Use Custom Aggregation Functions in Pandas GroupBy

Last update on December 21 2024 07:51:53 (UTC/GMT +8 hours)

Pandas Advanced Grouping and Aggregation: Exercise-3 with Solution

Custom Aggregation Functions:
Write a Pandas program to implement custom aggregation functions within groupby for tailored data analysis.

Sample Solution:

Python Code :

import pandas as pd

# Sample DataFrame
data = {'Category': ['A', 'A', 'B', 'B', 'C', 'C'],
        'Value': [5, 15, 25, 35, 45, 55]}

df = pd.DataFrame(data)
print("Sample DataFrame:")
print(df)

# Define custom aggregation function
def custom_agg(x):
    return x.max() - x.min()

# Group by 'Category' and apply custom aggregation
print("\nGroup by 'Category' and apply custom aggregation:")
grouped = df.groupby('Category').agg(custom_agg)

print(grouped)

Output:

Sample DataFrame:
  Category  Value
0        A      5
1        A     15
2        B     25
3        B     35
4        C     45
5        C     55

Group by 'Category' and apply custom aggregation:
          Value
Category       
A            10
B            10
C            10

Explanation:

• Import pandas.
• Create a sample DataFrame.
• Define a custom aggregation function.
• Group by 'Category' and apply the custom function.
• Print the result.

Python Code Editor:

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