Summary  
This chapter covers creating multi-dimensional arrays by passing nested lists to NumPy’s array() function to build 2D and 3D arrays.  

General domain of usage  
Scientific computing

## 2D Arrays

Let's now create a higher dimensional array, namely a **2D array**:

import numpy as np
# Creating a 2D array
array_2d = np.array([[1, 2, 3], [4, 5, 6]])
print(f'2-dimensional array: \n{array_2d}')

Basically, creating a higher-dimensional **NumPy** array involves passing a higher-dimensional **list** as the argument of the `array()` function.

Any NumPy array object is called an `ndarray`.

Note

We can think of it as a `2x3` **matrix**.

## 3D Array

Creating **3D arrays** is nearly identical to creating 2D arrays. The only difference is that we now need to pass a **3D list** as an argument:

import numpy as np
# Creating a 3D array
array_3d = np.array([
    [[1, 2, 3], [4, 5, 6], [7, 8, 9]],
    [[10, 11, 12], [13, 14, 15], [16, 17, 18]],
    [[19, 20, 21], [22, 23, 24], [25, 26, 27]]
])
print(f'3-dimensional array: \n{array_3d}')

However, visualizing a 3D array is a bit more complex, but it can still be done:

The array is `3x3x3`, which is why we have a **cube** with each side equal to 3.

In practice, the approach to handling 3D and higher-dimensional arrays is no different from handling 2D arrays.


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 dynamic test output"""
    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 код користувача
        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)
        cls.module_path = spec.origin

    def test_array_creation(self):
        """Перевіряємо, що створено 2D NumPy масив"""
        ok = hasattr(self.m, "array_2d")
        arr = getattr(self.m, "array_2d", None)

        type_ok = isinstance(arr, np.ndarray)
        dim_ok = type_ok and arr.ndim == 2
        not_empty = type_ok and arr.size > 0

        _dynamic_test(
            self,
            ok and type_ok and dim_ok and not_empty,
            "2D NumPy array created correctly",
            "2D NumPy array not created correctly",
        )


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.

Creating Higher Dimensional Arrays

2D Arrays

3D Array

Oplossing