Pandas - Grouping and aggregating DataFrame with a custom function using apply()

Last update on April 09 2025 12:53:05 (UTC/GMT +8 hours)

19. Aggregate DataFrame Groups with a Custom Function

Write a Pandas program that applies a custom function to aggregate DataFrame groups.

In this exercise, we have grouped a DataFrame by a specific column and apply a custom aggregation function using apply().

Sample Solution :

Code :

import pandas as pd

# Create a sample DataFrame
df = pd.DataFrame({
    'Category': ['A', 'B', 'A', 'B'],
    'Value': [10, 20, 15, 25]
})

# Define a custom aggregation function
def custom_agg(group):
    return group['Value'].sum()

# Group by 'Category' and apply the custom aggregation function
df_grouped = df.groupby('Category').apply(custom_agg)

# Output the result
print(df_grouped)

Output:

Category
A    25
B    45
dtype: int64                            

Explanation:

• Created a DataFrame with a 'Category' and 'Value' column.
• Defined a custom aggregation function custom_agg() to calculate the sum of values in each group.
• Grouped the DataFrame by 'Category' and applied the custom aggregation using apply().
• Returned the summed values for each group.

For more Practice: Solve these Related Problems:

• Write a Pandas program to group a DataFrame by a specific column and then use apply() to aggregate each group with a custom function.
• Write a Pandas program to create a custom aggregation function and apply it to each group using groupby() and apply().
• Write a Pandas program to compute multiple group-level statistics by applying a custom function to each group via apply().
• Write a Pandas program to compare the output of a custom aggregation function applied via apply() with built-in groupby aggregation methods.

Python-Pandas Code Editor:

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