Summary  
This chapter covers using NumPy’s C-based implementation and vectorized array operations to perform efficient numerical computations on sets of values at once.

General domain of usage  
Scientific 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

Master practical NumPy skills for data science by focusing on multidimensional array manipulation, vectorized mathematical operations, workflow optimization, and complex matrix building, with action-driven chapters and hands-on challenges.

Introduction to NumPy

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