Scalar operations on arrays in NumPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to multiply each element in the array by 3.

NumPy

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

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

A-

B+

C*

D/

Attempts:

3 left

💡 Hint

Common Mistakes

Using + instead of * will add 3 to each element, not multiply.

Using / will divide each element by 3, which is not what we want.

Using the * operator multiplies each element of the array by the scalar value.

2fill in blank

medium

Complete the code to subtract 5 from each element in the array.

NumPy

import numpy as np
arr = np.array([10, 15, 20, 25])
result = arr [1] 5
print(result)

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

A*

B/

C+

D-

Attempts:

3 left

💡 Hint

Common Mistakes

Using + will add 5 instead of subtracting.

Using * will multiply each element by 5, which is incorrect here.

The - operator subtracts the scalar from each element in the array.

3fill in blank

hard

Fix the error in the code to divide each element by 2 correctly.

NumPy

import numpy as np
arr = np.array([8, 16, 24, 32])
result = arr [1] 2
print(result)

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

A*

B/

C+

D-

Attempts:

3 left

💡 Hint

Common Mistakes

Using * will multiply instead of dividing.

Using + or - will not perform division.

The / operator divides each element of the array by the scalar.

4fill in blank

hard

Fill both blanks to create a dictionary with words as keys and their lengths squared if length is greater than 3.

NumPy

words = ['data', 'science', 'ai', 'ml']
lengths = {word: len(word)[1] 2 for word in words if len(word) [2] 3}
print(lengths)

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

A**

B%

C>

D<

Attempts:

3 left

💡 Hint

Common Mistakes

Using < instead of > will filter wrong words.

Using % instead of ** will cause wrong calculations.

The ** operator squares the length, and > checks if length is greater than 3.

5fill in blank

hard

Fill all three blanks to create a dictionary with uppercase words as keys and their lengths if length is less than 5.

NumPy

words = ['data', 'science', 'ai', 'ml']
result = {word[1]: [2] for word in words if len(word) [3] 5}
print(result)

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

A.upper()

Blen(word)

C<

D>

Attempts:

3 left

💡 Hint

Common Mistakes

Using > instead of < will filter wrong words.

Forgetting to convert words to uppercase.

.upper() converts words to uppercase keys, len(word) gets length, and < filters words shorter than 5.

Practice

1. What happens when you add a scalar value to a NumPy array?
arr = np.array([1, 2, 3])
result = arr + 5

easy

A. 5 is added only to the first element

B. The array becomes a scalar value 5

C. 5 is added to each element of the array

D. An error occurs because you cannot add a scalar to an array

Solution

1. Step 1: Understand scalar addition on arrays

   Adding a scalar to a NumPy array adds that scalar to every element individually.

2. Step 2: Apply addition to each element

   Each element in [1, 2, 3] gets 5 added, resulting in [6, 7, 8].

3. Final Answer:

   5 is added to each element of the array -> Option C

4. Quick Check:

   Scalar + array = element-wise addition [OK]

Hint: Scalar operations apply to every element individually [OK]

Common Mistakes:

• Thinking scalar adds only to first element
• Expecting an error when adding scalar
• Confusing scalar with array addition

2. Which of the following is the correct syntax to multiply every element of a NumPy array arr by 3?

easy

A. arr * 3

B. arr * [3]

C. arr * arr[3]

D. arr * (3,)

Solution

1. Step 1: Identify scalar multiplication syntax

   Multiplying an array by a scalar uses the * operator with a number, like arr * 3.

2. Step 2: Check other options

   arr * [3] tries to multiply by a list, arr * arr[3] accesses an element (may cause error if index out of range), arr * (3,) is a tuple, not scalar.

3. Final Answer:

   arr * 3 -> Option A

4. Quick Check:

   Scalar multiplication uses * scalar [OK]

Hint: Use * with a number, not list or tuple [OK]

Common Mistakes:

• Using list or tuple instead of scalar
• Accessing array element incorrectly
• Confusing scalar with array

3. What is the output of the following code?

import numpy as np
arr = np.array([2, 4, 6])
result = arr - 1
print(result)

medium

A. [3 5 7]

B. [1 3 5]

C. [2 4 6]

D. Error: unsupported operand

Solution

1. Step 1: Understand scalar subtraction on array

   Subtracting 1 from each element in [2, 4, 6] results in [1, 3, 5].

2. Step 2: Confirm output matches expected array

   The print statement outputs the array as [1 3 5] without commas.

3. Final Answer:

   [1 3 5] -> Option B

4. Quick Check:

   Array - scalar subtracts from each element [OK]

Hint: Subtract scalar from each element for result [OK]

Common Mistakes:

• Expecting commas in printed array
• Adding instead of subtracting
• Thinking operation causes error

4. The following code throws an error. What is the cause?

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

medium

A. Cannot add string scalar to numeric array

B. Array is not defined

C. Missing import statement

D. Syntax error in addition

Solution

1. Step 1: Identify data types involved in addition

   arr is numeric array, '5' is a string scalar. NumPy cannot add string to numbers.

2. Step 2: Understand error cause

   Adding string to numeric array causes TypeError because types mismatch.

3. Final Answer:

   Cannot add string scalar to numeric array -> Option A

4. Quick Check:

   Scalar type must match array type [OK]

Hint: Scalar must be number, not string, for numeric arrays [OK]

Common Mistakes:

• Thinking import is missing
• Confusing syntax error with type error
• Assuming array is undefined

5. You have a NumPy array arr = np.array([10, 20, 30, 40]). You want to reduce each element by 10% using scalar operations. Which code correctly does this?

hard

A. arr + 0.9

B. arr - 0.1

C. arr / 10

D. arr * 0.9

Solution

1. Step 1: Understand reducing by 10%

   Reducing by 10% means multiplying by 90% or 0.9.

2. Step 2: Apply scalar multiplication

   Multiplying arr by 0.9 scales each element down by 10%.

3. Step 3: Check other options

   Subtracting 0.1 reduces by fixed amount, dividing by 10 reduces by 90%, adding 0.9 increases values.

4. Final Answer:

   arr * 0.9 -> Option D

5. Quick Check:

   Multiply by 0.9 to reduce by 10% [OK]

Hint: Multiply by 0.9 to reduce by 10% [OK]

Common Mistakes:

• Subtracting 0.1 instead of 10%
• Dividing instead of multiplying
• Adding instead of reducing