我有不同狀態的客戶重復，因為每個客戶訂閱/產品都有一行。我想new_status為客戶生成一個并且要“取消”它，每個訂閱狀態都必須一起“取消”。我用了：df['duplicated'] = df.groupby('customer', as_index=False)['customer'].cumcount()將索引中的每個重復項分開以指示重復值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 來生成：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
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千萬里不及你
TA貢獻1784條經驗 獲得超9個贊
通過比較列Series.eq的==，并使用GroupBy.transform與GroupBy.all用于檢查是否所有值都True按組S，然后比較Customer通過Series.duplicated與keep=False退貨所有受騙者。最后按位AND( &)鏈接在一起，并通過numpy.where以下方式設置值：
m1 = df['Status'].eq('canceled').groupby(df['Customer']).transform('all')
m2 = df['Customer'].duplicated(keep=False)
df['new_status'] = np.where(m1 & m2, 'cancelled', '')
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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