GroupBy and Apply multiple Aggregations with named functions in Pandas

Pandas Advanced Grouping and Aggregation: Exercise-15 with Solution

GroupBy and Applying Multiple Aggregations with Named Functions:
Write a Pandas program to apply multiple aggregations with named functions in GroupBy for detailed data analysis.

Sample Solution:

Python Code :

import pandas as pd

# Sample DataFrame
data = {'Category': ['A', 'A', 'B', 'B', 'C', 'C'],
        'Value1': [5, 10, 15, 20, 25, 30],
        'Value2': [50, 100, 150, 200, 250, 300]}

df = pd.DataFrame(data)
print("Sample DataFrame:")
print(df)
      
# Group by 'Category' and apply multiple named aggregations
print("\nGroup by 'Category' and apply multiple named aggregations:")
grouped = df.groupby('Category').agg(
    Total_Value1=('Value1', 'sum'),
    Average_Value2=('Value2', 'mean')
)

print(grouped)

Output:

Sample DataFrame:
  Category  Value1  Value2
0        A       5      50
1        A      10     100
2        B      15     150
3        B      20     200
4        C      25     250
5        C      30     300

Group by 'Category' and apply multiple named aggregations:
          Total_Value1  Average_Value2
Category                              
A                   15            75.0
B                   35           175.0
C                   55           275.0

Explanation:

• Import pandas.
• Create a sample DataFrame.
• Group by 'Category'.
• Apply multiple named aggregations: sum for 'Value1' and mean for 'Value2'.
• Print the result.

Python Code Editor:

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

Previous:GroupBy and Handle Missing data in Pandas.