# Aggregate with different functions on different columns in Pandas

## 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:**

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