How to Calculate Mode in Python Pandas ?

To calculate mode in pandas, we use the dataframe’s mode() method. In statistics, the mode is the value in a dataset that occurs most frequently.

Examples –

Let’s create a dataset 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 . Calculate the mode of the columns –

You can calculate the mode of a single column like this

df['Apple'].mode()

#output
0     89
1    123
2    145
dtype: int64

or you can calculate the mode of all columns like this

df.mode()

2. Calculate the mode of the rows –

To calculate the mode of each rows, we need to set the axis parameter to axis= 1 or columns.

df.mode(axis=1)