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.