Dictionaries also offer a handy `clear()` method that removes all items, leaving you with an empty dictionary. Here's how it works.

# Creating a dictionary for book details
book = {
    "title": "Pride and Prejudice",
    "author": "Jane Austen",
    "year": 1813,
    "genre": "Romance"
}

# Printing the dictionary before clearing
print("Before clearing:", book)

# Clearing all items from the dictionary
book.clear()

# Printing the dictionary after clearing
print("After clearing:", book)

import unittest
import importlib

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 TestUserCode(unittest.TestCase):

    def test_authors_books_is_declared(self):
        import user_code
        _dynamic_test(
            self,
            hasattr(user_code, 'authors_books'),
            "The `authors_books` dictionary is declared.",
            "Expected `authors_books` to be declared."
        )

    def test_authors_books_is_dict(self):
        import user_code
        _dynamic_test(
            self,
            isinstance(user_code.authors_books, dict),
            "The `authors_books` is of type `dict`.",
            "Expected `authors_books` to be of type `dict`."
        )

    def test_authors_books_is_empty(self):
        import user_code
        _dynamic_test(
            self,
            len(user_code.authors_books) == 0,
            "The `authors_books` dictionary is empty after using `clear()`.",
            "Expected `authors_books` to be empty after using `clear()`, but it is not."
        )

    def test_clear_method_used(self):
        with open('user_code.py', 'r') as file:
            user_code_source = file.read()
        _dynamic_test(
            self,
            ".clear(" in user_code_source,
            "The `clear()` method is used to empty the dictionary.",
            "Expected the `clear()` method to be used to empty the dictionary."
        )

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


test_main.py

In this course, you'll get to know the fundamental data structures of the Python programming language. We'll explore utilizing Python's native data structures like lists, dictionaries, tuples, and sets to tackle different challenges.

What is a list? A list is an ordered collection that can be modified. It also allows for duplicate elements.

What is a dictionary? A dictionary is an ordered collection that can be changed, but it doesn't allow duplicate entries. It consists of `key:value` pairs, which are used to optimize its structure.

What is a tuple? A tuple is an ordered collection that cannot be altered. It can have duplicate elements. While it's similar to a list, the values in a tuple can be of any type and are indexed by integers. Although not mandatory, it's common to enclose the values of a tuple in parentheses, making them easier to understand in Python.

What is a set? In Python, a `set` is an unordered collection that is both `iterable` and `mutable`, with no repeated elements. While the order of items in a set isn't fixed, it can include a variety of elements. A key benefit of using sets over lists is their highly efficient method for determining if an element exists within the set.

Python Data Structures

The clear() Method

Solution

The clear() Method

Solution