Apply Multiple Aggregations on Grouped Data in Pandas

Last update on December 21 2024 09:22:52 (UTC/GMT +8 hours)

Pandas Advanced Grouping and Aggregation: Exercise-2 with Solution

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.

Python Code Editor:

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

Previous: Group by Multiple columns in Pandas.
Next: Use Custom Aggregation Functions in Pandas GroupBy.