Summary  
This chapter covers how a C‐backed array library enables fast numerical operations and uses vectorization to operate on entire arrays at once for performance gains.

General domain of usage  
Numerical computing

In a world full of data, working with matrices and arrays is extremely important. That's where **NumPy** comes in handy. With its blazing speed and relatively easy-to-use interface, it has become the **most used** Python library for working with arrays.

Let's now discuss the speed of NumPy and where it comes from. Despite being a Python library, it is mostly written in **C**, a low-level language that allows for fast computations.

Another contributing factor to NumPy's speed is **vectorization**. Essentially, vectorization involves transforming an algorithm from operating on a single value at a time to operating on a **set of values** (vector) at once, which is performed under the hood at the CPU level.

import unittest
import importlib.util
import re

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):
    @classmethod
    def setUpClass(cls):
        # Завантажуємо 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)

        # Зчитуємо сирцевий код
        with open("user_code.py", "r", encoding="utf-8") as f:
            cls.src = f.read()

    def test_numpy_import(self):
        # Перевірка, що numpy імпортовано з псевдонімом np
        has_np = "np" in dir(self.m)
        pat_alias = re.search(r"import\s+numpy\s+as\s+np", self.src)
        _dynamic_test(
            self,
            has_np and pat_alias,
            "NumPy imported correctly as np",
            "NumPy import alias incorrect or missing"
        )

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.

Introduction to NumPy

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