How to return the highest value from multiple columns to a new column in a pandas df

Question

Apologies for the opaque question name (not sure how to word it). I have the following dataframe:

import pandas as pd
import numpy as np

data = [['tom', 1,1,6,4],
        ['tom', 1,2,2,3],
        ['tom', 1,2,3,1],
        ['tom', 2,3,2,7],
        ['jim', 1,4,3,6],
        ['jim', 2,6,5,3]]

df = pd.DataFrame(data, columns = ['Name', 'Day','A','B','C']) 
df = df.groupby(by=['Name','Day']).agg('sum').reset_index()
df

I would like to add another column that returns text according to which column of A,B,C is the highest:

For example I would like Apple if A is highest, Banana if B is highest, and Carrot if C is highest. So in the example above the values for the 4 columns should be:

New Col
Carrot
Apple
Banana
Carrot

Any help would be much appreciated! Thanks

Answer 1

Use DataFrame.idxmax along axis=1 with Series.map:

dct = {'A': 'Apple', 'B': 'Banana', 'C': 'Carrot'}
df['New col'] = df[['A', 'B', 'C']].idxmax(axis=1).map(dct)

Result:

  Name  Day  A   B  C New col
0  jim    1  4   3  6  Carrot
1  jim    2  6   5  3   Apple
2  tom    1  5  11  8  Banana
3  tom    2  3   2  7  Carrot

Answer 2

@ShubhamSharma's answer is better than this, but here is another option:

df['New col'] = np.where((df['A'] > df['B']) & (df['A'] > df['C']), 'Apple', 'Carrot')
df['New col'] = np.where((df['B'] > df['A']) & (df['B'] > df['C']), 'Banana', df['New col'])

output:

    Name    Day A   B   C   New col
0   jim 1   4   3   6   Carrot
1   jim 2   6   5   3   Apple
2   tom 1   5   11  8   Banana
3   tom 2   3   2   7   Carrot