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.