Summary  
This chapter explains how to use multidimensional slice notation to extract subarrays from 2D arrays—specifying ranges and steps on each axis, combining slicing with basic indexing, and even reversing order.

General domain of usage  
Data analysis

Slicing in 2D and **higher-dimensional arrays** works similarly to slicing in 1D arrays. However, in 2D arrays, there are two axes.

If we want to perform slicing only on **axis 0** to retrieve 1D arrays, the syntax remains the same: `array[start:end:step]`. If we want to perform slicing on the elements of these 1D arrays (**axis 1**), the syntax is as follows: `array[start:end:step, start:end:step]`. Essentially, the number of slices corresponds to the **number of dimensions** of an array.

Moreover, we can use slicing for one axis and **basic indexing** for the other axis. Let's look at an example of 2D slicing (**purple** squares represent the elements retrieved from slicing, and the **black arrow** indicates that the elements are taken in reverse order):

import numpy as np
array_2d = np.array([
   [1, 2, 3, 4],
   [5, 6, 7, 8],
   [9, 10, 11, 12]
])
print(array_2d[1:])
print(array_2d[:, 0])
print(array_2d[1:, 1:-1])
print(array_2d[:-1, ::2])
print(array_2d[2, ::-1])

The picture below shows the structure of the `student_scores` array used in the task:

import unittest
import importlib.util
import numpy as np


def _dynamic_test(test_case, condition, success_message, failure_message):
    """Codefinity-style test message handler"""
    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):
        # Імпортуємо user_code
        spec = importlib.util.spec_from_file_location("user_code", "user_code.py")
        cls.m = importlib.util.module_from_spec(spec)
        spec.loader.exec_module(cls.m)

        # Очікувані дані
        cls.student_scores = np.array([
            [85, 92, 78],
            [88, 75, 83],
            [90, 88, 91]
        ])
        cls.expected_slice = cls.student_scores[0, 1:]  # [92, 78]

    def test_first_student_last_two_scores(self):
        """1–2. Slice includes last two scores of first student using positive indexing"""
        ok = hasattr(self.m, "first_student_last_two_scores")
        arr = getattr(self.m, "first_student_last_two_scores", None)

        type_ok = isinstance(arr, np.ndarray)
        val_ok = type_ok and np.array_equal(arr, self.expected_slice)
        shape_ok = type_ok and arr.shape == (2,)

        # Перевіряємо, що взято елементи саме з першого рядка (0) і стовпців з індексу 1+
        indices_correct = type_ok and np.array_equal(arr, self.student_scores[0][1:])

        _dynamic_test(
            self,
            ok and type_ok and shape_ok and val_ok and indices_correct,
            "first_student_last_two_scores slice created correctly",
            "first_student_last_two_scores slice incorrect",
        )


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.

Slicing in 2D Arrays

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