Summary  
Using boolean masks with the same dimensions as a multi-dimensional array to select and retrieve elements that satisfy given logical conditions, resulting in a 1D array of matched values.

General domain of usage  
Data analysis

**Boolean indexing** in 2D and higher-dimensional arrays works similarly to 1D arrays. However, the boolean array must have the **same number of dimensions** as the initial array (e.g., 2D for two-dimensional arrays). The returned array, however, will be **1D**.

import numpy as np
array_2d = np.array([
   [1, 2, 3],
   [4, 5, 6],
   [7, 8, 9]
])
# Retrieving elements less than 3 or greater than OR greater than or equal to 8
print(array_2d[(array_2d < 3) | (array_2d >= 8)])

The **boolean array** on the right is the resulting boolean array of these two combined conditions. Once again, **purple** elements match the conditions, and **green** elements don't. So when applying `array_2d[(array_2d < 3) | (array_2d >= 8)]`, we get a 1D array of **purple** elements.

import unittest
import importlib.util
import numpy as np
import io
from contextlib import redirect_stdout


def _dynamic_test(test_case, condition, success_message, failure_message):
    """Codefinity-style formatted feedback"""
    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):
    @classmethod
    def setUpClass(cls):
        # Очікувані дані
        cls.city_temperatures = np.array([[12, 18, 25, 14],
                                          [32, 20, 28, 35]])
        cls.expected = cls.city_temperatures[
            (cls.city_temperatures <= 15) | (cls.city_temperatures > 30)
        ]

    def test_boolean_filter_and_print(self):
        """1–2. Boolean indexing applied correctly and printed"""
        # Виконуємо код користувача і перехоплюємо stdout
        f = io.StringIO()
        spec = importlib.util.spec_from_file_location("user_code", "user_code.py")
        module = importlib.util.module_from_spec(spec)
        with redirect_stdout(f):
            spec.loader.exec_module(module)
        output = f.getvalue().strip()

        # Перевірка, що результат містить усі очікувані значення
        expected_values = [str(x) for x in self.expected.flatten()]
        contains_expected = all(val in output for val in expected_values)

        # Перевірка, що вивід не містить інших (зайвих) елементів із початкового масиву
        disallowed = [x for x in self.city_temperatures.flatten() if x not in self.expected]
        contains_disallowed = any(str(x) in output for x in disallowed)

        _dynamic_test(
            self,
            contains_expected and not contains_disallowed,
            "Boolean indexing and print output correct",
            "Boolean indexing incorrect or print output missing/wrong",
        )


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


test_code.py

Develop essential skills for numerical computing using NumPy. Learn how to work with NumPy arrays, create and manipulate multidimensional data, apply indexing and slicing, perform vectorized operations, reshape arrays, use broadcasting, and apply basic linear algebra techniques. The course focuses on practical operations commonly used in data analysis tasks.

Boolean Indexing in 2D Arrays

Solution