Module 1 · Lesson 1

Why NumPy? ndarray vs. Python list

A Python list is a flexible, general-purpose container: each element is a separate Python object, and the list itself is just an array of pointers to those objects. That flexibility has a cost — looping over a list in pure Python re-enters the interpreter for every single element.

A NumPy ndarray is a fixed-type, contiguous block of memory. Because every element has the same dtype and a known size, NumPy can hand the whole loop off to compiled C code — this is called vectorization, and it’s the single most important idea in this course.

# Same logical operation, two very different implementations.
a = [1, 2, 3, 4, 5]
b = [10, 20, 30, 40, 50]

# Python list: no "+" between lists means elementwise addition — you need a loop.
elementwise_sum_list = [x + y for x, y in zip(a, b)]
print("list comprehension:", elementwise_sum_list)

# NumPy array: "+" IS elementwise addition, done in compiled code.
arr_a = np.array(a)
arr_b = np.array(b)
print("vectorized:        ", arr_a + arr_b)

Output:

list comprehension: [11, 22, 33, 44, 55]
vectorized:         [11 22 33 44 55]

import time

big_a = list(range(2_000_000))
big_b = list(range(2_000_000))

start = time.perf_counter()
_ = [x + y for x, y in zip(big_a, big_b)]
print(f"Python loop:   {time.perf_counter() - start:.4f}s")

np_a = np.array(big_a)
np_b = np.array(big_b)

start = time.perf_counter()
_ = np_a + np_b
print(f"NumPy vectorized: {time.perf_counter() - start:.4f}s")

Output:

Python loop:   0.0945s
NumPy vectorized: 0.0146s

One more comparison you’ll use constantly — including in this course’s own “check” cells, starting with the very next exercise: == between two arrays compares elementwise and gives you back an array of booleans, not a single answer. When you want one True/False for “are these two arrays the same?”, use np.array_equal(a, b) instead — it checks that both arrays have the same shape and that every element matches.

a = np.array([1, 2, 3])
b = np.array([1, 2, 3])

print(a == b)                  # elementwise: array([ True,  True,  True])
print(np.array_equal(a, b))    # one answer: True

Output:

[ True  True  True]
True

Write elementwise_product(a, b) that takes two same-length Python lists of numbers and returns a NumPy array containing their elementwise product. You may not use a for loop, while loop, or list/generator comprehension anywhere in your implementation — convert to arrays and let NumPy do the work.

💡 Hint

Convert both lists to arrays with np.array(...), then use the * operator.

✅ Solution

def elementwise_product(a, b):
    return np.array(a) * np.array(b)

Why NumPy? ndarray vs. Python list

No loops allowed