Summary  
This chapter explains how to use the floor division (`//`) and modulus (`%`) operators to compute integer quotients and remainders in code.  

General domain of usage  
Budgeting and resource allocation

This chapter explores essential mathematical operations, such as **floor division** (`//`) and **modulus** (`%`). These operations are crucial for various calculations, especially in financial planning and resource allocation.

### Floor Division (`//`)

Floor division, denoted by the `//` operator, helps determine how many whole times one number fits into another. 

Imagine a company has a budget of `38000` dollars and needs to allocate funds to different departments, each requiring `7000` dollars. Using floor division, you can calculate how many departments can be fully funded:

departments = 38000 // 7000
print(departments)  # output: 5

In this case, the company can fully fund `5` departments.

### Modulus (`%`)

The modulus operation, represented by the `%` operator, calculates the remainder after division. This is useful for determining the remaining funds after allocating resources.

Continuing with the previous scenario, after allocating `35000` dollars to `5` departments, you can calculate the remaining budget:

remaining_budget = 38000 % 7000
print(remaining_budget)  # output: 3000

Here, the remainder is `3000` dollars, meaning the company has `3000` dollars left after funding the departments.

### Practical Applications

**Floor Division** is often used in scenarios requiring equal distribution of resources, such as budgeting, project funding, or inventory management.

**Modulus** is useful for tasks like determining leftover resources, handling periodic financial reviews, or calculating residual values in financial models.

By mastering these operations, you can enhance your financial planning and resource allocation strategies, making your code more efficient and effective.


import unittest
import importlib
import re

# 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):

    def setUp(self):
        # Reload the module to make sure we are testing the latest code
        self.module = importlib.import_module('user_code')
        importlib.reload(self.module)

    # Test if `completed` is declared
    def test_completed_is_declared(self):
        _dynamic_test(
            self,
            hasattr(self.module, 'completed'),
            "The `completed` variable is declared.",
            "Expected `completed` variable to be declared."
        )

    # Test if `minutes` is declared
    def test_minutes_is_declared(self):
        _dynamic_test(
            self,
            hasattr(self.module, 'minutes'),
            "The `minutes` variable is declared.",
            "Expected `minutes` variable to be declared."
        )

    # Test if `completed` is an integer
    def test_completed_is_integer(self):
        _dynamic_test(
            self,
            hasattr(self.module, 'completed') and isinstance(self.module.completed, int),
            "The `completed` is of type `int`.",
            "Expected `completed` to be of type `int`."
        )

    # Test if `minutes` is an integer
    def test_minutes_is_integer(self):
        _dynamic_test(
            self,
            hasattr(self.module, 'minutes') and isinstance(self.module.minutes, int),
            "The `minutes` is of type `int`.",
            "Expected `minutes` to be of type `int`."
        )

    # Test if `completed` has the correct value
    def test_completed_correct_value(self):
        expected_value = 60 // 7  # 60 minutes divided by 7 minutes per transaction
        _dynamic_test(
            self,
            hasattr(self.module, 'completed') and self.module.completed == expected_value,
            f"The `completed` is `{expected_value}`.",
            f"Expected `completed` to be `{expected_value}`, but got `{getattr(self.module, 'completed', 'undefined')}`."
        )

    # Test if `minutes` has the correct value
    def test_minutes_correct_value(self):
        expected_value = 60 % 7  # Remaining minutes after completing transactions
        _dynamic_test(
            self,
            hasattr(self.module, 'minutes') and self.module.minutes == expected_value,
            f"The `minutes` is `{expected_value}`.",
            f"Expected `minutes` to be `{expected_value}`, but got `{getattr(self.module, 'minutes', 'undefined')}`."
        )

    # Test if floor division `//` is used for `completed`
    def test_floor_division_used(self):
        with open('user_code.py', 'r') as file:
            user_code_source = file.read()
        _dynamic_test(
            self,
            re.search(r'completed\s*=\s*[\d\w_]+\s*//\s*[\d\w_]+', user_code_source),
            "The floor division `//` is used for `completed`.",
            "Expected floor division `//` to be used for `completed`."
        )

    # Test if modulo `%` is used for `minutes`
    def test_modulo_used(self):
        with open('user_code.py', 'r') as file:
            user_code_source = file.read()
        _dynamic_test(
            self,
            re.search(r'minutes\s*=\s*[\d\w_]+\s*%\s*[\d\w_]+', user_code_source),
            "The modulo `%` is used for `minutes`.",
            "Expected modulo `%` to be used for `minutes`."
        )

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

test_main.py

Gain a solid understanding of Python's core data types and learn how to work with them effectively. Explore numbers, booleans, and strings through hands-on exercises and practical examples. Build confidence in performing basic operations and develop the foundational skills needed to write clean and efficient Python code.

Discover the essentials of numerical data types in Python and learn how to work with integers and floating-point numbers. Master basic arithmetic operations, explore the concept of immutability, and get hands-on experience using Python's built-in functions and tools to handle numbers efficiently in real-world scenarios.

Gain a clear understanding of the Boolean data type, a specialized subtype of numeric data that represents truth values. Although it may seem challenging at first, mastering Booleans is essential for writing logical and efficient programs. This section will help you confidently apply Boolean logic to real-world programming scenarios.

Strings allow you to work with text in Python, from displaying messages to processing user input. You'll explore how to create, modify, and manipulate strings effectively. Understanding strings is essential for writing interactive programs and communicating information clearly through your code.

Learn how to handle data types effectively to ensure accuracy and consistency in your programs. Convert values to the appropriate type, clean text before comparison, and separate numeric operations from text formatting. You'll also discover how to manage missing data deliberately and handle human-readable text differently from raw input, achieving reliable, consistent, and easy-to-read results.

Deep Dive into Advanced Math Techniques in Python

Floor Division (`//`)

Modulus (`%`)

Practical Applications

解答

Deep Dive into Advanced Math Techniques in Python

Floor Division (//)

Modulus (%)

Practical Applications

解答

Floor Division (`//`)

Modulus (`%`)