Controlling copy behavior in NumPy - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

What is the output of this NumPy slicing and assignment?

Consider the following code snippet using NumPy arrays. What will be the output of print(a)?

NumPy

import numpy as np

a = np.array([1, 2, 3, 4, 5])
b = a[1:4]
b[0] = 100
print(a)

A [ 1 100 3 4 5]

B [ 1 2 3 4 5]

C [100 100 100 4 5]

D [ 1 100 100 4 5]

💡 Hint

Remember that slicing a NumPy array returns a view, not a copy.

Slicing a NumPy array returns a view, so modifying b changes the original array a. Only the element at index 1 changes to 100.

❓ Predict Output

intermediate

What does this code print when using np.copy()?

What will be printed after running this code?

NumPy

import numpy as np

a = np.array([10, 20, 30])
b = np.copy(a)
b[1] = 99
print(a)

A [99 20 30]

B [10 99 30]

C [10 20 99]

D [10 20 30]

💡 Hint

np.copy() creates a new array independent of the original.

Using np.copy() creates a new array b that does not share data with a. Changing b does not affect a.

❓ data_output

advanced

What is the shape of the array after this operation?

Given the following code, what is the shape of b?

NumPy

import numpy as np

a = np.arange(12).reshape(3,4)
b = a[:, 1:3].copy()
print(b.shape)

A (3, 3)

B (2, 3)

C (3, 2)

D (4, 2)

💡 Hint

Check the slicing dimensions before copying.

The slice a[:, 1:3] selects all rows and columns 1 and 2, resulting in shape (3, 2). The copy does not change shape.

🔧 Debug

advanced

Why does this code raise an error?

Examine the code below. Why does it raise an error?

NumPy

import numpy as np

a = np.array([1, 2, 3])
b = a[::2]
b[2] = 10
print(a)

A IndexError: assignment index out of range

B TypeError: 'numpy.ndarray' object is not callable

C No error; prints [1 10 3]

D ValueError: shape mismatch

💡 Hint

Check the length of the sliced array b.

The slice a[::2] returns elements at indices 0 and 2, so b has length 2. Trying to assign b[2] = 10 raises IndexError: assignment index out of range because index 2 is out of bounds for array of size 2.

🚀 Application

expert

How to create an independent copy of a 2D NumPy array and modify it without affecting the original?

You have a 2D NumPy array a. You want to create a new array b that you can change freely without changing a. Which code snippet achieves this?

A b = a[:, :]

B b = a.copy()

C b = np.array(a)

D b = a.view()

💡 Hint

Think about which method creates a deep copy.

a.copy() creates a new array with its own data. Changes to b do not affect a. Other options create views or shallow copies.

Practice

1. What does the copy() method do in NumPy arrays?

easy

A. Creates a full separate copy of the array data

B. Creates a new array object sharing the same data

C. Deletes the original array

D. Converts the array to a list

Solution

1. Step 1: Understand the purpose of copy()

   The copy() method creates a new array with its own separate data, independent of the original array.

2. Step 2: Compare with view()

   Unlike view(), which shares data, copy() protects the original data by making a full copy.

3. Final Answer:

   Creates a full separate copy of the array data -> Option A

4. Quick Check:

   copy() = full separate copy [OK]

Hint: copy() makes a full separate array copy [OK]

Common Mistakes:

• Confusing copy() with view() sharing data
• Thinking copy() modifies original array
• Assuming copy() returns a reference

2. Which of the following is the correct syntax to create a view of a NumPy array a?

easy

A. b = a.view()

B. b = view(a)

C. b = copy(a)

D. b = a.copy()

Solution

1. Step 1: Recall the method to create a view

   In NumPy, the method to create a view of an array is view(), called on the array object.

2. Step 2: Check syntax correctness

   Only b = a.view() correctly calls the method on a. Other options are either copy or invalid function calls.

3. Final Answer:

   b = a.view() -> Option A

4. Quick Check:

   Use view() method on array [OK]

Hint: Use a.view() to create a view, not copy() [OK]

Common Mistakes:

• Using copy() instead of view()
• Calling view() as a standalone function
• Confusing syntax with copy module

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

import numpy as np
x = np.array([1, 2, 3])
y = x.view()
y[0] = 10
print(x)

medium

A. Error

B. [1 2 3]

C. [0 2 3]

D. [10 2 3]

Solution

1. Step 1: Understand view() shares data

   The variable y is a view of x, so both share the same data buffer.

2. Step 2: Modify y and check effect on x

   Changing y[0] to 10 changes the shared data, so x[0] also becomes 10.

3. Final Answer:

   [10 2 3] -> Option D

4. Quick Check:

   view() shares data, so x changes [OK]

Hint: view() shares data; changes reflect in original [OK]

Common Mistakes:

• Assuming view() creates independent copy
• Expecting original array unchanged
• Confusing view() with copy()

4. Identify the error in the following code snippet:

import numpy as np
arr = np.array([4, 5, 6])
new_arr = arr.copy
new_arr[0] = 0
print(arr)

medium

A. Cannot assign to new_arr[0]

B. copy() does not create a new array

C. Missing parentheses after copy method

D. arr is not a NumPy array

Solution

1. Step 1: Check method call syntax

   The code uses arr.copy without parentheses, so new_arr is a method reference, not an array.

2. Step 2: Understand effect of missing parentheses

   Trying to assign new_arr[0] = 0 fails because new_arr is not an array but a method object.

3. Final Answer:

   Missing parentheses after copy method -> Option C

4. Quick Check:

   copy() needs parentheses to call [OK]

Hint: Always use parentheses to call copy() method [OK]

Common Mistakes:

• Forgetting parentheses after copy()
• Assuming copy is an attribute, not method
• Expecting copy() to modify original array

5. You have a large NumPy array data and want to modify a small part without affecting the original array to save memory. Which approach is best?

hard

A. Use data.view() and modify the view

B. Use data.copy() and modify the copy

C. Modify data directly

D. Convert data to list and modify

Solution

1. Step 1: Understand memory and modification needs

   You want to modify part of the array without changing the original, so you need a separate copy.

2. Step 2: Compare copy() and view() for this case

   view() shares data, so changes affect original. copy() creates independent data, protecting original.

3. Step 3: Consider memory usage

   Although copy() uses more memory, it is necessary here to avoid changing original data.

4. Final Answer:

   Use data.copy() and modify the copy -> Option B

5. Quick Check:

   copy() protects original when modifying [OK]

Hint: copy() protects original; view() shares data [OK]

Common Mistakes:

• Using view() and unintentionally changing original
• Modifying original array directly
• Converting to list wastes memory and speed