Average each Column and Row in Pandas DataFrame

You may use the following syntax to get the average for each column and row in pandas DataFrame:

(1) Average for each column:

df.mean(axis=0)

(2) Average for each row:

df.mean(axis=1)

Next, I’ll review an example with the steps to get the average for each column and row for a given DataFrame

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

Step 1: Gather the data

To start, gather the data that needs to be averaged.

For example, I gathered the following data about the commission earned by 3 employees (over the first 6 months of the year):

The goal is to get the average of the commission earned:

For each employee over the first 6 months (average by column)
For each month across all employees (average by row)

Step 2: Create the DataFrame

Next, create the DataFrame in order to capture the above data in Python:

import pandas as pd

data = {'Month': ['Jan ','Feb ','Mar ','Apr ','May ','Jun '],
        'Jon Commission': [7000,5500,6000,4500,8000,6000],
        'Maria Commission': [10000,7500,6500,6000,9000,8500], 
        'Olivia Commission': [3000,6000,4500,4500,4000,5500]
        }

df = pd.DataFrame(data,columns=['Month','Jon Commission','Maria Commission','Olivia Commission'])
print (df)

Run the code in Python, and you’ll get the following DataFrame:

Step 3: Get the Average for each Column and Row in Pandas DataFrame

You can then apply the following syntax to get the average for each column:

df.mean(axis=0)

For our example, this is the complete Python code to get the average commission earned for each employee over the 6 first months (average by column):

import pandas as pd

data = {'Month': ['Jan ','Feb ','Mar ','Apr ','May ','Jun '],
        'Jon Commission': [7000,5500,6000,4500,8000,6000],
        'Maria Commission': [10000,7500,6500,6000,9000,8500], 
        'Olivia Commission': [3000,6000,4500,4500,4000,5500]
        }

df = pd.DataFrame(data,columns=['Month','Jon Commission','Maria Commission','Olivia Commission'])

av_column = df.mean(axis=0)
print (av_column)

Run the code, and you’ll get the average commission per employee:

Alternatively, you can get the average for each row using the following syntax:

df.mean(axis=1)

Here is the code that you can use to get the average commission earned for each month across all employees (average by row):

import pandas as pd

data = {'Month': ['Jan ','Feb ','Mar ','Apr ','May ','Jun '],
        'Jon Commission': [7000,5500,6000,4500,8000,6000],
        'Maria Commission': [10000,7500,6500,6000,9000,8500], 
        'Olivia Commission': [3000,6000,4500,4500,4000,5500]
        }

df = pd.DataFrame(data,columns=['Month','Jon Commission','Maria Commission','Olivia Commission'], index =['Jan ','Feb ','Mar ','Apr ','May ','Jun '])

av_row = df.mean(axis=1)
print (av_row)

Once you run the code in Python, you’ll get the average commission earned per month:

You may also want to check the following source that explains the steps to get the sum for each column and row in pandas DataFrame.