Your grocery store's system is designed to track the inventory of items on different shelves using **tuples**, as tuples provide a way to ensure the data is **immutable** once set. 

However, the store needs to **monitor certain items** and track their quantities or positions to assist in restocking or rearranging.

You are tasked with analyzing and managing inventory data using tuples to determine certain metrics and take actions based on specific criteria.

import unittest
import importlib
import io
import re
from contextlib import redirect_stdout
import inspect
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)


def strip_comments_from_source(module):
    try:
        source = inspect.getsource(module)
        lines = source.split('\\n')
        cleaned = []
        for line in lines:
            stripped = line.strip()
            if stripped.startswith('#'):
                continue
            if '#' in line:
                line = line.split('#')[0]
            if line.strip():
                cleaned.append(line)
        return '\\n'.join(cleaned)
    except Exception:
        return ""


class TestFruitShelfTuple(unittest.TestCase):

    def normalize_text(self, text):
        text = text.lower()
        text = re.sub(r'\s+', ' ', text)
        text = re.sub(r'\s*([,:?\[\]\(\)])\s*', r'\1', text)
        return text.strip()

    def test_apple_count(self):
        importlib.reload(user_code)
        _dynamic_test(
            self,
            hasattr(user_code, 'apple_count') and user_code.apple_count == 3,
            "apple_count is correct (3)",
            f"Expected apple_count to be 3, got {getattr(user_code, 'apple_count', None)}"
        )

    def test_banana_index(self):
        importlib.reload(user_code)
        _dynamic_test(
            self,
            hasattr(user_code, 'banana_index') and user_code.banana_index == 2,
            "banana_index is correct (2)",
            f"Expected banana_index to be 2, got {getattr(user_code, 'banana_index', None)}"
        )

    def test_grape_count_logic(self):
        importlib.reload(user_code)
        grape_count = user_code.shelf.count("grapes")
        _dynamic_test(
            self,
            grape_count == 1,
            "grape_count is correct and equals 1",
            f"Expected grape count to be 1, got {grape_count}"
        )

    def test_oranges_in_shelf(self):
        importlib.reload(user_code)
        _dynamic_test(
            self,
            "oranges" in user_code.shelf,
            "'oranges' is present in shelf",
            "'oranges' expected in shelf, but not found"
        )

    def test_print_output(self):
        f = io.StringIO()
        with redirect_stdout(f):
            importlib.reload(user_code)
        output = self.normalize_text(f.getvalue())

        expected_fragments = [
            self.normalize_text("Number of Apples: 3"),
            self.normalize_text("First Banana Index: 2"),
            self.normalize_text("Apples need to be restocked."),
            self.normalize_text("Grapes need to be restocked."),
            self.normalize_text("Oranges are at index: 1"),
        ]

        for expected in expected_fragments:
            _dynamic_test(
                self,
                expected in output,
                f"Output includes: {expected}",
                f"Expected output to include '{expected}', but got: '{output}'"
            )

    def test_in_operator_used(self):
        source = strip_comments_from_source(user_code)
        found = re.search(r'\s+in\s+', source)
        _dynamic_test(
            self,
            found is not None,
            "'in' operator is used to check for 'oranges'",
            "Expected use of 'in' to check for 'oranges'"
        )


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

test_main.py

You can use the `in` and `not in` operators with both **tuples** and **lists**.

Note

Gain a solid foundation in Python for data analysis. Learn to work with variables, data types, strings, conditional statements, loops, core data structures, and functions using examples relevant to analytical tasks.

Challenge: Tuple Operations

Output Requirements

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