Why vectorized operations matter in NumPy - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a NumPy array from a Python list.

NumPy

import numpy as np
arr = np.[1]([1, 2, 3, 4, 5])

Drag options to blanks, or click blank then click option'

Aarray

Blist

Cmatrix

Drange

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'list' instead of 'array' will cause an error because NumPy has no 'list' function.

Using 'matrix' creates a matrix object, not a general array.

Using 'range' is a Python built-in, not a NumPy function.

The np.array function converts a Python list into a NumPy array, which allows vectorized operations.

2fill in blank

medium

Complete the code to add 5 to every element in the NumPy array using vectorized operations.

NumPy

import numpy as np
arr = np.array([1, 2, 3])
result = arr [1] 5

Drag options to blanks, or click blank then click option'

A*

B+

C-

D/

Attempts:

3 left

💡 Hint

Common Mistakes

Using '*' multiplies instead of adding.

Using '-' subtracts instead of adding.

Using '/' divides instead of adding.

Using the + operator adds 5 to each element of the array in a vectorized way.

3fill in blank

hard

Fix the error in the code to multiply two NumPy arrays element-wise.

NumPy

import numpy as np
arr1 = np.array([1, 2, 3])
arr2 = np.array([4, 5, 6])
result = arr1 [1] arr2

Drag options to blanks, or click blank then click option'

A/

B-

C*

D+

Attempts:

3 left

💡 Hint

Common Mistakes

Using '+' adds arrays instead of multiplying.

Using '-' subtracts arrays.

Using '/' divides arrays.

The * operator performs element-wise multiplication between two NumPy arrays.

4fill in blank

hard

Fill both blanks to create a vectorized operation that squares each element and then filters elements greater than 10.

NumPy

import numpy as np
arr = np.array([1, 2, 3, 4])
squared = arr [1] 2
filtered = squared[squared [2] 10]

Drag options to blanks, or click blank then click option'

A**

B>

C<

D*

Attempts:

3 left

💡 Hint

Common Mistakes

Using '*' instead of '**' will multiply by 2, not square.

Using '<' filters elements less than 10, not greater.

Use ** to square elements and > to filter elements greater than 10.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps each word to its length only if the length is greater than 3.

NumPy

words = ['data', 'is', 'fun', 'and', 'cool']
lengths = { [1] : [2] for [3] in words if len([3]) > 3 }

Drag options to blanks, or click blank then click option'

Aword

Blen(word)

Ditem

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'item' instead of 'word' causes a NameError.

Using the wrong variable name in the loop breaks the comprehension.

The comprehension uses word as the key, len(word) as the value, and iterates over word in words.

Practice

1. Why are vectorized operations in numpy preferred over using loops for array calculations?

easy

A. They run faster and use less code.

B. They require more memory but are easier to read.

C. They only work with small arrays.

D. They are slower but more accurate.

Solution

1. Step 1: Understand vectorized operations

   Vectorized operations apply calculations to whole arrays at once, avoiding explicit loops.

2. Step 2: Compare speed and code simplicity

   Because numpy uses optimized C code internally, vectorized operations run faster and require less code than loops.

3. Final Answer:

   They run faster and use less code. -> Option A

4. Quick Check:

   Vectorized = faster + simpler [OK]

Hint: Vectorized means fast and simple code [OK]

Common Mistakes:

• Thinking vectorized operations are slower
• Believing they only work for small arrays
• Confusing memory use with speed

2. Which of the following is the correct way to add two numpy arrays a and b using vectorized operations?

easy

A. c = a + b

B. c = np.add(a, b)

C. c = [a[i] + b[i] for i in range(len(a))]

D. Both A and B

Solution

1. Step 1: Identify vectorized addition methods

   Both a + b and np.add(a, b) perform element-wise addition efficiently.

2. Step 2: Compare with loop method

   The list comprehension adds elements but is not vectorized and slower.

3. Final Answer:

   Both A and B -> Option D

4. Quick Check:

   Vectorized addition = a + b or np.add(a, b) [OK]

Hint: Use + or np.add for vectorized addition [OK]

Common Mistakes:

• Choosing list comprehension as vectorized
• Thinking only np.add works
• Using loops unnecessarily

3. What will be the output of the following code?

import numpy as np
x = np.array([1, 2, 3])
y = np.array([4, 5, 6])
z = x * y
print(z)

medium

A. [5 7 9]

B. [1 2 3 4 5 6]

C. [4 10 18]

D. [4 5 6]

Solution

1. Step 1: Understand element-wise multiplication

   Multiplying two numpy arrays multiplies each element by the corresponding element in the other array.

2. Step 2: Calculate each element

   1*4=4, 2*5=10, 3*6=18, so the result is [4 10 18].

3. Final Answer:

   [4 10 18] -> Option C

4. Quick Check:

   Element-wise multiply = [4 10 18] [OK]

Hint: Array * array multiplies elements pairwise [OK]

Common Mistakes:

• Adding instead of multiplying
• Concatenating arrays by mistake
• Confusing element-wise with dot product

4. The following code is intended to add two numpy arrays element-wise. What is the error?

import numpy as np
x = np.array([1, 2, 3])
y = np.array([4, 5])
z = x + y
print(z)

medium

A. You cannot add numpy arrays with + operator.

B. Arrays have different shapes causing a broadcasting error.

C. The arrays must be converted to lists first.

D. The print statement is missing parentheses.

Solution

1. Step 1: Check array shapes

   Array x has shape (3,), y has shape (2,), so shapes differ.

2. Step 2: Understand broadcasting rules

   Arrays must be compatible in shape to add; here they are not, causing an error.

3. Final Answer:

   Arrays have different shapes causing a broadcasting error. -> Option B

4. Quick Check:

   Shape mismatch = broadcasting error [OK]

Hint: Array shapes must match or broadcast [OK]

Common Mistakes:

• Thinking + can't add arrays
• Believing lists are needed
• Ignoring shape mismatch errors

5. You have two large numpy arrays representing daily sales for two stores over a year. You want to find the total sales per day quickly. Which approach best uses vectorized operations to achieve this?

hard

A. Use total_sales = store1_sales + store2_sales to add arrays directly.

B. Use a for loop to add each day's sales from both arrays and store in a new list.

C. Convert arrays to lists, then use list comprehension to add elements.

D. Use nested loops to add each element individually.

Solution

1. Step 1: Identify vectorized addition for large arrays

   Adding numpy arrays directly uses fast, optimized vectorized operations.

2. Step 2: Compare with loops and list methods

   Loops and list conversions are slower and less efficient for large data.

3. Final Answer:

   Use total_sales = store1_sales + store2_sales to add arrays directly. -> Option A

4. Quick Check:

   Vectorized addition is fastest for big arrays [OK]

Hint: Add arrays directly for fast big data sums [OK]

Common Mistakes:

• Using loops instead of vectorized addition
• Converting arrays to lists unnecessarily
• Using nested loops causing slow code