Using GroupBy with Lambda functions in Pandas

Last update on April 11 2025 12:50:57 (UTC/GMT +8 hours)

7. Using GroupBy with Lambda functions

Write a Pandas program to use lambda functions within groupby for flexible and efficient data transformations.

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)

# Group by 'Category' and apply lambda function
print("\nGroup by 'Category' and apply lambda function:")
grouped = df.groupby('Category').agg(lambda x: x.max() - x.min())
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 lambda function:
          Value
Category       
A            10
B            10
C            10

Explanation:

• Import pandas.
• Create a sample DataFrame.
• Group by 'Category'.
• Apply a lambda function to calculate the range of values.
• Print the result.

For more Practice: Solve these Related Problems:

• Write a Pandas program to group data and apply a lambda function that computes the percentage share of each record within its group.
• Write a Pandas program to group a DataFrame and use a lambda function to filter each group based on the relative ranking of values.
• Write a Pandas program to group data and apply a lambda that returns the difference between each value and the group median.
• Write a Pandas program to group data and use a lambda function to standardize values within each group.

Python Code Editor:

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