Write a program in Python to find the minimum age of an employee id and salary in a given DataFrame

Finding the employee with the minimum age in a DataFrame is a common data analysis task. We can use pandas boolean indexing to filter rows where the age equals the minimum age value.

Problem Statement

Given a DataFrame with employee data (Id, Age, Salary), we need to find the Id and Salary of the employee with the minimum age.

Input DataFrame

   Id  Age  Salary
0   1   27   40000
1   2   22   25000
2   3   25   40000
3   4   23   35000
4   5   24   30000
5   6   32   30000
6   7   30   50000
7   8   28   20000
8   9   29   32000
9  10   27   23000

Expected Output

   Id  Salary
1   2   25000

Solution

To solve this problem, we follow these steps:

• Create a DataFrame with employee data

• Find rows where Age equals the minimum age using boolean indexing

• Select only the Id and Salary columns from the filtered result

Step-by-Step Approach

First, we filter rows where the age equals the minimum age ?

result = df[df['Age'] == df['Age'].min()]

Then, we select only the required columns ?

result[['Id', 'Salary']]

Complete Example

import pandas as pd

# Create DataFrame with employee data
data = [[1,27,40000],[2,22,25000],[3,25,40000],[4,23,35000],[5,24,30000],
        [6,32,30000],[7,30,50000],[8,28,20000],[9,29,32000],[10,27,23000]]
df = pd.DataFrame(data, columns=['Id', 'Age', 'Salary'])

print("DataFrame is:")
print(df)

# Find employee with minimum age
result = df[df['Age'] == df['Age'].min()]
print("\nEmployee with minimum age (Id and Salary):")
print(result[['Id', 'Salary']])

DataFrame is:
   Id  Age  Salary
0   1   27   40000
1   2   22   25000
2   3   25   40000
3   4   23   35000
4   5   24   30000
5   6   32   30000
6   7   30   50000
7   8   28   20000
8   9   29   32000
9  10   27   23000

Employee with minimum age (Id and Salary):
   Id  Salary
1   2   25000

How It Works

The solution uses pandas boolean indexing:

• df['Age'].min() finds the minimum age value (22)

• df['Age'] == df['Age'].min() creates a boolean mask

• df[boolean_mask] filters rows where the condition is True

• result[['Id', 'Salary']] selects specific columns from the filtered result

Conclusion

Use boolean indexing with df['Age'] == df['Age'].min() to find rows with minimum age. Select specific columns using double square brackets to get the final result with Id and Salary information.