GroupBy and Apply multiple Aggregations with named functions in Pandas

Last update on April 11 2025 12:51:22 (UTC/GMT +8 hours)

15. GroupBy and Applying Multiple Aggregations with Named Functions

Write a Pandas program to apply multiple aggregations with named functions in GroupBy for detailed data analysis.

Sample Solution:

Python Code :

import pandas as pd

# Sample DataFrame
data = {'Category': ['A', 'A', 'B', 'B', 'C', 'C'],
        'Value1': [5, 10, 15, 20, 25, 30],
        'Value2': [50, 100, 150, 200, 250, 300]}

df = pd.DataFrame(data)
print("Sample DataFrame:")
print(df)
      
# Group by 'Category' and apply multiple named aggregations
print("\nGroup by 'Category' and apply multiple named aggregations:")
grouped = df.groupby('Category').agg(
    Total_Value1=('Value1', 'sum'),
    Average_Value2=('Value2', 'mean')
)

print(grouped)

Output:

Sample DataFrame:
  Category  Value1  Value2
0        A       5      50
1        A      10     100
2        B      15     150
3        B      20     200
4        C      25     250
5        C      30     300

Group by 'Category' and apply multiple named aggregations:
          Total_Value1  Average_Value2
Category                              
A                   15            75.0
B                   35           175.0
C                   55           275.0

Explanation:

Import pandas.
Create a sample DataFrame.
Group by 'Category'.
Apply multiple named aggregations: sum for 'Value1' and mean for 'Value2'.
Print the result.

For more Practice: Solve these Related Problems:

Write a Pandas program to group data and apply multiple named aggregation functions, then output a DataFrame with clear column names.
Write a Pandas program to group a DataFrame and apply several named functions to each group, including custom metrics.
Write a Pandas program to perform groupby with named aggregations for multiple columns and then merge the results into a single summary table.
Write a Pandas program to group data and apply a set of named aggregation functions, ensuring the output columns are logically ordered.

Python Code Editor:

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