# Using GroupBy with Lambda functions in Pandas

## Pandas Advanced Grouping and Aggregation: Exercise-7 with Solution

Using GroupBy with Lambda functions:

Write a Pandas program to use lambda functions within groupby for flexible and efficient data transformations.

**Sample Solution:**

**Python Code :**

```
import pandas as pd
# Sample DataFrame
data = {'Category': ['A', 'A', 'B', 'B', 'C', 'C'],
'Value': [5, 15, 25, 35, 45, 55]}
df = pd.DataFrame(data)
print("Sample DataFrame:")
print(df)
# Group by 'Category' and apply lambda function
print("\nGroup by 'Category' and apply lambda function:")
grouped = df.groupby('Category').agg(lambda x: x.max() - x.min())
print(grouped)
```

Output:

Sample DataFrame: Category Value 0 A 5 1 A 15 2 B 25 3 B 35 4 C 45 5 C 55 Group by 'Category' and apply lambda function: Value Category A 10 B 10 C 10

**Explanation:**

- Import pandas.
- Create a sample DataFrame.
- Group by 'Category'.
- Apply a lambda function to calculate the range of values.
- Print the result.

**Python Code Editor:**

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**Next:** Grouping and Aggregating with multiple Index Levels in Pandas.

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