Vectorization over loops in NumPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

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

NumPy

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

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

Aarray

Brange

Cmatrix

Dlist

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'list' instead of 'array' which is not a NumPy function.

Using 'matrix' which is a different NumPy type.

Using 'range' which creates a range object, not an array.

Use np.array() to convert a list into a NumPy array.

2fill in blank

medium

Complete the code to add 5 to every element in the array using vectorization.

NumPy

import numpy as np
arr = np.array([1, 2, 3, 4, 5])
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 '*' which multiplies instead of adds.

Using '-' which subtracts instead of adds.

Using '/' which divides instead of adds.

Adding 5 to every element is done with the + operator in vectorized form.

3fill in blank

hard

Fix the error in the code to multiply each element by 2 using vectorization.

NumPy

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

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

Adot

Bmul

Cmultiply

D*

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'multiply' or 'mul' as methods which do not exist on arrays.

Using 'dot' which performs matrix multiplication, not element-wise.

Use the * operator for element-wise multiplication in NumPy arrays.

4fill in blank

hard

Fill both blanks to create a dictionary with words as keys and their lengths as values, but only for words longer than 3 letters.

NumPy

words = ['apple', 'bat', 'carrot', 'dog']
lengths = {word: [1] for word in words if len(word) [2] 3}

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

Alen(word)

Bword

C>

D<=

Attempts:

3 left

💡 Hint

Common Mistakes

Using the word itself as the value instead of its length.

Using '<=' which selects words with length 3 or less.

Use len(word) to get the length and filter words with length greater than 3 using >.

5fill in blank

hard

Fill all three blanks to create a dictionary with uppercase words as keys and their lengths as values, only for words longer than 3 letters.

NumPy

words = ['apple', 'bat', 'carrot', 'dog']
lengths = { [1]: [2] for word in words if len(word) [3] 3 }

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

Aword.upper()

Blen(word)

C>

Dword.lower()

Attempts:

3 left

💡 Hint

Common Mistakes

Using lowercase words as keys instead of uppercase.

Using '<=' which includes short words.

Using the word itself as value instead of length.

Use word.upper() for keys, len(word) for values, and filter words with length greater than 3 using >.

Practice

1. What is the main advantage of using vectorization in numpy instead of loops?

easy

A. It makes the code run faster by doing many operations at once.

B. It uses more memory but slows down the code.

C. It makes the code harder to read and understand.

D. It requires writing more lines of code.

Solution

1. Step 1: Understand vectorization concept

   Vectorization means applying operations on whole arrays at once, not one element at a time.

2. Step 2: Compare speed and simplicity

   This approach uses optimized numpy functions that run faster than explicit loops.

3. Final Answer:

   It makes the code run faster by doing many operations at once. -> Option A

4. Quick Check:

   Vectorization = faster code [OK]

Hint: Vectorization speeds up code by handling arrays at once [OK]

Common Mistakes:

• Thinking vectorization slows code
• Believing vectorization needs more code
• Confusing vectorization with loops

2. Which of the following numpy codes correctly replaces a loop to add 5 to each element in array a?

easy

A. a + 5

B. for i in a: i = i + 5

C. np.add(a, 5, out=a)

D. a.append(5)

Solution

1. Step 1: Identify vectorized addition

   Using np.add(a, 5, out=a) adds 5 to every element in array a efficiently.

2. Step 2: Check other options

   a + 5 works but returns a new array, for i in a: i = i + 5 does not change a, and a.append(5) is invalid for numpy arrays.

3. Final Answer:

   np.add(a, 5, out=a) -> Option C

4. Quick Check:

   np.add = vectorized add [OK]

Hint: Use numpy functions like np.add for vectorized operations [OK]

Common Mistakes:

• Using loops that don't modify array
• Trying list methods on numpy arrays
• Confusing array addition with append

3. What is the output of this code?

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

medium

A. [2 4 6]

B. [1 2 3 1 2 3]

C. [3 4 5]

D. Error: cannot multiply array by int

Solution

1. Step 1: Understand array multiplication

   Multiplying a numpy array by a number multiplies each element by that number.

2. Step 2: Calculate element-wise multiplication

   Each element in [1, 2, 3] is multiplied by 2, resulting in [2, 4, 6].

3. Final Answer:

   [2 4 6] -> Option A

4. Quick Check:

   Array * number = element-wise multiply [OK]

Hint: Array * number multiplies each element [OK]

Common Mistakes:

• Thinking it concatenates arrays
• Expecting an error on multiplication
• Confusing element-wise with matrix multiply

4. Identify the error in this code that tries to add 10 to each element of arr using a loop:

import numpy as np
arr = np.array([1, 2, 3])
for i in arr:
    i = i + 10
print(arr)

medium

A. Syntax error in for loop.

B. Loop variable i does not modify original array elements.

C. Array arr is not defined.

D. Cannot add integer to numpy array elements.

Solution

1. Step 1: Understand loop variable behavior

   Variable i is a copy of each element, so changing i does not change arr.

2. Step 2: Explain why array remains unchanged

   The original array arr stays the same because the loop does not assign back to array elements.

3. Final Answer:

   Loop variable i does not modify original array elements. -> Option B

4. Quick Check:

   Loop var copy ≠ array change [OK]

Hint: Loop vars are copies; assign to array to change [OK]

Common Mistakes:

• Assuming loop changes array automatically
• Thinking syntax error exists
• Believing numpy disallows int addition

5. You have a numpy array data = np.array([1, 2, 3, 4, 5]). You want to create a new array where each element is squared if it is even, otherwise doubled. Which vectorized code achieves this?

hard

A. data * 2 if data % 2 else data ** 2

B. [x**2 if x%2==0 else x*2 for x in data]

C. for i in range(len(data)): data[i] = data[i]**2 if data[i]%2==0 else data[i]*2

D. np.where(data % 2 == 0, data**2, data*2)

Solution

1. Step 1: Understand vectorized conditional with np.where

   np.where(condition, x, y) returns array choosing x where condition is true, else y.

2. Step 2: Apply condition for even and odd elements

   Condition data % 2 == 0 checks even numbers; squares them, else doubles.

3. Step 3: Check other options

   [x**2 if x%2==0 else x*2 for x in data] uses list comprehension (not vectorized), C uses loop (not vectorized), D is invalid syntax.

4. Final Answer:

   np.where(data % 2 == 0, data**2, data*2) -> Option D

5. Quick Check:

   np.where = vectorized if-else [OK]

Hint: Use np.where for vectorized if-else conditions [OK]

Common Mistakes:

• Using loops or list comprehensions instead of vectorization
• Writing invalid syntax for conditional operations
• Confusing element-wise operations with scalar