# 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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