You can use the following methods to calculate the average row values for selected columns in a pandas DataFrame:
Method 1: Calculate Average Row Value for All Columns
df.mean(axis=1)
Method 2: Calculate Average Row Value for Specific Columns
df[['col1', 'col3']].mean(axis=1)
The following examples shows how to use each method in practice with the following pandas DataFrame:
import pandas as pd #create DataFrame df = pd.DataFrame({'points': [14, 19, 9, 21, 25, 29, 20, 11], 'assists': [5, 7, 7, 9, 12, 9, 9, 4], 'rebounds': [11, 8, 10, 6, 6, 5, 9, 12]}) #view DataFrame df points assists rebounds 0 14 5 11 1 19 7 8 2 9 7 10 3 21 9 6 4 25 12 6 5 29 9 5 6 20 9 9 7 11 4 12
Method 1: Calculate Average Row Value for All Columns
The following code shows how to create a new column in the DataFrame that displays the average row value for all columns:
#define new column that shows the average row value for all columns
df['average_all'] = df.mean(axis=1)
#view updated DataFrame
df
points assists rebounds average_all
0 14 5 11 10.000000
1 19 7 8 11.333333
2 9 7 10 8.666667
3 21 9 6 12.000000
4 25 12 6 14.333333
5 29 9 5 14.333333
6 20 9 9 12.666667
7 11 4 12 9.000000
Here’s how to interpret the output:
The average value of the first row is calculated as: (14+5+11) / 3 = 10.
The average value of the second row is calculated as: (19+7+8) / 3 = 11.33.
And so on.
Method 2: Calculate Average Row Value for Specific Columns
The following code shows how to calculate the average row value for just the “points” and “rebounds” columns:
#define new column that shows average of row values for points and rebounds columns
df['avg_points_rebounds'] = df[['points', 'rebounds']].mean(axis=1)
#view updated DataFrame
df
points assists rebounds avg_points_rebounds
0 14 5 11 12.5
1 19 7 8 13.5
2 9 7 10 9.5
3 21 9 6 13.5
4 25 12 6 15.5
5 29 9 5 17.0
6 20 9 9 14.5
7 11 4 12 11.5
Here’s how to interpret the output:
The average value of “points” and “rebounds” in the first row is calculated as: (14+11) / 2 = 12.5.
The average value of “points” and “rebounds” in the second row is calculated as: (19+8) / 2 = 13.5.
And so on.
Additional Resources
The following tutorials explain how to perform other common operations in Python:
How to Calculate a Trimmed Mean in Python
How to Calculate Geometric Mean in Python
How to Replace Values in Pandas Column Based on Condition
