Understanding the difference between **mutable** and **immutable** data types is crucial. Mutable data types can be changed after their creation, while immutable data types cannot be modified once created.

**Immutable** data types cannot be changed once they're created. In contrast, **mutable** data types can be altered after their creation.

To conclude, `int` and `float` are examples of immutable data types in Python. Reassigning a variable of these types does not alter their immutability.

# Define a variable `var1` with an initial value
var1 = 40
print(var1)  # output: 40

# Change the value of `var1`
var1 = 50
print(var1)  # output: 50

import unittest
import re
import user_code
from io import StringIO
from unittest.mock import patch

# Helper function to dynamically generate test names and assertions
def _dynamic_test(test_case, condition, success_message, failure_message):
    if condition:
        test_case._testMethodName = success_message  # Rename the test for better readability
        test_case.assertTrue(True, success_message)  # Pass the test with a success message
    else:
        test_case._testMethodName = failure_message  # Rename the test with a failure description
        test_case.fail(failure_message)  # Fail the test with a failure message


class TestUserCode(unittest.TestCase):

    # Test if `project_budget` is declared
    def test_project_budget_is_declared(self):
        _dynamic_test(
            self,
            hasattr(user_code, 'project_budget'),
            "The `project_budget` variable is declared.",
            "Expected `project_budget` to be declared."
        )

    # Test if `project_budget` is equal to 150
    def test_project_budget_value_is_150(self):
        _dynamic_test(
            self,
            user_code.project_budget == 150,
            "The `project_budget` is equal to 150.",
            f"Expected `project_budget` to be 150, but got `{user_code.project_budget}`."
        )

    # Test if `print()` is used to output `project_budget` correctly
    def test_print_is_used_for_project_budget(self):
        with open('user_code.py', 'r') as file:
            user_code_source = file.read()
        _dynamic_test(
            self,
            re.search(r'print\s*\(\s*project_budget\s*\)', user_code_source),
            "The `print()` function is used to output `project_budget`.",
            "Expected `print()` to be used for `project_budget`."
        )

    # Test if the output of `print(project_budget)` is correct
    def test_project_budget_print_output(self):
        with patch('sys.stdout', new=StringIO()) as fake_out:
            import importlib
            importlib.reload(user_code)
            output = fake_out.getvalue().strip()
            _dynamic_test(
                self,
                output == '150',
                "The output of `print(project_budget)` is `150`.",
                f"Expected output to be `150`, but got `{output}`."
            )


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

test_main.py

This course is going to help you dive deeper into data types and try to work with them. Here you will learn basic operations and all ins and outs of numbers, boolean data type, and strings. This course consists of a lot of practical tasks after which you will become a skillful programmer.

Numerical data types are fundamental in Python, forming the backbone of many computational tasks. In this section, you'll dive into the essentials of working with integers and floating-point numbers, performing basic mathematical operations, and understanding the real-world applications of numerical data. You'll also explore critical concepts such as immutability and gain hands-on experience with Python’s built-in functions and tools for handling numbers effectively.

The main goal of this section is to clarify the boolean data types; it's a sub type of numeric data, but indeed it is more challenging. Since it is always better to excel in the more difficult topics, this section is a valuable part of your curriculum.

Strings are a way for us to implement your phrases in Python, that is, to give instructions to users who are the target audience of your program. Indeed, it is always better to be able to work with strings because they should be used in your code. You'll understand why soon.

This chapter consists of practical tasks that combine all the examined topics together with a bit of theory.

Exploring Mutability in Python Data Types

Soluzione

Exploring Mutability in Python Data Types

Soluzione