熊猫有条件申请

里卡多·费尔南德斯

我有状态重复的客户重复记录，因为每个客户订阅/产品都有一行。我想new_status为客户生成一个“取消”，每个订阅状态必须一起“取消”。

我用了：

df['duplicated'] = df.groupby('customer', as_index=False)['customer'].cumcount()

分隔索引中的每个重复项以指示重复值

Customer | Status | new_status | duplicated
 X       |canceled|            | 0
 X       |canceled|            | 1
 X       |active  |            | 2
 Y       |canceled|            | 0
 A       |canceled|            | 0
 A       |canceled|            | 1
 B       |active  |            | 0
 B       |canceled|            | 1

因此，我想使用.apply和/或.loc生成：

Customer | Status | new_status | duplicated
 X       |canceled|            | 0
 X       |canceled|            | 1
 X       |active  |            | 2
 Y       |canceled|            | 0
 A       |canceled| canceled   | 0
 A       |canceled| canceled   | 1
 B       |active  |            | 0
 B       |canceled|            | 1

据我了解，您可以尝试做：

df['new_status']=(df.groupby('Customer')['Status'].
  transform(lambda x: x.eq('canceled').all()).map({True:'cancelled'})).fillna(df.new_status)
print(df)

    Customer    Status new_status  duplicated
0   X         canceled             0         
1   X         canceled             1         
2   X         active               2         
3   Y         canceled  cancelled  0         
4   A         canceled  cancelled  0         
5   A         canceled  cancelled  1         
6   B         active               0         
7   B         canceled             1

由于预期的o / p已更改，因此进行了编辑：

df['new_status']=(df.groupby('Customer')['Status'].
             transform(lambda x: x.duplicated(keep=False)&(x.eq('canceled').all()))
                         .map({True:'cancelled',False:''}))
print(df)

  Customer    Status new_status  duplicated
0   X         canceled             0         
1   X         canceled             1         
2   X         active               2         
3   Y         canceled             0         
4   A         canceled  cancelled  0         
5   A         canceled  cancelled  1         
6   B         active               0         
7   B         canceled             1

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