flatten() and ravel() for 1D conversion in NumPy - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of flatten() vs ravel() on a 2D array

What is the output of the following code snippet?

NumPy

import numpy as np
arr = np.array([[1, 2], [3, 4]])
flat = arr.flatten()
rav = arr.ravel()
flat[0] = 100
rav[1] = 200
print(arr)
print(flat)
print(rav)

A

[[1 2]
 [3 4]]
[100   2   3   4]
[1 200 3 4]

B

[[100 2]
 [3 4]]
[100   2   3   4]
[1 200 3 4]

C

[[1 2]
 [3 4]]
[1 2 3 4]
[1 2 3 4]

D

[[1 200]
 [3 4]]
[100   2   3   4]
[1 200 3 4]

💡 Hint

Remember that flatten() returns a copy, while ravel() returns a view when possible.

Modifying the flattened array does not affect the original because flatten() returns a copy. Modifying the raveled array affects the original because ravel() returns a view when possible.

❓ data_output

intermediate

Shape of arrays after flatten() and ravel()

Given the code below, what are the shapes of flat and rav?

NumPy

import numpy as np
arr = np.arange(12).reshape(3,4)
flat = arr.flatten()
rav = arr.ravel()
print(flat.shape)
print(rav.shape)

A

(3,4)
(12,)

B

(12,)
(12,)

C

(12,)
(3,4)

D

(3,4)
(3,4)

💡 Hint

Both flatten() and ravel() convert the array to 1D.

Both flatten() and ravel() return 1D arrays, so their shape is (12,).

🔧 Debug

advanced

Why does modifying ravel() output change the original array?

Consider this code: import numpy as np arr = np.array([[5, 6], [7, 8]]) r = arr.ravel() r[0] = 100 print(arr) Why does arr change after modifying r?

NumPy

import numpy as np
arr = np.array([[5, 6], [7, 8]])
r = arr.ravel()
r[0] = 100
print(arr)

A ravel() returns a copy, so changes to r do not affect arr.

B arr is immutable, so changes to r raise an error.

C ravel() returns a view of the original array, so changes to r affect arr.

D ravel() flattens the array but does not allow modification.

💡 Hint

Think about whether ravel() returns a copy or a view.

ravel() returns a view when possible, so modifying r changes the original array arr.

🧠 Conceptual

advanced

Memory usage difference between flatten() and ravel()

Which statement about memory usage is true when comparing flatten() and ravel()?

A flatten() always creates a copy, using more memory; ravel() returns a view when possible, using less memory.

B flatten() returns a view, so it uses less memory; ravel() always copies the data.

C Both flatten() and ravel() always create copies, so memory usage is the same.

D Both flatten() and ravel() always return views, so memory usage is minimal.

💡 Hint

Check which method returns a copy and which returns a view.

flatten() always returns a copy, so it uses more memory. ravel() returns a view when possible, so it uses less memory.

🚀 Application

expert

Choosing flatten() or ravel() for safe modification

You have a large 2D numpy array and want to create a 1D version to modify without changing the original array. Which method should you use and why?

A Use flatten() because it returns a copy, so the original array stays unchanged.

B Use ravel() because it returns a view, so changes reflect in the original array.

C Use flatten() because it returns a view, so changes reflect in the original array.

D Use ravel() because it returns a copy, so the original array stays unchanged.

💡 Hint

Think about whether you want changes to affect the original array or not.

flatten() returns a copy, so modifying it does not affect the original array. ravel() returns a view, so modifying it changes the original array.