Average each Column and Row in Pandas DataFrame

To get the average of each numeric column and row in Pandas DataFrame (apply only for numeric values):

Average each column:

df.mean(axis=0)

Average each row:

df.mean(axis=1)

Steps to get the Average of each Column and Row in Pandas DataFrame

Step 1: Prepare the data

For example, here is a simple dataset that contains information about the commission earned by 3 people (over the first 6 months of the year):

The goal is to get the average commission earned:

• For each person over the first 6 months (average by column)
• For each month across all people (average by row)

Step 2: Get the Average of each Column and Row in Pandas DataFrame

To get the average of each numeric column using df.mean(axis=0):

import pandas as pd

data = {"month": ["Jan", "Feb", "Mar", "Apr", "May", "Jun"],
        "a_commission": [7000, 5500, 6000, 4500, 8000, 6000],
        "b_commission": [10000, 7500, 6500, 6000, 9000, 8500], 
        "c_commission": [3000, 6000, 4500, 4500, 4000, 5500]
        }

df = pd.DataFrame(data, columns=["a_commission", "b_commission", "c_commission"])

df_avg = df.mean(axis=0)

print(df_avg)

The average commission per person:

a_commission    6166.666667
b_commission    7916.666667
c_commission    4583.333333

To get the average of each numeric row using df.mean(axis=1):

import pandas as pd

data = {"month": ["Jan", "Feb", "Mar", "Apr", "May", "Jun"],
        "a_commission": [7000, 5500, 6000, 4500, 8000, 6000],
        "b_commission": [10000, 7500, 6500, 6000, 9000, 8500], 
        "c_commission": [3000, 6000, 4500, 4500, 4000, 5500]
        }

df = pd.DataFrame(data, columns=["a_commission", "b_commission", "c_commission"])

df_avg = df.mean(axis=1)

print(df_avg)

The average commission earned per month:

0    6666.666667
1    6333.333333
2    5666.666667
3    5000.000000
4    7000.000000
5    6666.666667

The following guide explains the steps to get the sum of each column and row in Pandas DataFrame.