NumpyFunction2.py

""".Suppose you have two sets of employee data—one containing information
about full-time employees and another containing information about part-time
employees. You want to combine this data to create a
comprehensive employee dataset for HR analysis. """

import numpy as np

# Employee data for full-time employees
full_time_employees = np.array([
    [101, 'John Doe', 'Full-Time', 55000],
    [102, 'Jane Smith', 'Full-Time', 60000],
    [103, 'Mike Johnson', 'Full-Time', 52000]
])

# Employee data for part-time employees
part_time_employees = np.array([
    [201, 'Alice Brown', 'Part-Time', 25000],
    [202, 'Bob Wilson', 'Part-Time', 28000],
    [203, 'Emily Davis', 'Part-Time', 22000]
])

# Combine the two datasets
combined_employees = np.concatenate((full_time_employees, part_time_employees), axis=0)

# Display the combined dataset
print("Combined Employee Dataset:")
for employee in combined_employees:
    print(f"Employee ID: {employee[0]}, Name: {employee[1]}, Type: {employee[2]}, Salary: {employee[3]}")


"""Output:-
         Combined Employee Dataset:
Employee ID: 101, Name: John Doe, Type: Full-Time, Salary: 55000
Employee ID: 102, Name: Jane Smith, Type: Full-Time, Salary: 60000
Employee ID: 103, Name: Mike Johnson, Type: Full-Time, Salary: 52000
Employee ID: 201, Name: Alice Brown, Type: Part-Time, Salary: 25000
Employee ID: 202, Name: Bob Wilson, Type: Part-Time, Salary: 28000
Employee ID: 203, Name: Emily Davis, Type: Part-Time, Salary: 22000 """