Combining GroupBy with Transform in Pandas

Last update on May 03 2025 12:57:25 (UTC/GMT +8 hours)

11. Combining GroupBy with Transform

Write a Pandas program to combine GroupBy with Transform to perform complex data transformations on grouped data.

Sample Solution:

Python Code :

import pandas as pd
# Sample DataFrame
data = {'Category': ['A', 'A', 'B', 'B', 'C', 'C'],
        'Value': [1, 2, 3, 4, 5, 6]}

df = pd.DataFrame(data)
print("Sample DataFrame:")
print(df)
# Group by 'Category' and transform by calculating the mean
print("\nGroup by 'Category' and transform by calculating the mean:")
transformed = df.groupby('Category').transform('mean')
print(transformed)

Output:

Sample DataFrame:
  Category  Value
0        A      1
1        A      2
2        B      3
3        B      4
4        C      5
5        C      6

Group by 'Category' and transform by calculating the mean:
   Value
0    1.5
1    1.5
2    3.5
3    3.5
4    5.5
5    5.5

Explanation:

Import pandas.
Create a sample DataFrame.
Group by 'Category'.
Transform by calculating the mean for each group.
Print the transformed DataFrame.

For more Practice: Solve these Related Problems:

Write a Pandas program to group data and then use transform to return a DataFrame with the same shape as the original.
Write a Pandas program to group a DataFrame and apply transform to normalize each group's values.
Write a Pandas program to combine groupby with transform to calculate the deviation of each record from its group's mean.
Write a Pandas program to group data and use transform to add a new column with group-based z-scores.

Go to:

Previous: Using named Aggregations in Pandas GroupBy.
Next: GroupBy and Apply different functions using a Dictionary in Pandas.

Python Code Editor:

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