知道比循环更好的方法吗？谢谢你。

朱利安·马雷克（Julien Marrec）

您可以使用以下内容：

首先处理身份Closed：

df.loc[df.Status == 'Closed','ActiveYears'] = df.loc[df.Status == 'Closed','Years']

使用groupby转换来处理主动状态：

df.loc[df.Status == 'Active', 'ActiveYears'] = df[df.Status == 'Active'].groupby('AccID')['Years'].transform(max)

print(df)

  AccID AccTypes  Status  Years  ActiveYears
0   001        A  Closed      5          5.0
1   001        B  Active     15         15.0
2   001        C  Active     10         15.0
3   002        A  Active     20         20.0
4   002        B  Closed     25         25.0
5   003        C  Active     30         30.0

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