Python：删除数据帧的行并保留特定组

Question

The question is still not answered !!!!

Let's say that I have this dataframe :

import pandas as 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

I want to reduce this dataframe ! I want to reduce only the rows that contains the string "bal" by keeping the group of rows that is associated at the the mode : "CLBD". That means that I search the value "CLBD" for the the name "ID_bal_mod" and then I keep all the others names ID_bal_amt, ID_bal_time, ID_bal_mod, ID_bal_type that are in the same group. In our example, it is the names that are in the group 2

In addition, I want to change the their value in the column "Group" to 0.

So at the end I would like to get this new dataframe where the indexing is reset too

          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

Anyone has an efficient idea ? Thank you

Answer 1

Let's try your logic:

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]

Output:

           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