Apply different functions to different columns with GroupBy

Last update on May 03 2025 13:04:49 (UTC/GMT +8 hours)

9. Applying different functions to different columns with GroupBy

Write a Pandas program that applies different functions to different columns in Pandas GroupBy for tailored data analysis.

Sample Solution:

Python Code :

import pandas as pd

# Sample DataFrame
data = {'Category': ['A', 'A', 'B', 'B', 'C', 'C'],
        'Value1': [10, 20, 30, 40, 50, 60],
        'Value2': [100, 200, 300, 400, 500, 600]}

df = pd.DataFrame(data)
print("Sample DataFrame:")
print(df)
# Group by 'Category' and apply different functions
print("\nGroup by 'Category' and apply different functions:")
grouped = df.groupby('Category').agg({'Value1': 'mean', 'Value2': 'sum'})
print(grouped)

Output:

Sample DataFrame:
  Category  Value1  Value2
0        A      10     100
1        A      20     200
2        B      30     300
3        B      40     400
4        C      50     500
5        C      60     600

Group by 'Category' and apply different functions:
          Value1  Value2
Category                
A           15.0     300
B           35.0     700
C           55.0    1100

Explanation:

Import pandas.
Create a sample DataFrame.
Group by 'Category'.
Apply mean aggregation on 'Value1' and sum aggregation on 'Value2'.
Print the result.

For more Practice: Solve these Related Problems:

Write a Pandas program to group data and apply a sum on one column while applying a custom aggregation on another, using a dictionary.
Write a Pandas program to group data and apply distinct functions to separate columns, then merge the results into a single DataFrame.
Write a Pandas program to group a DataFrame and use different aggregation functions on various columns, ensuring each output column is renamed.
Write a Pandas program to group data and apply lambda functions to different columns, displaying results side-by-side.

Go to:

Previous: Grouping and Aggregating with multiple Index Levels in Pandas.
Next: Calculating Percentage change in Resampled data.

Python Code Editor:

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