I am trying to handle the following dataframe

import pandas as pd

df =pd.DataFrame(
    data = {'m1' : [0,0,1,0,0,0,0,0,0,0,0],
            'm2' : [0,0,0,0,0,1,0,0,0,0,0],
            'm3' : [0,0,0,0,0,0,0,0,1,0,0],
            'm4' : [0,1,0,0,0,0,0,0,0,0,0],
            'm5' : [0,0,0,0,0,0,0,0,0,0,0],
            'm6' : [0,0,0,0,0,0,0,0,0,1,0]}
) 
df
#
  m1 m2 m3 m4 m5 m6
0  0  0  0  0  0  0
1  0  0  0  1  0  0
2  1  0  0  0  0  0
3  0  0  0  0  0  0
4  0  0  0  0  0  0
5  0  1  0  0  0  0
6  0  0  0  0  0  0
7  0  0  0  0  0  0
8  0  0  1  0  0  0
9  0  0  0  0  0  1
10 0  0  0  0  0  0

From the above dataframe, I want to separate m1 and other features.

Assign 1 to m_other if any of m2 to m6 is 1.

Ideal results are shown below.

   m1 m_other
0  0  0  
1  0  1  
2  1  0  
3  0  0 
4  0  0  
5  0  1  
6  0  0  
7  0  0  
8  0  1  
9  0  1 
10 0  0

I thought about adapting the any function, but I stumbled and couldn't figure it out.

If anyone has any good ideas, I would appreciate it if you could share them with me.

CodePudding user response：

Use

CodePudding user response：

Here is one way to do it using concat to combine the first column and the max of the renaming columns and then renaming the column name

df2=pd.concat([df.iloc[:,:1],(df.iloc[:,1:].max(axis=1))], axis=1)
df2=df2.rename(columns={0:'m_other'})
df2

    m1  m_other
0   0   0
1   0   1
2   1   0
3   0   0
4   0   0
5   0   1
6   0   0
7   0   0
8   0   1
9   0   1
10  0   0