Hello! In our journey through functional programming, we've explored currying, partial application, and callable objects. Today, we'll dive into an advanced example using callable objects in combination with Python's powerful higher-order functions and list comprehensions. The goal is to deepen your understanding of how to create and utilize callable objects to make your code more modular and reusable, especially in a real-world context like adjusting employee salaries.

By the end of this lesson, you'll be able to create complex callable objects, apply them to collections using higher-order functions, and understand the benefits of such an approach.

We start by defining a class for employees:

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1class Employee:
2    def __init__(self, name, salary):
3        self.name = name
4        self.salary = salary

This class holds basic details about an employee, which include their name and salary.

Let's create a callable class that increases salary by a certain factor:

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1class SalaryIncrease:
2    def __init__(self, factor):
3        self.factor = factor
4
5    def __call__(self, employee):
6        return Employee(employee.name, employee.salary * self.factor)

In this example:

• The constructor __init__(self, factor) initializes the factor used to increase the salary.
• The __call__ method takes an Employee object and returns a new Employee object with the updated salary.

Let's integrate the Employee class and the SalaryIncrease callable into a simple program. We need a collection of employees. We'll use a Python list:

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1employees = [
2    Employee("Alice", 30000),
3    Employee("Bob", 45000),
4    Employee("Charlie", 32000),
5    Employee("David", 52000),
6    Employee("Eve", 48000)
7]

To apply the 10% salary increase, we instantiate the SalaryIncrease callable:

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1increase_factor = 1.1  # 10% raise
2increase = SalaryIncrease(increase_factor)

We can apply our callable to each employee in the list using the map function:

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1increased_salaries = list(map(increase, employees))

The map function applies increase to each object in the employees list.

To print the results, we'll use a simple for loop:

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1for emp in increased_salaries:
2    print(f"{emp.name} now earns: {emp.salary}")
3# Output:
4# Alice now earns: 33000.0
5# Bob now earns: 49500.0
6# Charlie now earns: 35200.0
7# David now earns: 57200.0
8# Eve now earns: 52800.0

In this code:

• The for loop iterates over the transformed list.
• We print each employee's name and new salary.

Here is the complete program demonstrating all the concepts:

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1class Employee:
2    def __init__(self, name, salary):
3        self.name = name
4        self.salary = salary
5
6class SalaryIncrease:
7    def __init__(self, factor):
8        self.factor = factor
9
10    def __call__(self, employee):
11        return Employee(employee.name, employee.salary * self.factor)
12
13
14if __name__ == "__main__":
15    employees = [
16        Employee("Alice", 30000),
17        Employee("Bob", 45000),
18        Employee("Charlie", 32000),
19        Employee("David", 52000),
20        Employee("Eve", 48000)
21    ]
22
23    increase_factor = 1.1  # 10% raise
24    increase = SalaryIncrease(increase_factor)
25
26    increased_salaries = list(map(increase, employees))
27
28    for emp in increased_salaries:
29        print(f"{emp.name} now earns: {emp.salary}")
30    # Output:
31    # Alice now earns: 33000.0
32    # Bob now earns: 49500.0
33    # Charlie now earns: 35200.0
34    # David now earns: 57200.0
35    # Eve now earns: 52800.0

To recap, in this lesson we've:

• Defined and utilized callable objects in Python to make our code more modular and reusable.
• Applied these callable objects to collections using list comprehensions to create elegant and readable code.
• Printed the results using a standard for loop.

Now it's time to get hands-on practice. You'll move to practice sessions where you'll apply these concepts using your own IDE. This will help solidify your understanding and mastery of creating and using callable objects in Python. Happy coding!