# Grouping and Aggregating with multiple Index Levels in Pandas

## Pandas Advanced Grouping and Aggregation: Exercise-8 with Solution

Grouping and Aggregating with Multiple Index Levels:

Write a Pandas program to perform grouping and aggregation operations using multiple index levels.

**Sample Solution:**

**Python Code :**

```
import pandas as pd
# Sample DataFrame
data = {'Category': ['A', 'A', 'B', 'B', 'C', 'C'],
'Type': ['X', 'Y', 'X', 'Y', 'X', 'Y'],
'Value': [1, 2, 3, 4, 5, 6]}
df = pd.DataFrame(data)
print("Sample DataFrame:")
print(df)
# Group by 'Category' and 'Type'
print("\nGroup by 'Category' and 'Type':")
grouped = df.groupby(['Category', 'Type']).sum()
print(grouped)
```

Output:

Sample DataFrame: Category Type Value 0 A X 1 1 A Y 2 2 B X 3 3 B Y 4 4 C X 5 5 C Y 6 Group by 'Category' and 'Type': Value Category Type A X 1 Y 2 B X 3 Y 4 C X 5 Y 6

**Explanation:**

- Import pandas.
- Create a sample DataFrame.
- Group by 'Category' and 'Type'.
- Sum the grouped data.
- Print the result.

**Python Code Editor:**

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