w3resource

Group by and Apply function to Groups in Pandas


Pandas Advanced Grouping and Aggregation: Exercise-5 with Solution


Group by and Apply function:
Write a Pandas program to group data and apply custom functions to groups for flexible data transformations.

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)
# Define function to apply to each group
def scale_values(x):
    return x / x.max()
# Group by 'Category' and apply function
print("\nGroup by 'Category' and apply function:")
grouped = df.groupby('Category').transform(scale_values)
print(grouped)

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 apply function:
      Value
0  0.500000
1  1.000000
2  0.750000
3  1.000000
4  0.833333
5  1.000000

Explanation:

  • Import pandas.
  • Create a sample DataFrame.
  • Define a function to scale values within each group.
  • Group by 'Category' and apply the function.
  • Print the transformed DataFrame.

Python Code Editor:

Have another way to solve this solution? Contribute your code (and comments) through Disqus.

Previous: Use Custom Aggregation Functions in Pandas GroupBy.
Next: Aggregate with different functions on different columns in Pandas.

What is the difficulty level of this exercise?

Test your Programming skills with w3resource's quiz.



Follow us on Facebook and Twitter for latest update.