CODEWITHSUNDEEP
pythonpandasdataframe
sizo_abe
sizo_abe

Reputation: 501

filter on multiple columns and operation row wise pandas dataframe

if a1_status, b1_status, c1_status, d1_status equal "Active" and a1, b1, c1, d1 equals "Loan" then output column "Loan_active" contains count of "Active" row wise. input dataframe looks like this.

enter image description here

output dataframe :

enter image description here

use below code to create same dataframe mentioned above in image in pandas .

import pandas as pd
df = pd.DataFrame({'a1':['Loan','Loan','Loan'],
            'a1_status' : ['active','closed','active'] ,
            'b1' : ['Loan','Loan','Loan'],
            'b1_status' : ['active','active','active'] ,
            'c1' : ['Credit','Credit','Credit'],
            'c1_status' : ['closed','closed','closed'] ,
            'd1' : ['Loan','Loan','Loan'],
            'd1_status' : ['closed','closed','active'] ,
})
print(df)

Upvotes: 1

Views: 220

Answers (2)

hteza
hteza

Reputation: 321

This solution is not as elegant as the other, but it explictly select the columns that includes ONLY loans, then check their status. But this also assumes that the status columns will be named loan_status.

loan = df.columns[df.isin(["Loan"]).all()]
df['loan_active'] = df[loan+"_status"].eq('active').sum(axis=1)

or if you prefer one liners;

df['loan_active'] = df[df.columns[df.isin(["Loan"]).all()]+"_status"].eq('active').sum(axis=1)

Upvotes: 1

BENY
BENY

Reputation: 323376

Let us do shift

df['new'] = (df.eq('active') & df.shift(axis=1).eq('Loan')).sum(axis=1)
Out[349]: 
0    2
1    1
2    3
dtype: int64

Upvotes: 4

Related Questions