Aggregate with different functions on different columns in Pandas

Last update on December 21 2024 07:51:48 (UTC/GMT +8 hours)

Pandas Advanced Grouping and Aggregation: Exercise-6 with Solution

Aggregating with different functions on different Columns:
Write a Pandas program to use different aggregation functions on different columns for versatile data analysis.

Sample Solution:

Python Code :

import pandas as pd
# Sample DataFrame
data = {'Category': ['A', 'A', 'B', 'B', 'C', 'C'],
        'Value1': [1, 2, 3, 4, 5, 6],
        'Value2': [10, 20, 30, 40, 50, 60]}
df = pd.DataFrame(data)
print("Sample DataFrame:")
print(df)
# Group by 'Category' and apply different aggregations
print("\nGroup by 'Category' and apply different aggregations:")
grouped = df.groupby('Category').agg({'Value1': 'sum', 'Value2': 'mean'})
print(grouped)

Output:

Sample DataFrame:
  Category  Value1  Value2
0        A       1      10
1        A       2      20
2        B       3      30
3        B       4      40
4        C       5      50
5        C       6      60

Group by 'Category' and apply different aggregations:
          Value1  Value2
Category                
A              3    15.0
B              7    35.0
C             11    55.0

Explanation:

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

Python Code Editor:

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

Previous: Group by and Apply function to Groups in Pandas.
Next: Using GroupBy with Lambda functions in Pandas.