Apply Multiple Aggregations on Grouped Data in Pandas

Last update on September 09 2025 12:38:29 (UTC/GMT +8 hours)

2. Applying Multiple Aggregations

Write a Pandas program to apply multiple aggregation functions to grouped data using for enhanced data insights.

Sample Solution:

Python Code :

import pandas as pd

# Sample DataFrame
data = {'Category': ['A', 'A', 'B', 'B', 'C', 'C'],
        'Value': [10, 20, 30, 40, 50, 60]}

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

# Group by 'Category' and apply multiple aggregations
print("\nGroup by 'Category' and apply multiple aggregations:")
grouped = df.groupby('Category').agg(['sum', 'mean', 'max'])

print(grouped)

Output:

Sample DataFrame:
  Category  Value
0        A     10
1        A     20
2        B     30
3        B     40
4        C     50
5        C     60

Group by 'Category' and apply multiple aggregations:
           Value          
           sum  mean max
Category                
A           30  15.0  20
B           70  35.0  40
C          110  55.0  60

Explanation:

Import pandas.
Create a sample DataFrame.
Group by 'Category'.
Apply sum, mean, and max aggregations.
Print the result.

For more Practice: Solve these Related Problems:

Write a Pandas program to group a DataFrame by a column and apply multiple aggregations (min, max, mean) on different numeric columns.
Write a Pandas program to apply a list of aggregation functions on grouped data and then rename the aggregated columns for clarity.
Write a Pandas program to group data and simultaneously calculate the standard deviation and variance for a given numeric column.
Write a Pandas program to group a dataset and use custom aggregations alongside built-in functions on different columns.

Go to:

PREV : Grouping by Multiple columns.
NEXT : Custom Aggregation Functions.

Python Code Editor:

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