﻿ GroupBy and Apply multiple Aggregations with named functions in Pandas

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:

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