Use Custom Aggregation Functions in Pandas GroupBy

Last update on September 09 2025 12:38:37 (UTC/GMT +8 hours)

3. Custom Aggregation Functions

Write a Pandas program to implement custom aggregation functions within groupby for tailored data analysis.

Sample Solution:

Python Code :

import pandas as pd

# Sample DataFrame
data = {'Category': ['A', 'A', 'B', 'B', 'C', 'C'],
        'Value': [5, 15, 25, 35, 45, 55]}

df = pd.DataFrame(data)
print("Sample DataFrame:")
print(df)

# Define custom aggregation function
def custom_agg(x):
    return x.max() - x.min()

# Group by 'Category' and apply custom aggregation
print("\nGroup by 'Category' and apply custom aggregation:")
grouped = df.groupby('Category').agg(custom_agg)

print(grouped)

Output:

Sample DataFrame:
  Category  Value
0        A      5
1        A     15
2        B     25
3        B     35
4        C     45
5        C     55

Group by 'Category' and apply custom aggregation:
          Value
Category       
A            10
B            10
C            10

Explanation:

Import pandas.
Create a sample DataFrame.
Define a custom aggregation function.
Group by 'Category' and apply the custom function.
Print the result.

For more Practice: Solve these Related Problems:

Write a Pandas program to group data and apply a custom function that calculates the range (max-min) for each group.
Write a Pandas program to implement a custom aggregation that returns the second highest value within each group.
Write a Pandas program to group data and apply a custom lambda function that computes a weighted average for a specified column.
Write a Pandas program to group data and apply a custom aggregation function that outputs multiple statistics as a tuple.

Go to:

PREV : Applying Multiple Aggregations.
NEXT : Group by and Filter Groups.

Python Code Editor:

Have another way to solve this solution? Contribute your code (and comments) through Disqus.