Vectorized operations vs loops in Data Analysis Python - Practice Questions

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of vectorized addition vs loop addition

What is the output of the following code comparing vectorized addition with a loop?

Data Analysis Python

import numpy as np
arr = np.array([1, 2, 3, 4])
result_vectorized = arr + 5
result_loop = []
for x in arr:
    result_loop.append(x + 5)
print(result_vectorized)
print(result_loop)

A

[6 7 8 9]
[6, 7, 8, 9]

B

[6 7 8 9]
[5, 6, 7, 8]

C

[6 7 8 9]
[1, 2, 3, 4]

D Error: unsupported operand type(s) for +: 'int' and 'list'

💡 Hint

Think about how numpy arrays handle addition compared to lists.

The numpy array adds 5 to each element directly, producing [6 7 8 9]. The loop appends each element plus 5 to a list, resulting in [6, 7, 8, 9].

❓ data_output

intermediate

Resulting array shape after vectorized multiplication

Given the code below, what is the shape of the resulting array after vectorized multiplication?

Data Analysis Python

import numpy as np
arr1 = np.array([[1, 2], [3, 4]])
arr2 = np.array([10, 20])
result = arr1 * arr2
print(result.shape)

A (2, 2)

B (1, 2)

C (2,)

D Error: operands could not be broadcast together

💡 Hint

Consider how numpy broadcasts the smaller array to match the larger array's shape.

The array arr2 is broadcast across the rows of arr1, so the result keeps the shape (2, 2).

🔧 Debug

advanced

Identify the error in loop vs vectorized code

What error will the following code produce?

Data Analysis Python

import numpy as np
arr = np.array([1, 2, 3])
result = []
for x in arr:
    result = result + x
print(result)

A SyntaxError: invalid syntax

B TypeError: can only concatenate list (not "int") to list

C [6]

D [1, 2, 3]

💡 Hint

Check how list addition works with integers.

The code tries to add an integer to a list using '+', which causes a TypeError because list concatenation requires another list.

🚀 Application

advanced

Performance difference between vectorized and loop sum

Which statement best describes the performance difference when summing a large array using vectorized numpy sum vs a Python loop?

A Vectorized sum is slower because it creates intermediate arrays.

B Both have the same speed because they do the same operations.

C Vectorized numpy sum is much faster because it uses optimized C code internally.

D Python loop is faster because it processes elements one by one.

💡 Hint

Think about how numpy is implemented under the hood.

Numpy's vectorized operations use optimized compiled code, making them much faster than Python loops for large data.

🧠 Conceptual

expert

Why vectorized operations reduce memory overhead

Why do vectorized operations typically use less memory than equivalent Python loops when processing large datasets?

A Because loops use less memory by processing one element at a time without storing results.

B Because loops automatically compress data during iteration.

C Because vectorized operations store data in multiple copies to speed up access.

D Because vectorized operations avoid creating intermediate Python objects and operate on contiguous memory blocks.

💡 Hint

Consider how numpy arrays store data compared to Python lists.

Vectorized operations work on contiguous blocks of memory and avoid overhead of Python objects, reducing memory use.