Lambda functions are anonymous functions, meaning they don't have a name. They are created using the `lambda` keyword and are often used to define short functions where you can specify a function on the spot.

The basic syntax of a lambda function is as follows:

```python
lambda arguments: expression
```

- `lambda`: the keyword indicating the start of a lambda function definition;
- `arguments`: the list of arguments the function takes;
- `expression`: the expression executed when the function is called. The result of the expression is returned as the function's value.

The key feature of lambda functions is their concise syntax. They are convenient when you need to define a simple function without writing a lot of code.

square = lambda x: x**2
result = square(5)
print(result) 

import unittest

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 TestApplyTax(unittest.TestCase):
    def test_apply_tax_single_value(self):
        """Checks tax deduction on a single price"""
        import user_code
        try:
            result = user_code.apply_tax(200)
            expected_output = 174.0
            condition = result == expected_output
            failure_message = f"For input `200`, expected `{expected_output}`, but got `{result}`."
        except AttributeError:
            condition = False
            failure_message = "The function `apply_tax` is not defined."
        _dynamic_test(self, condition, "The function correctly calculates tax for a single price.", failure_message)
    
    def test_apply_tax_zero(self):
        """Checks that tax deduction on zero returns zero"""
        import user_code
        try:
            result = user_code.apply_tax(0)
            expected_output = 0.0
            condition = result == expected_output
            failure_message = f"For input `0`, expected `{expected_output}`, but got `{result}`."
        except AttributeError:
            condition = False
            failure_message = "The function `apply_tax` is not defined."
        _dynamic_test(self, condition, "The function correctly handles zero input.", failure_message)

    def test_negative_price(self):
        """Checks that negative prices are handled correctly and return 0"""
        import user_code
        result = user_code.apply_tax(-123)
        expected_output = 0
        condition = result == expected_output
        failure_message = f"For input `-123`, expected `{expected_output}`, but got `{result}`."
        _dynamic_test(self, condition, "The function correctly handles negative values.", failure_message)
    
    def test_apply_tax_is_declared(self):
        import user_code
        _dynamic_test(
            self,
            hasattr(user_code, 'apply_tax'),
            "The `apply_tax` variable is declared.",
            "Expected `apply_tax` to be declared."
        )
if __name__ == '__main__':
    unittest.main()

test_main.py

You'll have a solid grasp of how important functions are in Python, no matter your field in computer science. We'll cover creating functions, defining arguments, handling return values, and exploring their use in Python.

We will consider what a function in Python and some basic techniques used to create functions is: what are the arguments of the function, what is the return value of the function, what is the body of the function, and how to use the function in the code.

Learn how to define functions in Python using positional and optional arguments. Master the flexibility of creating functions that adapt to different input scenarios.

Learn to work with arbitrary arguments and keyword arguments in Python to handle variable numbers of inputs. Understand how arbitrary arguments manage flexible positional inputs and how keyword arguments organize named arguments into a dictionary for efficient function design.

The return value of a function is the direct result of the execution of the function, which we can use in the program. There are several types of function return values - we will consider the most commonly used.

Explore the power of recursion and lambda functions in Python. Understand how recursion allows a function to call itself for solving complex problems, and how lambda functions provide a concise way to define anonymous functions for simple tasks.

Lambda Functions

Solution

Lambda Functions

Solution