Python：删除数据框的行并保留特定组

Question

问题还是没有回答！！！

假设我有这个数据框：

将熊猫导入为 pd

Name = ['ID', 'Country', 'IBAN','ID_bal_amt', 'ID_bal_time','Dan_city','ID_bal_mod','Dan_country','ID_bal_type', 'ID_bal_amt', 'ID_bal_time','ID_bal_mod','ID_bal_type' ,'Dan_sex', 'Dan_Age', 'Dan_country','Dan_sex' , 'Dan_city','Dan_country','ID_bal_amt', 'ID_bal_time','ID_bal_mod','ID_bal_type' ]
Value = ['TAMARA_CO', 'GERMANY','FR56', '12','June','Berlin','OPBD', '55','CRDT','432', 'August', 'CLBD','DBT', 'M', '22', 'FRA', 'M', 'Madrid', 'ESP','432','March','FABD','CRDT']
Ccy = ['','','','EUR','EUR','','EUR','','','','EUR','EUR','USD','USD','USD','','CHF', '','DKN','','','USD','CHF']
Group = ['0','0','0','1','1','1','1','1','1','2','2','2','2','2','2','2','3','3','3','4','4','4','4']

df = pd.DataFrame({'Name':Name, 'Value' : Value, 'Ccy' : Ccy,'Group':Group})

print(df)

          Name      Value  Ccy Group
0            ID  TAMARA_CO          0
1       Country    GERMANY          0
2          IBAN       FR56          0
3    ID_bal_amt         12  EUR     1
4   ID_bal_time       June  EUR     1
5      Dan_city     Berlin          1
6    ID_bal_mod       OPBD  EUR     1
7   Dan_country         55          1
8   ID_bal_type       CRDT          1
9    ID_bal_amt        432          2
10  ID_bal_time     August  EUR     2
11   ID_bal_mod       CLBD  EUR     2
12  ID_bal_type        DBT  USD     2
13      Dan_sex          M  USD     2
14      Dan_Age         22  USD     2
15  Dan_country        FRA          2
16      Dan_sex          M  CHF     3
17     Dan_city     Madrid          3
18  Dan_country        ESP  DKN     3
19   ID_bal_amt        432          4
20  ID_bal_time      March          4
21   ID_bal_mod       FABD  USD     4
22  ID_bal_type       CRDT  CHF     4

我想减少这个数据框！我想通过保留与模式关联的行组来仅减少包含字符串“bal”的行：“CLBD”。这意味着我在值“CLBD”中搜索名称“ID_bal_mod”，然后保留同一组中的所有其他名称 ID_bal_amt、ID_bal_time、ID_bal_mod、ID_bal_type。在我们的示例中，它是组 2 中的名称

另外，我想将它们在“组”列中的值更改为 0。

所以最后我想获得这个索引也被重置的新数据框

          Name      Value  Ccy Group
0            ID  TAMARA_CO          0
1       Country    GERMANY          0
2          IBAN       FR56          0
3      Dan_city     Berlin          1
4   Dan_country         55          1
5    ID_bal_amt        432          0
6   ID_bal_time     August  EUR     0
7    ID_bal_mod       CLBD  EUR     0
8   ID_bal_type        DBT  USD     0
9       Dan_sex          M  USD     2
10      Dan_Age         22  USD     2
11  Dan_country        FRA          2
12      Dan_sex          M  CHF     3
13     Dan_city     Madrid          3
14  Dan_country        ESP  DKN     3

任何人有一个有效的想法？ 谢谢

Answer 1

让我们试试你的逻辑：

rows_with_bal = df['Name'].str.contains('bal')
groups_with_CLBD = ((rows_with_bal & df['Value'].eq('CLBD')) 
                    .groupby(df['Group']).transform('any')
                   )

# set the `Group` to 0 for `groups_with_CLBD`
df.loc[groups_with_CLBD, 'Group'] = 0

# keep the rows without bal or `groups_with_CLBD`
df = df.loc[(~rows_with_bal) | groups_with_CLBD]

输出：

           Name      Value  Ccy Group
0            ID  TAMARA_CO          0
1       Country    GERMANY          0
2          IBAN       FR56          0
5      Dan_city     Berlin          1
7   Dan_country         55          1
9    ID_bal_amt        432          0
10  ID_bal_time     August  EUR     0
11   ID_bal_mod       CLBD  EUR     0
12  ID_bal_type        DBT  USD     0
13      Dan_sex          M  USD     0
14      Dan_Age         22  USD     0
15  Dan_country        FRA          0
16      Dan_sex          M  CHF     3
17     Dan_city     Madrid          3
18  Dan_country        ESP  DKN     3