How to compare one column's value to multiple values in other columns?

Question

I don't know how to ask this, so I made up an example. The goal is to compare the value of columns A & B to the values of C,D and E separately. If the value is the same or higher (>=) then return '1' else '0'.

     A      B     C     D     E
1   101    103   100   101   99
2   102    100   101   99    103
3   100    99    103   100   98

The desired outcome would be this:

A               B       
C   D   E       C   D   E
1   1   1       1   1   1
1   1   0       0   1   0
0   1   1       0   0   1

Ideally I would like a function for this, what's the best way to approach this?

Answer 1

Direct compare A and B to C, D, E, and concat them together

A = df[['C','D','E']].le(df[['A']].values).astype(int)
B = df[['C','D','E']].le(df[['B']].values).astype(int)

pd.concat([A,B], axis=1, keys=['A','B'])

Out[650]:
   A        B
   C  D  E  C  D  E
1  1  1  1  1  1  1
2  1  1  0  0  1  0
3  0  1  1  0  0  1

Answer 2

You can use with le which checks for every column if its equal or less than A and then B:

cols = ['A', 'B']
compare = ['C', 'D', 'E']

for col in cols:
    print(col)
    df[compare].le(df[col], axis=0).astype(int)

output

A
   C  D  E
0  1  1  1
1  1  1  0
2  0  1  1
B
   C  D  E
0  1  1  1
1  0  1  0
2  0  0  1

---

It would be more elegant to make a dict with the column names as key:

cols = ['A', 'B']
compare = ['C', 'D', 'E']

dct = {col: df[compare].le(df[col], axis=0).astype(int) for col in cols}

print(dct['A'], '\n')
print(dct['B'])

output

   C  D  E
0  1  1  1
1  1  1  0
2  0  1  1 

   C  D  E
0  1  1  1
1  0  1  0
2  0  0  1