GroupBy and create a new column with Aggregated data in Pandas

Last update on April 11 2025 12:51:23 (UTC/GMT +8 hours)

13. 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.

For more Practice: Solve these Related Problems:

• Write a Pandas program to group data and then create a new column that contains the group mean for each row.
• Write a Pandas program to group a DataFrame and merge aggregated totals back into the original DataFrame as a new column.
• Write a Pandas program to group data and add a column showing the count of records in each group next to each original row.
• Write a Pandas program to group data and compute a new column that represents the percentage contribution of each record to its group's total.

Python Code Editor:

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