Python 一次返回多行时，如何修改Datafram中的单行？

我有一个包含两列的数据框，即事务和状态

预期数据帧：

Transaction |   Status
-------------------------
57230477    |   Completed
57232288    |   Completed
57232288    | 
57232288    | 
57228666    |   Completed
57229869    |   Completed
57233318    |   Completed
57233318    |
57227149    |   Completed
57227149    |
57222266    |   Completed
57222266    |
57222266    |
57233319    |   Completed
57233319    |
57230490    |   Completed

我的代码中发生的是：

for txn in df['Transaction'].unique():
    df.loc[df['Transaction'] == txn, 'Status'] = 'Completed'

在本例中，所发生的是，它将所有行中的状态分配为“已完成”

我得到的是：

Transaction |   Status
-------------------------
57230477    |   Completed
57232288    |   Completed
57232288    |   Completed
57232288    |   Completed
57228666    |   Completed
57229869    |   Completed
57233318    |   Completed
57233318    |   Completed
57227149    |   Completed
57227149    |   Completed
57222266    |   Completed
57222266    |   Completed
57222266    |   Completed
57233319    |   Completed
57233319    |   Completed
57230490    |   Completed

因此，我的问题是，我如何只将Status as Completed的值分配给事务的第一次出现（如顶部的预期数据框中），即只将值分配给唯一的事务并跳过重复的事务

例如，57232288重复3次，而不是分配已完成的3次，在第一次出现时只分配一次值。

一种方法是使用drop_duplicates并获取索引，然后直接分配：

df.loc[df.drop_duplicates(keep="first").index, "Status"] = "Completed"
print (df)

    Transaction     Status
0      57230477  Completed
1      57232288  Completed
2      57232288           
3      57232288           
4      57228666  Completed
5      57229869  Completed
6      57233318  Completed
7      57233318           
8      57227149  Completed
9      57227149           
10     57222266  Completed
11     57222266           
12     57222266           
13     57233319  Completed
14     57233319           
15     57230490  Completed

按~英寸与反转遮罩一起使用：

df.loc[~df['Transaction'].duplicated(), "Status"] = "Completed"
print (df)
    Transaction     Status
0      57230477  Completed
1      57232288  Completed
2      57232288        NaN
3      57232288        NaN
4      57228666  Completed
5      57229869  Completed
6      57233318  Completed
7      57233318        NaN
8      57227149  Completed
9      57227149        NaN
10     57222266  Completed
11     57222266        NaN
12     57222266        NaN
13     57233319  Completed
14     57233319        NaN
15     57230490  Completed