Getting list of all columns corresponding to maximum value in each row in a Dataframe

You could try these codes (bot approaches do the same):

max_cols_df = pd.DataFrame({"max_cols": [list(df[df==mv].iloc[i].dropna().index) for i, mv in enumerate(df.max(axis=1))]})
max_cols_df = pd.DataFrame({"max_cols": [list(df.iloc[i][v].index) for i, v in df.eq(df.max(axis=1), axis=0).iterrows()]})

max_cols_df 
----------------
    max_cols
0   [a, b]
1   [b]
2   [a, b]
3   [a]
----------------

But I think you asked a related question to another post and cannot upgrade pandas. Therefore, the second could throw an error.

Solved using Pandas Melt

import pandas as pd

df = pd.DataFrame({'a':[12,34,98,26],'b':[12,87,98,12],'c':[11,23,43,1]})

df = df.reset_index()

df = pd.melt(df, id_vars=['index'],value_vars=['a','b','c'], var_name='col_name')

df['Index_Value_Max'] = df.groupby(['index'])['value'].transform('max')

df[df['value'] == df['Index_Value_Max']].groupby(['index']).agg({'col_name':'unique'}).reset_index()

Output:

First, get the max value in each row:

df.max(axis=1)

Then check the dataframe for values that equal max values.

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

Then apply a function row-wise to select for the column names where the value in the row is True.

df.apply(
    lambda row: [k for k, v in row.iteritems() if v], 
    axis=1
)

Summarily, this is:

df.eq(df.max(axis=1), axis=0).apply(
    lambda row: [k for k, v in row.iteritems() if v], 
    axis=1
)

0    [a, b]
1       [b]
2    [a, b]
3       [a]
dtype: object