Why array processing matters 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

numbers = [1, 2, 3, 4, 5]
array = np.[1](numbers)
print(array)

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

Aarray

Blist

Carrayify

Dtoarray

Attempts:

3 left

💡 Hint

Common Mistakes

Using np.list() instead of np.array()

Trying to use np.arrayify() which does not exist

The np.array() function converts a Python list into a NumPy array, which allows fast numerical operations.

2fill in blank

medium

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

NumPy

import numpy as np

arr = np.array([10, 20, 30])
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 * which multiplies instead of adds

Using / which divides instead of adds

Adding 5 to a NumPy array adds 5 to each element because NumPy supports element-wise operations.

3fill in blank

hard

Fix the error in the code to compute the mean of the array elements.

NumPy

import numpy as np

arr = np.array([2, 4, 6, 8])
mean_value = arr.[1]()
print(mean_value)

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

Amean

Bavg

Cmedian

Daverage

Attempts:

3 left

💡 Hint

Common Mistakes

Using arr.average() which is not a NumPy method

Using arr.avg() which does not exist

The correct NumPy method to compute the average value is mean().

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 = ['cat', 'elephant', 'dog', 'giraffe']
lengths = {word: [1] for word in words if [2]
print(lengths)

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

Alen(word)

Bword > 3

Clen(word) > 3

Dword

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'word > 3' which compares string to number

Using 'word' instead of length in the dictionary values

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

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 4 letters.

NumPy

words = ['apple', 'bat', 'carrot', 'dog']
result = { [1]: [2] for w in words if [3] }
print(result)

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

Aw.upper()

Blen(w)

Clen(w) > 4

Dw.lower()

Attempts:

3 left

💡 Hint

Common Mistakes

Using w.lower() instead of w.upper() for keys

Not filtering words by length

Using w instead of len(w) for values

The keys are uppercase words using w.upper(), values are lengths with len(w), and filter words longer than 4 with len(w) > 4.

Practice

1. Why is array processing important in data science?

easy

A. It makes code longer and more complex.

B. It slows down calculations significantly.

C. It allows handling many numbers at once efficiently.

D. It only works with text data.

Solution

1. Step 1: Understand array processing purpose

   Array processing helps to work with many numbers together instead of one by one.

2. Step 2: Identify the benefit in data science

   This makes calculations faster and code simpler, which is very useful in data science.

3. Final Answer:

   It allows handling many numbers at once efficiently. -> Option C

4. Quick Check:

   Array processing = efficient handling [OK]

Hint: Think: many numbers, one operation, faster code [OK]

Common Mistakes:

• Thinking array processing slows code
• Believing it only works with text
• Assuming it makes code complex

2. Which of the following is the correct way to create a NumPy array from a list [1, 2, 3]?

easy

A. np.array([1, 2, 3])

B. np.list([1, 2, 3])

C. array([1, 2, 3])

D. np.create_array(1, 2, 3)

Solution

1. Step 1: Recall NumPy array creation syntax

   NumPy arrays are created using np.array() with a list inside parentheses.

2. Step 2: Check each option

   Only np.array([1, 2, 3]) matches the correct syntax; others are invalid or undefined.

3. Final Answer:

   np.array([1, 2, 3]) -> Option A

4. Quick Check:

   Use np.array() to create arrays [OK]

Hint: Remember: np.array() creates arrays from lists [OK]

Common Mistakes:

• Using np.list() which does not exist
• Omitting np prefix
• Using wrong function names

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: unsupported operation

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] multiplied by 2 gives [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 addition instead of multiplication
• Assuming error due to operator

4. Find the error in this code snippet:

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

medium

A. Missing import statement for NumPy.

B. Cannot add string to NumPy array of numbers.

C. Array should be created with np.list().

D. The print statement syntax is wrong.

Solution

1. Step 1: Identify data types involved in addition

   The array contains numbers, but the code tries to add a string '2'.

2. Step 2: Understand type compatibility in NumPy

   NumPy does not support adding strings to numeric arrays directly, causing a TypeError.

3. Final Answer:

   Cannot add string to NumPy array of numbers. -> Option B

4. Quick Check:

   Numeric array + string = error [OK]

Hint: Check data types before adding arrays [OK]

Common Mistakes:

• Ignoring type mismatch
• Thinking np.list() is valid
• Blaming print syntax

5. You have two NumPy arrays:

a = np.array([1, 2, 3])
b = np.array([4, 5, 6])

You want to create a new array that contains the sum of corresponding elements from a and b. Which code correctly does this efficiently?

hard

A. c = a + b

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

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

D. Both A and D

Solution

1. Step 1: Understand element-wise addition in NumPy

   NumPy supports element-wise addition using both the + operator and the np.add() function.

2. Step 2: Compare options for efficiency

   Both c = a + b and c = np.add(a, b) perform fast, vectorized addition without explicit loops.

3. Step 3: Identify less efficient option

   c = [a[i] + b[i] for i in range(len(a))] uses a Python loop, which is slower and less efficient than vectorized operations.

4. Final Answer:

   Both A and D -> Option D

5. Quick Check:

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

Hint: Use vectorized ops: + or np.add() for arrays [OK]

Common Mistakes:

• Using slow Python loops instead of vectorized ops
• Thinking only one method works
• Ignoring NumPy's built-in functions