assign column names in pandas from pd.series an loop

Question

Let say my df:

A User1 User2
1 0       0
2 1       1
3 1       0

And I want create another column in dataframe that was sum of column user1 plus user So it's easy: df['sum_result_Newcolum'] = df['user1']+df['user2'] . So it looks like this:

 A User1 User2   sum_result_Newcolum
    1 0       0   0
    2 1       1   2
    3 1       0   1

But I have panda.series look like this:

    0
0   AC.IR
1   AC.PO
2   B.0
3   B.1

end etc. So I want create for statement there run through pd.series and add value to column name. So that issue, I try something like this without any success:

df['sum_result_'+ {cv_details.get(0)}]=df['user1']+df['user2']
df['sum_result_'+ cv_details.get(0)]=df['user1']+df['user2']
df['sum_result_'+ cv_details[0]]=df['user1']+df['user2']
df['sum_result_'+ cv_details.iloc[0]]=df['user1']+df['user2']

All time best result is column name: "sum_result_Action..."

Maybe solution convert pd.series to frame?

Answer 1

• it's not 100% clear what your expected result is.
• If you want a new column for each value in your series
  • assign ** kwargs can be built as a dict comprehension
  • from your sample code, you have defined a static calculation User1 + User2

import io
import pandas as pd

df = pd.read_csv(io.StringIO("""A User1 User2
1 0       0
2 1       1
3 1       0"""), sep="\s+")

cv_details = pd.Series(['AC.IR', 'AC.PO', 'B.0', 'B.1'])

df.assign(**{f"sum_result_{v}":df["User1"]+df["User2"] for v in cv_details})

	A	User1	User2	sum_result_AC.IR	sum_result_AC.PO	sum_result_B.0	sum_result_B.1
0	1	0	0	0	0	0	0
1	2	1	1	2	2	2	2
2	3	1	0	1	1	1	1