Summary
This chapter explains how to assign values to 1D and 2D subarrays of arrays—whether defined by slices, integer indexing, or boolean indexing—using scalars or arrays of compatible shape, with automatic broadcasting when a scalar or 1D array is assigned to a larger slice.

General domain of usage
Numerical computing

With indexed arrays, things start getting more interesting. Here we'll focus on 1D and 2D **subarrays**, as 3D subarrays are rarely used in practice.

Let's first start with assigning values to **slices**. The general syntax looks like this: `slice = values`, where `slice` is a slice of a certain array and `values` are the values to be assigned.

Possible formats of `values`:
* a **single scalar** (number);
* a **1D array** of the same size as the slice (if it is 1D); or the size of the second dimension (if the slice is 2D);
* a **2D array** of the same shape as a 2D slice.

import numpy as np
array_1d = np.array([1, 4, 6, 2, 9])
# Assigning an array to the slice of array_1d
array_1d[1:-1] = np.array([3, 5, 7])
print(array_1d)
# Assigning a scalar to the slice of array_1d
array_1d[1:-1] = 5
print(array_1d)
array_2d = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12]])
# Assigning a 2D array to the slice of array_2d
array_2d[1:3, 1:] = np.array([[20, 21], [40, 41]])
print(array_2d)
# Assigning a 1D array to the slice of array_2d
array_2d[1:3, 1:] = [50, 51]
print(array_2d)
# Assigning a scalar to the slice of array_2d
array_2d[1:3, 1:] = 30
print(array_2d)

When we assign a **scalar** to a 1D slice, this scalar is assigned to **every element** of the slice. When a **1D array** is assigned to a **2D slice**, this 1D array is assigned to **every 1D array** in the slice. Assigning a **scalar** to a 2D slice works the same as with a 1D slice.

Assigning values to **integer array indexed** subarrays works the same way as with slices. Assigning values to **boolean indexed** subarrays works the same way as with 1D slices.

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 output for test results"""
    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 значення
        cls.init_prices = np.array([15, 8, 22, 7, 12, 5])
        cls.expected_prices = cls.init_prices.copy()
        cls.expected_prices[cls.init_prices > 10] = 20

        cls.init_ratings = np.array([[6, 8, 9], [7, 5, 10]])
        cls.expected_ratings = cls.init_ratings.copy()
        cls.expected_ratings[1, 1:] = np.array([9, 8])

    def test_prices_update(self):
        """1. Assign 20 to every element in prices greater than 10"""
        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()

        prices = getattr(module, "prices", None)
        type_ok = isinstance(prices, np.ndarray)
        val_ok = type_ok and np.array_equal(prices, self.expected_prices)

        # Перевіряємо, що всі значення > 10 стали 20
        logic_ok = type_ok and np.all(prices[self.init_prices > 10] == 20)

        # Перевіряємо наявність друку
        printed_ok = "20" in output

        _dynamic_test(
            self,
            type_ok and val_ok and logic_ok and printed_ok,
            "prices updated and printed correctly",
            "prices update incorrect or print missing",
        )

    def test_ratings_update(self):
        """2. Assign [9, 8] to last two elements of second row using positive row index and slice"""
        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()

        ratings = getattr(module, "ratings", None)
        type_ok = isinstance(ratings, np.ndarray)
        val_ok = type_ok and np.array_equal(ratings, self.expected_ratings)

        # Перевірка: змінилася лише друга стрічка
        second_row_correct = type_ok and np.array_equal(ratings[1, 1:], np.array([9, 8]))
        first_row_unchanged = type_ok and np.array_equal(ratings[0], np.array([6, 8, 9]))

        printed_ok = "9" in output and "8" in output

        _dynamic_test(
            self,
            type_ok and val_ok and second_row_correct and first_row_unchanged and printed_ok,
            "ratings updated and printed correctly",
            "ratings update incorrect or print 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.

Assigning Values to Indexed Subarrays

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