Group by and Apply function to Groups in Pandas

Last update on April 11 2025 12:50:59 (UTC/GMT +8 hours)

5. Group by and Apply function

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.

For more Practice: Solve these Related Problems:

• Write a Pandas program to group data and apply a function that normalizes the values within each group.
• Write a Pandas program to group a DataFrame and apply a custom function that returns the top two records from each group.
• Write a Pandas program to group data and apply a function that transforms each group's data by subtracting the group mean.
• Write a Pandas program to group data and apply a complex transformation function that computes a rolling window statistic within groups.

Python Code Editor:

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