Column Name Corresponding To Largest Value In Pandas Dataframe

for below dataframe data: x y a b c 2 6 12 1 2 1 2 4 6 8 I want result in new column(i.e d) that returns name of column with max value only among a,b,c. cols a c I'm trying

Solution 1:

The fastest solution I can think of is DataFrame.dot:

df.eq(df.max(1), axis=0).dot(df.columns)

Details First, compute the maximum per row:

df.max(1)
01218
dtype: int64

Next, find the positions these values come from:

df.eq(df.max(1), axis=0)     
       x      y      a      b      c
0FalseFalseTrueFalseFalse1FalseFalseFalseFalseTrue

I use eq to make sure the comparison is broadcasted correctly across columns.

Next, compute the dot product with the column list:

df.eq(df.max(1), axis=0).dot(df.columns)
0    a
1    c
dtype: object

---

If the max is not unique, use

df.eq(df.max(1), axis=0).dot(df.columns + ',').str.rstrip(',')

To get a comma separated list of columns. For example,

Change a couple values:

df.at[0, 'c'] = 12
df.at[1, 'y'] = 8

Everything is the same, but notice I append a comma to every column:

df.columns + ','
Index(['x,', 'y,', 'a,', 'b,', 'c,'], dtype='object')

df.eq(df.max(1), axis=0).dot(df.columns + ',')
0    a,c,
1y,c,
dtype: object

From this, strip any trailing commas:

df.eq(df.max(1), axis=0).dot(df.columns + ',').str.rstrip(',') 
0    a,c
1y,c
dtype: object

Solution 2:

If you don't mind returning the first occurence of the maximum (or if maximum will always be unique), use idxmax

df[['a', 'b', 'c']].idxmax(1)

0a1cdtype: object

If you need all occurences of a maximum, use coldspeed's answer