Question

我一直在寻找一种方法，根据要检查的条件在另一行中从数据框中删除行。

这是我的数据框：

product product_id  account_status
prod-A  100         active
prod-A  100         cancelled
prod-A  300         active
prod-A  400         cancelled

如果针对产品＆和product_id组合存在account_status ='active'的行，则保留该行并删除其他行。

所需的输出是：

product product_id  account_status
prod-A  100         active
prod-A  300         active
prod-A  400         cancelled

我看到解决方案提到了here，但无法将其复制为字符串。

请提出建议。

Answer 1

IMO，groupby不是必需的（我这样说是因为您已经相应地标记了您的问题），可以利用sort_values和drop_duplicates，利用“活动” <“已取消”，按字典顺序：

(df.sort_values(['account_status'])
   .drop_duplicates(['product', 'product_id'])
   .sort_index())

  product  product_id account_status
0  prod-A         100         active
2  prod-A         300         active
3  prod-A         400      cancelled

本着其他答案保持一致的精神，您可能希望看一下涉及groupby和掩蔽的基于duplicated的解决方案。

df
  product  product_id account_status
0  prod-A         100         active
1  prod-A         100      cancelled
2  prod-A         100        pending
3  prod-A         300         active
4  prod-A         300        pending
5  prod-A         400      cancelled
6  prod-A         500         active
7  prod-A         500         active
8  prod-A         600        pending
9  prod-A         600      cancelled


m1 = (df.assign(m=df.account_status.eq('active'))
        .groupby(['product', 'product_id'])['m']
        .transform('any'))
m2 = df.duplicated(['product', 'product_id'])

df[~(m1 & m2)]

  product  product_id account_status
0  prod-A         100         active
3  prod-A         300         active
5  prod-A         400      cancelled
6  prod-A         500         active
8  prod-A         600        pending
9  prod-A         600      cancelled

与其他解决方案一样，这也将“很好地”推广到多个类别，并且仅在还存在“活动”的组中删除与其他状态相对应的行。

Answer 2

对于一般解决方案，如果每个组中至少存在一个account_status值，则每个组仅删除另一个active值：

print (df)
  product  product_id account_status
0  prod-A         100         active
1  prod-A         100      cancelled <- necessary remove
2  prod-A         300         active
3  prod-A         400      cancelled
4  prod-A         500         active
5  prod-A         500         active
6  prod-A         600      cancelled
7  prod-A         600      cancelled

s = df['account_status'].eq('active')
g = df.assign(A=s).groupby(['product','product_id'])['A']
mask = ~g.transform('any') | g.transform('all') | s
df = df[mask]
print (df)
  product  product_id account_status
0  prod-A         100         active
2  prod-A         300         active
3  prod-A         400      cancelled
4  prod-A         500         active
5  prod-A         500         active
6  prod-A         600      cancelled
7  prod-A         600      cancelled

在多个类别上也能很好地工作：

print (df)
  product  product_id account_status
0  prod-A         100         active
1  prod-A         100      cancelled <- necessary remove
2  prod-A         100        pending <- necessary remove
3  prod-A         300         active
4  prod-A         300        pending <- necessary remove
5  prod-A         400      cancelled
6  prod-A         500         active
7  prod-A         500         active
8  prod-A         600        pending
9  prod-A         600      cancelled

s = df['account_status'].eq('active')
g = df.assign(A=s).groupby(['product','product_id'])['A']
mask = ~g.transform('any') | g.transform('all') | s
df = df[mask]
print (df)
  product  product_id account_status
0  prod-A         100         active
3  prod-A         300         active
5  prod-A         400      cancelled
6  prod-A         500         active
7  prod-A         500         active
8  prod-A         600        pending
9  prod-A         600      cancelled

根据其他行中的值删除行

2 个答案: