# GroupBy and create a new column with Aggregated data in Pandas

## Pandas Advanced Grouping and Aggregation: Exercise-13 with Solution

GroupBy and Create a New Column with Aggregated Data:

Write a Pandas program to create a new column with aggregated data from a GroupBy operation for enriched data insights.

**Sample Solution:**

**Python Code :**

```
import pandas as pd
# Sample DataFrame
data = {'Category': ['A', 'A', 'B', 'B', 'C', 'C'],
'Value': [10, 20, 30, 40, 50, 60]}
df = pd.DataFrame(data)
print("Sample DataFrame:")
print(df)
# Group by 'Category' and calculate the sum
print("\nGroup by 'Category' and calculate the sum:")
df['SumValue'] = df.groupby('Category')['Value'].transform('sum')
print(df)
```

Output:

Sample DataFrame: Category Value 0 A 10 1 A 20 2 B 30 3 B 40 4 C 50 5 C 60 Group by 'Category' and calculate the sum: Category Value SumValue 0 A 10 30 1 A 20 30 2 B 30 70 3 B 40 70 4 C 50 110 5 C 60 110

**Explanation:**

- Import pandas.
- Create a sample DataFrame.
- Group by 'Category' and calculate the sum.
- Create a new column 'SumValue' with the aggregated data.
- Print the DataFrame with the new column.

**Python Code Editor:**

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