Summary
This chapter explains how to use **kwargs in function definitions to accept any number of named arguments and handle them as a dictionary.

General domain of usage
Designing flexible APIs or libraries that accept optional configuration parameters.

In programming, there is a special syntax for passing any number of named parameters to a function, known as `**kwargs`. 

`**kwargs` allows a function to accept any number of named arguments and treat them as a dictionary.

def example_function(**kwargs):
    for key, value in kwargs.items():
        print(f'{key}: {value}')

# Example function call
example_function(name='John', age=25, city='New York')

In this example, `**kwargs` receives named arguments and prints their keys and values.



The `.items()` method is used to obtain a list of key-value pairs from a dictionary in Python. Each element in this list is represented as a tuple (key, value).

Note

import unittest
import user_code

def _dynamic_test(test_case, condition, success_message, failure_message):
    if condition:
        test_case._testMethodName = success_message
        test_case.assertTrue(True, success_message)
    else:
        test_case._testMethodName = failure_message
        test_case.fail(failure_message)

class TestFilterProductsByBudget(unittest.TestCase):
    def test_products_within_budget(self):
        """Checks that only affordable products are returned."""
        try:
            result = user_code.filter_products_by_budget(100, laptop=1200, smartphone=800, mouse=50, keyboard=90, headphones=150)
            expected_output = "Available products within budget: {'mouse': 50, 'keyboard': 90}"
            condition = result == expected_output
            failure_message = f"For input `(100, laptop=1200, smartphone=800, mouse=50, keyboard=90, headphones=150)`, expected `{expected_output}`, but got `{result}`."
        except AttributeError:
            condition = False
            failure_message = "The function `filter_products_by_budget` is not defined."
        
        _dynamic_test(self, condition, "The function correctly filters products within budget.", failure_message)
    
    def test_no_affordable_products(self):
        """Checks that the function returns a message when no products are within budget."""
        try:
            result = user_code.filter_products_by_budget(10, laptop=1200, smartphone=800, mouse=50, keyboard=90, headphones=150)
            expected_output = "No products available within the budget."
            condition = result == expected_output
            failure_message = f"For input `(10, laptop=1200, smartphone=800, mouse=50, keyboard=90, headphones=150)`, expected `{expected_output}`, but got `{result}`."
        except AttributeError:
            condition = False
            failure_message = "The function `filter_products_by_budget` is not defined."
        
        _dynamic_test(self, condition, "The function correctly returns a message when no products are affordable.", failure_message)
    
    def test_all_products_affordable(self):
        """Checks that all products are returned if all are within budget."""
        try:
            result = user_code.filter_products_by_budget(500, mouse=50, keyboard=90, headphones=150)
            expected_output = "Available products within budget: {'mouse': 50, 'keyboard': 90, 'headphones': 150}"
            condition = result == expected_output
            failure_message = f"For input `(500, mouse=50, keyboard=90, headphones=150)`, expected `{expected_output}`, but got `{result}`."
        except AttributeError:
            condition = False
            failure_message = "The function `filter_products_by_budget` is not defined."
        
        _dynamic_test(self, condition, "The function correctly returns all products when all are affordable.", failure_message)

if __name__ == '__main__':
    unittest.main()

test_main.py

Gain a solid understanding of how functions shape Python programming. Master defining and calling functions, working with arguments, handling return values, and creating flexible, efficient code using recursion and lambda expressions.

Explore what functions are and why they're essential in Python. Learn how to create functions, define arguments, handle return values, and use built-in functions effectively.

Learn how positional and optional arguments work in Python. Build adaptable functions that handle different inputs and improve flexibility in your code.

Understand how to work with arbitrary and keyword arguments to handle variable input sizes. Learn how these techniques make your functions more dynamic and organized.

Discover how return values represent a function's output. Learn to return single or multiple values, use None, and explore generators for more advanced data flow control.

Unlock advanced function concepts with recursion and lambda functions. Learn how recursion solves repetitive problems and how lambda expressions create concise, one-line functions.

Arbitrary Keyword Arguments

Solution