﻿ Apply different functions to different columns with GroupBy

# Apply different functions to different columns with GroupBy

## Pandas Advanced Grouping and Aggregation: Exercise-9 with Solution

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.

Python Code Editor:

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