Pandas DataFrame idxmax() method with examples

The Pandas DataFrame.idxmax() method returns the column/row label of the maximum value of each row/column of the source DataFrame. NA/null values are excluded.

Syntax –

DataFrame.idxmax(axis=0, skipna=True)

axis – The axis to use. 0 or ‘index’ for row-wise, 1 or ‘columns’ for column-wise.

skipna – Exclude NA/null values. If an entire row/column is NA, the result will be NA.

Examples –

Let’s create a dataframe to work with.

import pandas as pd

data = {'Apple':[89, 89, 90, 110, 125, 84, 131, 123, 123, 140, 145, 145],
       'Orange': [46, 46, 50, 65, 63, 48, 110, 120, 60, 42, 47, 62],
       'Banana': [26, 30, 30, 25, 38, 22, 22, 36, 20, 27, 23, 34 ],
       'Mango': [80, 80, 90, 125, 130, 150, 140, 140, 135, 135, 80, 90]}

index = ['Jan','Feb','Mar','Apr','May','June','Jul','Aug','Sep','Oct','Nov','Dec']
df = pd.DataFrame(data, index=index)
df

1 . Find the Index that has Maximum value for Each Columns –

To find the index that has maximum value for each columns, we can use the idxmax() method in pandas.

df.idxmax()

#output
Apple      Nov
Orange     Aug
Banana     May
Mango     June
dtype: object

For Apples the highest value is in the month of Nov. For Orange it’s Aug, for Banana it’s May and for Mango it is June.

2 . Find Column that has Max value For Each Row –

To find the column that has max value for each row, we need to set the axis parameter to axis=1 or columns.

df.idxmax(axis=1)

#output
Jan     Apple
Feb     Apple
Mar     Apple
Apr     Mango
May     Mango
June    Mango
Jul     Mango
Aug     Mango
Sep     Mango
Oct     Apple
Nov     Apple
Dec     Apple
dtype: object

IN Jan, Feb and Mar, Apple costs more than any other fruits in this dataframe. Then in Apr, May, June, Jul, Aug and Sep Mango costs more than any other fruits. Then again Apple costs more in the month of Oct, Nov and Dec.

Related Posts –

1. Pandas DataFrame idxmin() method with examples