Is there a way to do this in Python?-CodePudding

I have a data frame that looks like this:

data = {'State': ['24', '24', '24', 
                  '24','24','24','24','24','24','24','24','24'],
        'County code': ['001', '001', '001', 
                        '001','002','002','002','002','003','003','003','003'],
        'TT code': ['123', '123', '123',
                    '123','124','124','124','124','125','125','125','125'],
        'BLK code': ['221', '221', '221',
                     '221','222','222','222','222','223','223','223','223'],
        'Age Code': ['1', '1', '2', '2','2','2','2','2','2','1','2','1']}
df = pd.DataFrame(data)

essentially I want to just have where only the TT code where the age code is 2 and there are no 1's. So I just want to have the data frame where:

'State': ['24', '24', '24', '24'],
'County code': ['002','002','002','002',],
'TT code': ['124','124','124','124',],
'BLK code': ['222','222','222','222'],
'Age Code': ['2','2','2','2']

is there a way to do this?

CodePudding user response：

This should work.

df111['Age Code'] = "2"

I am just wondering why the choice of string for valueType of integer

CodePudding user response：

IIUC, you want to keep only the TT groups where there are only Age groups with value '2'?

You can use a groupby.tranform('all') on the boolean Series:

df[df['Age Code'].eq('2').groupby(df['TT code']).transform('all')]

output:

  State County code TT code BLK code Age Code
4    24         002     124      222        2
5    24         002     124      222        2
6    24         002     124      222        2
7    24         002     124      222        2