View vs copy behavior in NumPy - Interactive Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a view of the original NumPy array.

NumPy

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

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

A.view()

B.copy()

C.reshape(2, 2)

D.flatten()

Attempts:

3 left

💡 Hint

Common Mistakes

Using .copy() instead of .view() creates a separate array, not a view.

Using .reshape() changes shape but does not guarantee a view.

Using .view() creates a view of the original array, sharing the same data.

2fill in blank

medium

Complete the code to modify the view and see the change in the original array.

NumPy

import numpy as np
arr = np.array([10, 20, 30, 40])
view_arr = arr.view()
view_arr[1] = 99
result = arr

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

A[1]

B[0]

C[2]

D[3]

Attempts:

3 left

💡 Hint

Common Mistakes

Modifying an index other than 0 and expecting no change in original array.

Using .copy() instead of .view() so changes don't reflect.

Changing view_arr[0] modifies the original array because the view shares data.

3fill in blank

hard

Fix the error in the code to ensure modifying the copy does NOT change the original array.

NumPy

import numpy as np
arr = np.array([5, 6, 7, 8])
copy_arr = arr[1]
copy_arr[0] = 100
result = arr

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

A.copy()

B.view()

C.reshape(2, 2)

D[:]

Attempts:

3 left

💡 Hint

Common Mistakes

Using .view() instead of .copy() causes changes to reflect in original array.

Using slicing [:] creates a view, not a copy.

Using .copy() creates a new array independent of the original, so changes do not affect it.

4fill in blank

hard

Fill both blanks to create a dictionary with word lengths only for words longer than 3 letters.

NumPy

words = ['cat', 'house', 'dog', 'elephant']
lengths = {word: [1] for word in words if [2]

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

Alen(word)

Blen(word) > 3

Cword.startswith('e')

Dword == 'dog'

Attempts:

3 left

💡 Hint

Common Mistakes

Using the word itself as value instead of its length.

Filtering words incorrectly with wrong condition.

The dictionary comprehension uses len(word) for values and filters words with length greater than 3.

5fill in blank

hard

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

NumPy

words = ['apple', 'bat', 'carrot', 'dog']
result = [1]: [2] for w in words if [3]

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

Aw.upper()

Bw

Clen(w) > 3

Dw.lower()

Attempts:

3 left

💡 Hint

Common Mistakes

Using lowercase keys instead of uppercase.

Not filtering words by length correctly.

The dictionary comprehension uses uppercase words as keys, original words as values, and filters words longer than 3 letters.

Practice

1. What happens when you create a view of a NumPy array and modify the view?

easy

A. The original array also changes because the view shares data.

B. The original array remains unchanged because the view is a copy.

C. An error occurs because views cannot be modified.

D. The view becomes independent and changes do not affect anything.

Solution

1. Step 1: Understand what a view is in NumPy

   A view shares the same data buffer with the original array, so changes affect both.

2. Step 2: Effect of modifying the view

   When you modify the view, the original array's data changes because they share memory.

3. Final Answer:

   The original array also changes because the view shares data. -> Option A

4. Quick Check:

   View shares data = original changes [OK]

Hint: Views share data; modifying view changes original [OK]

Common Mistakes:

• Thinking views are independent copies
• Assuming modifying a view raises an error
• Confusing views with deep copies

2. Which of the following is the correct way to create a copy of a NumPy array named arr?

easy

A. copy_arr = arr.view()

B. copy_arr = arr

C. copy_arr = arr.copy()

D. copy_arr = arr[:]

Solution

1. Step 1: Identify methods to create copies

   The copy() method creates a new independent array with its own data.

2. Step 2: Differentiate from view and slicing

   view() creates a view, arr[:] creates a view (slice), and copy_arr = arr just assigns a reference.

3. Final Answer:

   copy_arr = arr.copy() -> Option C

4. Quick Check:

   copy() makes independent copy [OK]

Hint: Use copy() for independent array copy [OK]

Common Mistakes:

• Using view() instead of copy()
• Assuming slicing creates a copy
• Assigning array without copy creates reference

3. What is the output of the following code?

import numpy as np
arr = np.array([1, 2, 3, 4])
view_arr = arr[1:3]
view_arr[0] = 10
print(arr)

medium

A. [1 10 3 4]

B. [1 2 3 4]

C. [1 10 10 4]

D. [10 2 3 4]

Solution

1. Step 1: Understand slicing creates a view

   The slice arr[1:3] creates a view sharing data with arr.

2. Step 2: Modify the view and observe original array

   Changing view_arr[0] changes arr[1] to 10, so arr becomes [1, 10, 3, 4].

3. Final Answer:

   [1 10 3 4] -> Option A

4. Quick Check:

   Slicing = view, change affects original [OK]

Hint: Slicing creates view; changes reflect in original [OK]

Common Mistakes:

• Thinking slicing creates a copy
• Expecting original array unchanged
• Modifying wrong index in view

4. The following code is intended to create an independent copy of arr and modify it without changing arr. What is the error?

import numpy as np
arr = np.array([5, 6, 7])
copy_arr = arr[::]
copy_arr[0] = 100
print(arr)

medium

A. You cannot modify arrays created by slicing.

B. The syntax for slicing is incorrect and causes an error.

C. The print statement is missing parentheses.

D. Slicing arr[::] creates a view, not a copy, so original changes.

Solution

1. Step 1: Analyze slicing behavior

   Slicing with arr[::] creates a view, not a copy, so both share data.

2. Step 2: Effect of modifying the slice

   Changing copy_arr[0] changes arr[0] as well, so original array is modified.

3. Final Answer:

   Slicing arr[::] creates a view, not a copy, so original changes. -> Option D

4. Quick Check:

   Slicing = view, not copy [OK]

Hint: Slicing creates view, not copy; use copy() for independence [OK]

Common Mistakes:

• Assuming slicing makes a copy
• Thinking slicing syntax is wrong
• Believing modifying slice raises error

5. You have a large NumPy array data. You want to create a smaller array subset that you can modify freely without affecting data. Which approach is best and why?

import numpy as np
data = np.arange(1000)
subset = data[100:200]
subset[0] = -1
print(data[100])

hard

A. Use subset = data[100:200] because slicing creates a copy.

B. Use subset = data[100:200].copy() to create an independent copy.

C. Use subset = data.view()[100:200] to avoid copying data.

D. Assign subset = data and modify it directly.

Solution

1. Step 1: Understand slicing creates a view

   Slicing data[100:200] creates a view sharing data with data.

2. Step 2: Use copy() to avoid modifying original

   Calling copy() creates an independent array, so changes to subset don't affect data.

3. Step 3: Check the effect of modification

   Without copy(), modifying subset[0] changes data[100]. With copy(), data[100] stays unchanged.

4. Final Answer:

   Use subset = data[100:200].copy() to create an independent copy. -> Option B

5. Quick Check:

   copy() makes independent array for safe modification [OK]

Hint: Use copy() on slice to avoid changing original array [OK]

Common Mistakes:

• Assuming slicing creates independent copy
• Using view() which shares data
• Modifying original array unintentionally